From 6c16fd289b78ca61490527a517c1a0b305be5277 Mon Sep 17 00:00:00 2001 From: minixalpha Date: Mon, 5 Oct 2026 01:22:43 -0700 Subject: [PATCH] chore(release): prepare v0.9.0 --- .../skills/release/scripts/verify-release.sh | 10 +- docs/changelogs/0.8.x.md | 75 - docs/changelogs/0.9.x.md | 81 + docs/changelogs/README.md | 3 + docs/dev_notes/README.md | 7 +- docs/dev_notes/en/0.8.x.md | 1071 ------------ docs/dev_notes/en/0.9.x.md | 1076 ++++++++++++ docs/dev_notes/zh-CN/0.8.x.md | 1446 ---------------- docs/dev_notes/zh-CN/0.9.x.md | 1451 +++++++++++++++++ 9 files changed, 2622 insertions(+), 2598 deletions(-) create mode 100644 docs/changelogs/0.9.x.md create mode 100644 docs/dev_notes/en/0.9.x.md create mode 100644 docs/dev_notes/zh-CN/0.9.x.md diff --git a/.agents/skills/release/scripts/verify-release.sh b/.agents/skills/release/scripts/verify-release.sh index a7a35b8..8ab289e 100755 --- a/.agents/skills/release/scripts/verify-release.sh +++ b/.agents/skills/release/scripts/verify-release.sh @@ -8,8 +8,7 @@ # # Usage: verify-release.sh e.g. verify-release.sh 0.1.1 # -# Smoke test runs the entry point with no args (currently side-effect-free). -# If the entry point ever gains required args/interaction, adjust step 3. +# Smoke test uses --version without a task and checks the installed version. set -euo pipefail PACKAGE="nanoPyCodeAgent" @@ -63,11 +62,12 @@ printf 'OK: GitHub Release %s has wheel + sdist\n' "$tag" # --default-index (not --index): --index only *adds* an index, and uv's # first-index strategy would stop at a locally configured mirror that has the # package but not the new version yet. --refresh: uv's cached simple-index -# metadata can be stale right after a publish. /dev/null 2>&1 /dev/null = deadline )) && fail "uvx smoke test failed for ${PACKAGE}@${version} after ${POLL_TIMEOUT}s" printf 'uvx not ready (index propagation?); retrying in %ss...\n' "$POLL_INTERVAL"; sleep "$POLL_INTERVAL" done diff --git a/docs/changelogs/0.8.x.md b/docs/changelogs/0.8.x.md index 83c73b4..0fb5457 100644 --- a/docs/changelogs/0.8.x.md +++ b/docs/changelogs/0.8.x.md @@ -4,81 +4,6 @@ All notable changes in the **0.8.x** release series are documented here. ## [Unreleased] -### Added -- Standardize Harbor runs through a recorded working-tree wheel, a shared - SHA256-verified APT cache, and task-revision-specific dependency preflight. - Add a QEMU Alpine SSH profile and an install-only check before model spend; - preserve setup failures separately from scored results and test the workflow - contracts in CI. -- Add an opt-in Harbor `RetryingVerifier` that retries a timed-out Linux test - script up to three total attempts at the task's native timeout, reusing the - existing agent output. Preserve attempt logs, clear stale rewards, and stop - timed-out test sessions before retrying. Completed scores, including zero, - and non-timeout errors are not retried. -- Estimate model-call costs from token usage when a provider reports tokens - but no per-request cost, starting with the DeepSeek official - Anthropic-compatible endpoint. The estimate uses a static DeepSeek price - list, is marked `kind: estimated` so it stays distinct from a - provider-reported or reconciled cost, and is only used after a real - provider-reported cost is absent. DeepSeek exposes no cost or generation - endpoint, so the existing OpenRouter reconciliation cannot resolve it. -- Configure per-reply generation budgets through `--max-tokens` or - `ANTHROPIC_MAX_TOKENS`, including the user settings file and Harbor adapter. - Record the effective limit in startup output, Event Journals, and ATIF - trajectories while retaining compatibility with older journals. -- Add a wall-clock time budget to headless runs through - `--time-budget-seconds` and the Harbor adapter `time_budget_seconds` kwarg. - Tell the model how much time and how many replies remain, and start - finalization guidance when either reserve is reached (10 replies or the - larger of 180 seconds and 15% of the time budget). On POSIX main threads, - interrupt in-flight model/tool work at the deadline, constrain retry waits, - and limit subsequent cost reconciliation to 30 seconds before saving the - trajectory. Budget exhaustion has a distinct `time_budget_exhausted` outcome. - Append the reminder to the tail of the - conversation so the system prompt stays fixed and provider prefix caching - keeps working, and record each injected reminder as an `input.injected` - event projected into ATIF. Record the budget in startup output, Event - Journals, and ATIF. New Event Journals use schema v4, with v1-v3 replay still - supported. - -### Fixed -- Preserve estimated-cost classification and subtotals in ATIF and Harbor - summaries, while allowing generation billing lookups to replace estimates. -- Reject unresolved QEMU task revisions selected through local paths or - datasets, accept install-only mode from JSON configuration, and preserve - archived verifier attempts when Harbor filters downloaded logs. -- Retry the Harbor adapter's system-dependency installation a bounded number - of times when the package index returns a transient HTTP 404, instead of - failing agent setup immediately. Non-404 failures and the final attempt still - propagate. -- Terminate a timed-out or interrupted bash command's process group on POSIX - and other members of its session on Linux, including GNU `timeout` children - in separate groups. Close output pipes without draining detached children - and bound direct-child reaping. Preserve background services when their - launching command finishes normally. -- Return correctable tool errors for missing or incorrectly typed arguments and - unknown tool names, allowing the model to retry within the existing turn - budget. Keep previews safe, reject incomplete argument JSON yielded by the - SDK, and preserve rejected inputs and error results in Journals and ATIF. -- Stop explicitly when a model response reaches `max_tokens`, reporting - `response_truncated` in Event Journals and ATIF trajectories instead of task - completion. Preserve partial output, usage, and cost accounting; skip tools - from the truncated reply and keep subsequent interactive requests valid. - New journals use schema v2, with v1 replay still supported. -- Recover interrupted model response streams. Retry response-body read errors, - read timeouts, and remote protocol errors up to twice (1s, 2s) before - committing a reply to history or running any of its tools, handling both the - `httpx` and `httpx2` transport families. Record each attempt as a Journal v3 - `model.failed` event, project it as an incomplete ATIF step, and mark token - and cost totals as partial when usage is missing. - -### Changed -- Increase the default per-reply generation limit from 8192 to 32768 tokens. -- Moved detailed CLI and configuration guidance out of the bilingual READMEs - into dedicated English and Chinese user references, keeping the READMEs - concise while documenting task input, options, exit statuses, trajectories, - Event Journals, supported settings, defaults, precedence, and empty values. - ## [0.8.0] - 2026-09-04 ### Added diff --git a/docs/changelogs/0.9.x.md b/docs/changelogs/0.9.x.md new file mode 100644 index 0000000..c8084cd --- /dev/null +++ b/docs/changelogs/0.9.x.md @@ -0,0 +1,81 @@ +# Changelog — 0.9.x + +All notable changes in the **0.9.x** release series are documented here. + +## [Unreleased] + +## [0.9.0] - 2026-10-05 + +### Added +- Standardize Harbor runs through a recorded working-tree wheel, a shared + SHA256-verified APT cache, and task-revision-specific dependency preflight. + Add a QEMU Alpine SSH profile and an install-only check before model spend; + preserve setup failures separately from scored results and test the workflow + contracts in CI. +- Add an opt-in Harbor `RetryingVerifier` that retries a timed-out Linux test + script up to three total attempts at the task's native timeout, reusing the + existing agent output. Preserve attempt logs, clear stale rewards, and stop + timed-out test sessions before retrying. Completed scores, including zero, + and non-timeout errors are not retried. +- Estimate model-call costs from token usage when a provider reports tokens + but no per-request cost, starting with the DeepSeek official + Anthropic-compatible endpoint. The estimate uses a static DeepSeek price + list, is marked `kind: estimated` so it stays distinct from a + provider-reported or reconciled cost, and is only used after a real + provider-reported cost is absent. DeepSeek exposes no cost or generation + endpoint, so the existing OpenRouter reconciliation cannot resolve it. +- Configure per-reply generation budgets through `--max-tokens` or + `ANTHROPIC_MAX_TOKENS`, including the user settings file and Harbor adapter. + Record the effective limit in startup output, Event Journals, and ATIF + trajectories while retaining compatibility with older journals. +- Add a wall-clock time budget to headless runs through + `--time-budget-seconds` and the Harbor adapter `time_budget_seconds` kwarg. + Tell the model how much time and how many replies remain, and start + finalization guidance when either reserve is reached (10 replies or the + larger of 180 seconds and 15% of the time budget). On POSIX main threads, + interrupt in-flight model/tool work at the deadline, constrain retry waits, + and limit subsequent cost reconciliation to 30 seconds before saving the + trajectory. Budget exhaustion has a distinct `time_budget_exhausted` outcome. + Append the reminder to the tail of the + conversation so the system prompt stays fixed and provider prefix caching + keeps working, and record each injected reminder as an `input.injected` + event projected into ATIF. Record the budget in startup output, Event + Journals, and ATIF. New Event Journals use schema v4, with v1-v3 replay still + supported. + +### Fixed +- Preserve estimated-cost classification and subtotals in ATIF and Harbor + summaries, while allowing generation billing lookups to replace estimates. +- Reject unresolved QEMU task revisions selected through local paths or + datasets, accept install-only mode from JSON configuration, and preserve + archived verifier attempts when Harbor filters downloaded logs. +- Retry the Harbor adapter's system-dependency installation a bounded number + of times when the package index returns a transient HTTP 404, instead of + failing agent setup immediately. Non-404 failures and the final attempt still + propagate. +- Terminate a timed-out or interrupted bash command's process group on POSIX + and other members of its session on Linux, including GNU `timeout` children + in separate groups. Close output pipes without draining detached children + and bound direct-child reaping. Preserve background services when their + launching command finishes normally. +- Return correctable tool errors for missing or incorrectly typed arguments and + unknown tool names, allowing the model to retry within the existing turn + budget. Keep previews safe, reject incomplete argument JSON yielded by the + SDK, and preserve rejected inputs and error results in Journals and ATIF. +- Stop explicitly when a model response reaches `max_tokens`, reporting + `response_truncated` in Event Journals and ATIF trajectories instead of task + completion. Preserve partial output, usage, and cost accounting; skip tools + from the truncated reply and keep subsequent interactive requests valid. +- Recover interrupted model response streams. Retry response-body read errors, + read timeouts, and remote protocol errors up to twice (1s, 2s) before + committing a reply to history or running any of its tools, handling both the + `httpx` and `httpx2` transport families. Record each attempt as a Journal + `model.failed` event, project it as an incomplete ATIF step, and mark token + and cost totals as partial when usage is missing. + +### Changed +- Increase the default per-reply generation limit from 8192 to 32768 tokens. +- Moved detailed CLI and configuration guidance out of the bilingual READMEs + into dedicated English and Chinese user references, keeping the READMEs + concise while documenting task input, options, exit statuses, trajectories, + Event Journals, supported settings, defaults, precedence, and empty values. diff --git a/docs/changelogs/README.md b/docs/changelogs/README.md index e755a43..81dd38d 100644 --- a/docs/changelogs/README.md +++ b/docs/changelogs/README.md @@ -5,3 +5,6 @@ Changelogs for nanoPyCodeAgent, organized one file per `X.Y.x` release series The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/). + +Current release series: [0.9.x](0.9.x.md). Previous release series: +[0.8.x](0.8.x.md). diff --git a/docs/dev_notes/README.md b/docs/dev_notes/README.md index d018e9e..d0e3d54 100644 --- a/docs/dev_notes/README.md +++ b/docs/dev_notes/README.md @@ -5,7 +5,7 @@ series (e.g. `0.1.x.md`). Notes are bilingual, split by language: -- [`zh-CN/`](zh-CN/) — **hand-written Chinese source** (source of truth) +- [`zh-CN/`](zh-CN/) — **Chinese source** (source of truth) - [`en/`](en/) — **English, generated from the Chinese source** (do not edit by hand) These are free-form notes — insights, takeaways, and design decisions gathered @@ -16,3 +16,8 @@ The date in a `## X.Y.Z - YYYY.MM.DD` section heading is the **release date** of that version (the same date as the git tag and the changelog entry). While the version is unreleased, keep the literal `YYYY.MM.DD` placeholder — it is filled in by the release process. + +Current release series: **0.9.x** ([Chinese source](zh-CN/0.9.x.md), +[English version](en/0.9.x.md)). The **0.8.x** notes retain the development +record for v0.8.0 ([Chinese source](zh-CN/0.8.x.md), +[English version](en/0.8.x.md)). diff --git a/docs/dev_notes/en/0.8.x.md b/docs/dev_notes/en/0.8.x.md index e57e83b..e095823 100644 --- a/docs/dev_notes/en/0.8.x.md +++ b/docs/dev_notes/en/0.8.x.md @@ -291,1074 +291,3 @@ uv run pytest - Trajectory: 11 steps, including 10 model steps. - Tokens: 49,878 input, 42,240 cache, 5,845 output. - Cost: 0.00222441 USD; all 10 reconciliations succeeded on the first attempt. - -### Running Terminal-Bench in batches and establishing a baseline - -Single-task acceptance established the task-container, agent, trajectory, cost-reconciliation, and verifier workflow. Next, expand to 20 tasks to observe failures and actual consumption before setting a budget for all 89 tasks. This pilot's pass rate is not the full Terminal-Bench 2.1 score. - -#### Fixed experimental configuration - -Prepared on 2026-09-06, this run fixes the following conditions. Later harness comparisons should reuse tasks and limits while recording each new agent revision. - -| Item | Configuration | -| --- | --- | -| Harbor | 0.21.0, using `benchmarks/harbor/uv.lock` | -| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | -| Dataset | `terminal-bench/terminal-bench-2-1`, 89 tasks | -| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | -| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | -| API endpoint | `https://openrouter.ai/api`, using Anthropic Messages | -| Provider routing | No request-level provider override; account routing settings are also experimental conditions | -| Pilot size | First 20 dataset entries, one attempt each | -| Environment | Local Docker, concurrency 2 | -| Agent limit | At most 50 model replies per task | -| Per-response limit | `MAX_TOKENS = 8192`; no explicit reasoning effort or temperature | -| Harbor retries | 0; Anthropic SDK internal request retries remain enabled | -| Timeouts and resources | Preserve task defaults | - -Harbor 0.21.0's `--n-tasks 20` selects the first 20 entries after filtering, rather than sampling randomly. Pinning the dataset hash fixes task versions, but the actual selection should still be saved and later selected with repeated `--include-task-name` arguments. OpenRouter provider routing and server behavior can change, so a pinned model slug does not make the experiment fully deterministic. - -Host Harbor uses a lockfile. Inside containers, `uv tool install` pins the agent revision but leaves dependency resolution unlocked. Public results retain Docker version and task-image RepoDigests for checking later environments; host hardware details stay in local records. - -#### Running the experiment - -Confirm that `docker info` succeeds, then run from the repository root. Supply credentials through environment variables, without placing them in arguments or experiment records. Because `ANTHROPIC_MODEL` overrides the adapter's model derived from `--model`, explicitly remove it: - -```bash -export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" -export ANTHROPIC_BASE_URL="https://openrouter.ai/api" -unset ANTHROPIC_MODEL - -BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" -BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" - -uv run --locked --project benchmarks/harbor harbor run \ - --dataset "$BENCH_DATASET" \ - --n-tasks 20 \ - --agent harbor_adapter:NanoPyCodeAgent \ - --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ - --agent-kwarg max_turns=50 \ - --model openrouter/deepseek/deepseek-v4-flash-0731 \ - --env docker \ - --n-concurrent 2 \ - --n-attempts 1 \ - --max-retries 0 \ - --job-name tb21-flash0731-pilot20-20260906 -``` - -If credentials are stored in `~/.nanoPyCodeAgent/settings.json`, call `nanopycodeagent.settings.load_settings_env()` in the Python process launching Harbor, remove `ANTHROPIC_MODEL`, and start the same command. Letting only the container CLI read settings is insufficient: host settings are not mounted automatically, so host Harbor must first receive the connection environment. This run used that method in a new right split in the current Herdr tab, preserving focus in the original pane. - -Use a new `--job-name` for each rerun. To run all 89 tasks, remove `--n-tasks 20`, choose a separate baseline job name, and preserve the other conditions. This reruns the entire dataset, with pilot costs counted separately. Analyze this run before executing all tasks or increasing attempts. - -#### Budget and recording plan - -The certificate task cost 0.00222441 USD, but a simple task cannot represent the dataset's average. According to [OpenRouter model prices](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) and [provider quotes](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints) checked on 2026-09-06, Baidu's uncached-input/cache-read/output prices were 0.04998/0.009996/0.09996 USD per million tokens. DeepSeek's base prices were 0.22/0.007/0.66 USD. Routing, promotions, and time affect actual quotes. - -At one million input and 30,000 output tokens per task, with 80% input cache hits, 20 tasks would cost about 0.4–1.4 USD at those two price levels. At three million input and 150,000 output tokens, the estimate becomes 1.4–5 USD. These are budget scenarios, not observed averages or cost ceilings. A 50-turn limit is not a dollar cap. These estimates cover only model API costs, excluding cloud containers, networking, and local execution. - -Record and verify the run in this order: - -1. Save `jobs//config.json`, `lock.json`, task content hashes, and the selected 20-task list. -2. Preserve each `result.json`, `agent/trajectory.json`, agent text log, and verifier log. -3. Count reward-1 tasks, reward-0 tasks, unscored tasks, and infrastructure exceptions separately. Report passes against planned tasks without silently reducing the denominator. -4. Check ATIF validation, usage/cost completeness, and Harbor metric backfill for every trajectory. Missing or partial costs stay unknown; known subtotals are not complete totals. -5. Record total input/cache/output tokens, actual USD cost, job wall time, and per-task durations. Input already includes cache tokens; do not add them again. -6. Distinguish incorrect solutions, exhausted turns, execution timeouts, API errors, and installation/image/verifier failures. Preserve first results and create separate records for reruns. -7. Adjust the full 89-task budget using pilot consumption, then establish a single-attempt baseline. If later measuring variation, retain every repeated experiment instead of replacing first results with the best rerun. - -`/jobs/` is Git-ignored. Archive raw results separately and preserve reviewable configuration, per-task summaries, and artifact locations in development notes. Recording only local directory names does not place a baseline under version control. - -#### Results of the 20-task pilot - -All 20 tasks in `tb21-flash0731-pilot20-20260906` have finished. The [machine-readable results](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) retain per-task versions, metrics, termination reasons, and cost completeness. Individual billing receipts remain local. - -| Metric | Observed result | -| --- | --- | -| Run date | 2026-09-06 | -| Job wall time | 111 min 10 sec | -| Passed / failed / unscored | 8 / 12 / 0 | -| Pass rate, with 20 planned tasks as denominator | 8 / 20 = 40% | -| Passed without Harbor exceptions | 7 / 20 = 35% | -| Harbor exceptions / automatic retries | 1 / 0 | -| Model replies / tool calls | 267 / 329 | -| Original Harbor input / cache / output tokens | 5,853,395 / 5,050,368 / 311,514 | -| Input / cache / output tokens recalculated from final ATIF | 6,207,708 / 5,362,944 / 331,041 | -| ATIF files validated | 20 / 20 | -| Original Harbor metrics consistent | 19 / 20 | -| Trajectories with incomplete costs before independent lookup | 13 / 20 | -| Original Harbor job cost total | 0.045695378 USD | -| Known cost subtotal across trajectories | 0.11053603 USD | -| Cost after independent reconciliation | 0.119200332 USD, complete | - -The table below uses final ATIF model metrics and independently reconciled costs. Original `result.json` files and trajectories were not rewritten. - -| Task | Reward | Model replies | Last stop reason | Cost (USD) | -| --- | ---: | ---: | --- | ---: | -| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | -| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | -| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | -| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | -| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | -| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | -| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | -| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | -| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | -| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | -| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | -| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | -| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | -| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | -| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | -| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | -| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | -| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | -| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | -| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | - -`pytorch-model-recovery` has both reward 1 and `AgentTimeoutError`. Preserve Harbor's raw pass rate of **8/20 (40%)**, and separately report **7/20 (35%) passing without Harbor exceptions**, both using the planned 20 tasks. This task is not a normally completed success sample. - -#### Problems exposed by the pilot - -1. **The final reply in 11 failed tasks has `max_tokens`.** The 8192-token response limit leaves some replies spending most of their budget on thinking before delivering a complete solution. At the time, the agent interpreted “no more tools requested” as the model finishing its work, without distinguishing replies interrupted by budget exhaustion. Those trajectories therefore still end with outcome `completed`. Explicit truncation handling and reasoning/output budget evaluation are needed; pass rates and costs at a higher limit require another experiment. -2. **`mteb-leaderboard` exhausts 50 replies.** Its outcome is `max_turns_exhausted`; the final reply still requests tools and the verifier-required `/app/result.txt` has not been created. This differs from truncating one response. -3. **The agent keeps running after timeout and encounters a tool-argument exception.** Measured from agent execution start, `pytorch-model-recovery` reaches its limit around second 900 and verification starts around second 901. Its journal records new model calls around seconds 929 and 939. The final `edit` supplies `old_text` and `new_text` but no `path`, causing a `KeyError` while displaying the tool call. The run failure is recorded around second 1063. This exception occurs after the timeout and cannot explain it. Execution overlaps verification, limiting how the reward can be interpreted. -4. **Harbor's original aggregate misses the timed-out task's metrics, and cost availability is delayed.** At timeout collection, its trajectory is marked missing. A valid ATIF file later appears and matches an independent backup, but Harbor tokens remain null. Only 19/20 original metrics match final ATIF. Thirteen trajectories initially have partial costs, with 18 generations unresolved by built-in reconciliation. Independent queries to the same OpenRouter endpoint retrieve all 18 receipts, obtaining costs for **all 267 recorded replies**. The original 0.045695378 USD job aggregate is incomplete; the full observed cost is **0.119200332 USD**, including both calls after timeout. - -Complete input is 6,207,708 tokens, including 5,362,944 cached tokens; output is 331,041. Costs come from generation `total_cost`, not advertised-price estimates or changes in the entire account balance. All 20 tasks have scores and valid ATIF. Apart from the timeout, Harbor records no other trial exceptions. - -The original job is under `jobs/tb21-flash0731-pilot20-20260906/`. Its local archive, `jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`, contains the task list, logs, trajectories, per-generation costs, supplemental receipts, Docker image identifiers, and one-off execution and aggregation scripts. It has not been uploaded; its SHA-256 is in the machine-readable results. Two original `torch-pipeline-parallelism` logs contain runtime API credentials. Their permissions are 0600 and archived copies are redacted; checksums for both remain in local records. - -Version control retains only concise experimental configuration, per-task scores, tokens, cost, durations, and exception summaries. Request IDs, internal run IDs, individual billing receipts, precise call times, host hardware, and complete conversations remain local. This minimization applies to the latest version; earlier pushed commits retain reconciliation metadata, and Git history was not rewritten. - -#### Next run and budget implications - -First fix truncation handling, missing-tool-argument crashes, timeout termination, and trajectory collection, then improve delayed cost lookup. These fixes belong to a new revision; the agent source remained pinned throughout this run. Rerun the same 20 task hashes under a new job name, comparing passes without exceptions, termination reasons, tokens, and costs. Preserve this pilot as the pre-fix baseline. Once execution and accounting are reliable, run all 89 tasks and record that result as the full-dataset baseline. - -This pilot costs about 0.12 USD. Multiplying mechanically by 89/20 gives about 0.53 USD, but 11 tasks truncate early and the sample is the first 20 list entries, so this cannot budget the full dataset after fixes. The earlier scenario of three million input tokens, 150,000 output tokens, and 80% input cache hits per task implies about 6–22 USD for 89 tasks. Adjust this to provider prices at the time and consumption in later pilots. Only 20 tasks have run; the full 89-task experiment remains pending. - -### Fixing problems found in batch Terminal-Bench runs - -The following notes explain the problems, implementations, and proposed fixes. Truncation handling, configurable model generation budgets, and recovery from missing tool arguments are implemented. The remaining sections describe problems and proposed work whose code has not yet been implemented. - -#### Truncation handling - -In the original pilot, each model reply had an 8192-token generation limit. If the model exhausts this budget before finishing, the API returns `stop_reason="max_tokens"`, indicating a forced stop at the generation limit. The model may still be analyzing a code change before producing a tool call or complete solution. Some pilot replies spent most of their budget on thinking; all 11 affected failed tasks ended with `max_tokens`. - -Previously, the agent interpreted “no more tools requested” as the model finishing its work. However, generation-budget exhaustion can also stop a reply before it produces a tool call. As a result, unfinished tasks were recorded as `completed`. Evaluating a run requires distinguishing a normal model finish, exhaustion of the task's turn budget, and truncation of an individual reply: each explains a different stopping reason. Even a normal model finish leaves correctness for the verifier to establish. - -This change first makes the reported result trustworthy: recognize truncation, preserve evidence, and explicitly say that the task remains unfinished. Stop the run after truncation. Larger generation budgets and automatic recovery are follow-up capabilities whose effectiveness and additional consumption will be evaluated separately. - -**Implemented on 2026-09-07: recognize truncation and stop.** - -Record the stopping reason as `response_truncated`, even on the final allowed turn, so users know that the last reply itself did not finish generating. The agent makes no further model calls and skips every tool call in that reply, including calls with apparently complete arguments, to avoid acting on an unfinished response. - -Generated text, thinking, tool arguments, and usage remain in the execution record, and cost reconciliation still runs. This allows analysis of where the budget went and where progress stopped. Unexecuted tools are not recorded as executed and have no fabricated results. Analysis must distinguish whether a run ended normally from whether its task was completed. - -Users see an explicit truncation diagnostic, and text already output remains visible. Headless still exits 0, leaving the verifier to judge task success. Interactive mode returns to the input prompt so the user can send another message. Subsequent conversation retains generated text and a truncation notice while removing unexecuted tool requests and incomplete thinking, avoiding invalid history in the next request. The original reply remains in the execution record for later inspection. - -The new stopping reason exceeds the old record protocol's allowed values, so new records use Journal v2. Existing v1 records remain readable and convertible, and public trajectories stay at ATIF-v1.7. A model reaching its generation limit is independent of logs shortening content to control storage size. See [Event Journal v2](../../dev_docs/en/event-journal-protocol-v2.md) for fields, event ordering, and compatibility rules. - -Regression tests cover text, thinking-only and empty replies, truncation on the last turn, skipped tools, the next interactive turn, and usage/cost preservation. A locally simulated SSE response exercises partial tool JSON through the real Anthropic SDK. Old v1 and new v2 truncated trajectories pass Harbor's pinned ATIF validator. These tests do not call paid models. At implementation time on 2026-09-07, no benchmark had been rerun and no pass-rate improvement was inferred from the automated tests. The real-model rerun on 2026-09-09 follows. - -Select `regex-log` and `write-compressor` from `tb21-flash0731-pilot20-20260906`. Both previously scored 0 and ended with `max_tokens`, while their old trajectories recorded outcome `completed`. This rerun checks both official verifier scores and accurate truncation reporting on the current branch. - -**Method and configuration.** Open a right split in the current Herdr tab, preserve focus in the original pane, and run from the repository root. Execute the two tasks concurrently with one attempt each, preserving the original job. - -| Item | Rerun configuration | -| --- | --- | -| Agent branch / commit | `fix/handle-truncated-model-responses` / `f7788616e11f3347300ac2fdef6c6400d4eb5611` | -| Installed container package | `0.8.1.dev11+gf7788616e` | -| Harbor | 0.21.0, run through `uv run --locked --project benchmarks/harbor` | -| Dataset | `terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | -| Model / API endpoint | `openrouter/deepseek/deepseek-v4-flash-0731` / `https://openrouter.ai/api` | -| Generation limits | 8192 tokens per reply; at most 50 model replies per task; no explicit reasoning effort or temperature | -| Environment and retries | Local Docker, concurrency 2, one attempt per task, Harbor `max_retries=0`; task-default resources and timeouts | -| Job | `tb21-truncation-rerun2-20260909-f778861` | - -Reuse the original task versions and verify identical task identities in the old and new trial configurations: - -| Task | Task hash | -| --- | --- | -| `terminal-bench/regex-log` | `sha256:802c16cfd132e6c457529cb864be5a757c1b23b6cadc57f2d01983cb0110292a` | -| `terminal-bench/write-compressor` | `sha256:d9ddd9a8e925e2c566b37b2492cbf995afecefe58874e4043ef78d7f3c892c7e` | - -The branch had not been pushed, so containers could not install its remote Git revision. Instead, run `uv build --wheel`, compare every packaged Python source file with the commit, and upload the wheel into each container's `/tmp/`. A temporary test adapter changes installation to use this local wheel. Model calls, tools, and trajectory export still use the current branch. The wheel SHA-256 is `3c4ebd9268680ba0798ccbc44e6e56ee91424c5773399171050b7ac24734e9e4`. - -The launcher reads host connection settings, removes the `ANTHROPIC_MODEL` override, and checks that the endpoint is OpenRouter. Credentials pass through the environment without appearing in command arguments. Task filters require the `terminal-bench/` prefix: - -```bash ---include-task-name terminal-bench/regex-log \ ---include-task-name terminal-bench/write-compressor -``` - -The actual local entry point was: - -```bash -uv run --locked --project benchmarks/harbor python \ - jobs/tb21-truncation-rerun2-20260909-f778861-record/workflow/run.py -``` - -The script prepares the wheel, records a manifest, and invokes Harbor. It and the temporary adapter are local experiment artifacts, not a committed general-purpose benchmark command. - -Related automated regressions also ran that day: all 54 core tests and 16 Harbor adapter tests passed: - -```bash -.venv/bin/python -m pytest -q \ - tests/test_truncation.py tests/test_event_journal.py tests/test_atif.py -benchmarks/harbor/.venv/bin/python -m pytest -q \ - -c benchmarks/harbor/pyproject.toml benchmarks/harbor/tests -``` - -**Results.** Both tasks completed verification without Harbor exceptions. The pass rate was **0/2**, and Harbor reported about **9 min 42 sec** of job wall time. Both truncations were correctly recorded, but neither task was completed. - -| Task | Previous / new reward | Model replies | Final reply tokens | Last stop reason | New terminal outcome | Verifier failure | -| --- | --- | ---: | ---: | --- | --- | --- | -| `regex-log` | 0.0 / 0.0 | 1 | 8192 | `max_tokens` | `response_truncated` | `/app/regex.txt` was not created; 1 test failed | -| `write-compressor` | 0.0 / 0.0 | 2 | 8192 | `max_tokens` | `response_truncated` | `/app/data.comp` was not created; 3 tests failed | - -| Task | Input tokens | Output tokens | Cost (USD) | -| --- | ---: | ---: | ---: | -| `regex-log` | 1678 | 8192 | 0.00158363 | -| `write-compressor` | 3927 | 8303 | 0.001742255 | -| Total | 5605 | 16495 | 0.003325885 | - -Costs come from actual accounting in the final trajectories and are complete for both tasks. Both ATIF files pass Harbor validation, startup versions contain `f778861`, both logs include explicit truncation diagnostics, and no model call follows the final reply. Neither final reply contains tool calls; skipping partial tool arguments and maintaining valid follow-up history are covered by the automated tests but were not triggered by these two real runs. Harbor's successful exit 0 does not establish task success; verifier rewards remain authoritative. - -This rerun verifies recognition of truncation followed by stopping. It neither increases the response limit nor resumes generation. Both tasks still exhaust their per-response budgets before delivering the required files. These results do not establish pass rates with larger budgets or automatic continuation; those need separate experiments. - -Raw trial logs, trajectories, and verifier output are under `jobs/tb21-truncation-rerun2-20260909-f778861/`. The corresponding `jobs/tb21-truncation-rerun2-20260909-f778861-record/` contains `manifest.json`, `summary.json`, `results.en.md`, the wheel, and run, temporary-installation, and summary scripts under `workflow/`. These directories are Git-ignored and have not been uploaded. This document preserves versions, methods, and results without treating local artifacts as version-controlled files. Future reruns must use new job names and preserve this original 0/2 result. - -#### Increasing the model generation budget - -**Per-reply generation budgets are now configurable, with the default raised from 8192 to 32768 for evaluation.** This budget is the **generated-token limit for one model reply**, not cumulative task usage or the context window. **In fully traced experiments, `regex-log` passed twice at 32768; `write-compressor` truncated twice at 32768, then passed a 09-12 rerun explicitly configured at 65536, producing a 2263-byte file.** However, the successful run's largest reply used only **32073 output tokens**, never consuming output space above 32768, so this observation alone cannot attribute success to doubling the budget. Agent execution took **1976.4 seconds**, with no target file at the 900-second checkpoint, establishing that this execution did require a longer deadline. Earlier installation failures, service interruptions, and timeouts remain recorded below. - -**The other 18 tasks passed 5/18 on their initial attempts at 65536, versus 8/18 in the same group's 8192 baseline; five additional attempts across four tasks added no passes.** These experiments did not terminate at `max_tokens` or the 3600-second agent deadline. They also exposed the 50-turn limit, missing or malformed tool arguments, and environment failures. Their failures cannot all be attributed to thinking budgets, nor do these observations establish that increasing the budget improved or reduced the pass rate. - -**Research conclusion: 32768 and 65536 are suitable experimental candidates, but public sources do not establish a universal default to copy.** TB 2.1 evaluations explicitly use both values. Code agents include defaults around 32k/64k, model-dependent limits, and implementations that omit an explicit limit. Evaluation settings and product defaults are recorded separately below so model capacity is not mistaken for an actual request parameter. - -**Settings used by other agents/models on Terminal-Bench.** These first-party evaluation descriptions were checked on 2026-09-09, focusing on this project's **TB 2.1**; TB 2.0 is supplementary. Values are per-generation limits disclosed by the publisher, not cumulative task consumption or independently captured production requests. - -| Benchmark | Model and agent | Disclosed per-generation limit | Comparison conditions and sources | -| --- | --- | --- | --- | -| **TB 2.1** | GLM-5.3 and GLM-5.3-Flash / Claude Code **2.1.207** | **65536** (`max_new_tokens`) | Z.ai specifies `temperature=1.0`, `top_p=1`, and a six-hour timeout. [GLM-5.3 notes](https://huggingface.co/zai-org/GLM-5.3/blob/main/README.md#footnotes), [Flash notes](https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/README.md#footnotes) | -| **TB 2.1** | GLM-5.3-Flash-NVFP4 / Terminus-2 | **32768** (`max_tokens`) | RadixArk's quantized-model evaluation uses `max` effort and **disables task deadlines**. Some trials could not run or finish, so its pass rate is not a comparison under this project's conditions. [Evaluation notes](https://huggingface.co/RadixArk/GLM-5.3-Flash-NVFP4#evaluation) | -| TB 2.0 (supplementary) | Qwen3.6-27B / Harbor + Terminus-2 | **80K** (`max_tokens`, preserving the original unit) | Qwen specifies 256K context, a three-hour timeout, 32 CPUs / 48 GB RAM, and a five-run average. [Model-card footnotes](https://huggingface.co/Qwen/Qwen3.6-27B#evaluation) | -| TB 2.0 (supplementary) | Kimi-K2.6 / Tinker Cookbook's simplified agent | **8192** (`max_tokens`) | Thinking Machines uses 32K context, 200 turns, and no context compaction, and reports many context-limit failures. This establishes that 8192 has been used, not that it suits this project. [Configuration and results](https://github.com/thinking-machines-lab/tinker-cookbook/blob/main/tinker_cookbook/recipes/harbor_rl/README.md#evaluation) | - -DeepSeek-V4 is closer to this project's model. Its technical report describes an internal bash + file-edit harness, at most 500 steps, and 512K **context**, but that section does not disclose per-reply `max_tokens` and evaluates TB 2.0. The 512K value therefore cannot be treated as a generation budget or establish the appropriate setting for `deepseek-v4-flash-0731` on TB 2.1. [Report §5.3.1](https://arxiv.org/html/2606.19348v1#S5.SS3.SSS1) - -**Defaults in other code agents.** Defaults require a version, model, and configuration path. Source links below are pinned to the checked commits; official documentation reflects the 2026-09-09 lookup. - -| Agent / inspected scope | Behavior without an explicit override | Evidence and limits | -| --- | --- | --- | -| Claude Code / Opus 4.6 | **64k** | The **v2.1.77 release notes** explicitly raise this model's default to 64k. The same entry's 128k is an available upper limit, not the default, and this finding cannot be generalized to every model. [Release notes](https://github.com/anthropics/claude-code/releases/tag/v2.1.77) | -| Claude Code / unrecognized model IDs, such as custom gateway names | **32000** | Current official documentation specifies this fallback, overridable with `CLAUDE_CODE_MAX_OUTPUT_TOKENS`. Defaults and ceilings for recognized models vary. [Environment variables](https://code.claude.com/docs/en/env-vars#variables) | -| OpenCode / `830d5eb` | **`min(model output limit, 32000)`**; missing or zero model limits fall back to 32000 | The default request-preparation path uses this calculation; environment variables and plugins can override it. [Calculation](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/provider/transform.ts#L1468), [request construction](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/session/llm/request.ts#L117) | -| pi / ordinary model-request path at `acaa253` | **Determined by the model's `maxTokens`**, then tightened to remaining context | `buildBaseOptions` uses `options.maxTokens ?? model.maxTokens` and reserves 4096 tokens in its context estimate. There is no common fixed value for all models. Thinking budgets also undergo provider-specific processing. [Source](https://github.com/earendil-works/pi/blob/acaa253cc8e3f159e6100b6f3874861b1f0bfc99/packages/ai/src/api/simple-options.ts#L11) | -| mini-swe-agent / default configuration and LiteLLM path at `04d809c` | **No explicit uniform `max_tokens`** | The default YAML omits it; the model layer forwards `model_kwargs`. The selected configuration, LiteLLM, and server determine the actual value. Omitting the parameter does not mean unlimited generation. [Default configuration](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/config/default.yaml), [model call](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/models/litellm_model.py#L64) | -| Codex CLI / official configuration reference | **No uniform default number confirmed in this research** | The inspected configuration reference does not give a default per-generation limit. API model-page output capacity cannot substitute for the CLI's actual default. [Configuration reference](https://learn.chatgpt.com/docs/config-file/config-reference) | - -**Record 32000 and 32768 separately**, rather than reducing both to “32k.” Preserve original units where sources disclose only `64k` or `80K`. Thinking limits and effort levels such as `high`/`max` also need to remain distinct from total generation limits; a separate thinking parameter has not been designed. - -**Implementation and automated acceptance.** Implementation commit: `7a610cbf8`. Configuration uses `--max-tokens`, `ANTHROPIC_MAX_TOKENS`, and the existing `settings.json` `env` object, with precedence **CLI > environment > settings file > 32768**. Both interactive and headless modes resolve the value once at startup and use it for every reply. The effective value must be a positive integer; invalid values fail before any request with exit code `2`. Settings entries retain the existing string rules, for example `"ANTHROPIC_MAX_TOKENS": "32768"`. - -Harbor forwards `--agent-kwarg max_tokens=N` and `ANTHROPIC_MAX_TOKENS`. Omitting the kwarg does not insert a CLI default, allowing environment configuration and the installed version's default to apply. Startup output, Journal `run.started.max_tokens`, and ATIF `agent.extra.max_tokens` record the effective limit separately from actual usage. Older journals without the field still convert; ATIF does not invent a historical budget. - -```bash -nanoPyCodeAgent --max-tokens 65536 -p "fix the failing tests" -# Harbor: add --agent-kwarg max_tokens=65536 to the run command -``` - -All **242 core tests** and **22 Harbor adapter tests** passed. Coverage includes precedence, budgets on every interactive/headless request, early rejection of invalid values, separation of recorded limits from usage, and real SDK request serialization. **Real-model results at 65536 appear in the 09-12 experiment below; successful parameter forwarding does not establish effectiveness attribution.** - -**Handling `stop_reason="max_tokens"` must remain even with a higher limit.** Any finite budget can be exhausted. Raising it gives the model more generation room; existing truncation handling records the stopping reason accurately. Both preceding cases exhausted 8192, providing a comparison point without guaranteeing success at a higher limit. - -Truncation regressions also pass at higher budgets: `max_tokens` still produces `response_truncated`, preserves usage and costs, and skips tools from that reply. This change adds neither a separate thinking parameter nor automatic continuation. - -**Real-model experiment at 32768.** New job `tb21-budget32768-20260909-7a610cb` compares against the 8192 results in `tb21-truncation-rerun2-20260909-f778861`. It retains the same TB 2.1 dataset, task hashes, OpenRouter `deepseek/deepseek-v4-flash-0731`, `max_turns=50`, native 900-second task timeouts, concurrency 2, one attempt per task, and no retries. A local wheel was built from the implementation commit, with each packaged Python source verified against that commit. The launcher removes `ANTHROPIC_MAX_TOKENS` and omits `--max-tokens` to test the built-in default. - -The first `regex-log` trial exceeded the default 360-second installation timeout before model execution and received no verifier score; that record remains in the original job. New job `tb21-budget32768-regex-setup-retry-20260909-7a610cb` reruns only that task with an installation timeout of 1080 seconds (`--agent-setup-timeout-multiplier 3`), retaining its 900-second task deadline. - -The initial `write-compressor` trial entered model execution and completed tool calls in its first reply. Its second call received the server error `stream closed before completion` after about 421 seconds of agent execution. Harbor records `UnknownApiError`, and the verifier reward is 0. The response did not return `max_tokens`, so this does not establish budget exhaustion. The completed first reply used 1528 input / 103 output tokens and cost $0.00011786; complete usage and cost for the interrupted call are unknown. That known subtotal must not be reported as the task's complete cost. - -To check whether the service failure was transient, one additional attempt uses the same model, budget, and task deadline under `tb21-budget32768-compressor-api-retry-20260909-7a610cb`, also with a 1080-second installation window. Both model runs are retained rather than replacing the initial failure. - -**Final results (executed 2026-09-09, verified and archived 09-10).** Times below are Harbor's agent execution durations, excluding installation and verification. The initial installation failure has no model execution time or score. - -| Task | 8192 baseline | Initial 32768 trial | 32768 retry | -| --- | --- | --- | --- | -| `regex-log` | Reward **0**; `response_truncated`; 187.3 sec | `AgentSetupTimeoutError`; no model execution or score | Reward **0**; `AgentTimeoutError`; 900.9 sec | -| `write-compressor` | Reward **0**; `response_truncated`; 304.4 sec | Reward **0**; `UnknownApiError`; 421.4 sec | Reward **0**; `AgentTimeoutError`; 900.9 sec | - -Both retry startup logs confirm `max tokens 32768`; agent versions and task hashes match the experimental configuration. Verifiers report missing `/app/regex.txt` and `/app/data.comp`, respectively. Including the initial server interruption, **three runs entered model execution and received scores, all 0**; one additional trial failed during installation. No retry result replaces an initial failure. - -**A complete usage and cost comparison was not possible.** Neither timed-out retry exported final ATIF. Harbor records trajectory status `missing` and `null` input/output tokens and costs. Model-call counts, final `stop_reason`, and actual truncation counts also cannot be fully reconstructed from the available records; missing values must not become zeroes. Complete baseline costs at 8192 were $0.00158363 and $0.001742255. At 32768, only the initial `write-compressor` trial's completed first reply has the known subtotal of $0.00011786, so no reliable total cost increase can be calculated. - -**Keep 32768 as the experimental default, without describing the 09-09 experiment as successful acceptance of the higher budget.** Configuration, request forwarding, and truncation regressions are verified. Real-model evaluation remains affected by a server interruption, task timeouts, and missing trajectories after timeout. Without complete final replies, the experiment cannot establish that a higher budget eliminates `max_tokens` truncation or imply a gain across the full benchmark. - -All three jobs used the same wheel, SHA-256 `91996b1d4c70af8062318a38b3af9235c85d93c9685e10ac1323f3bef3625425`. Raw logs, verifier output, and exceptions remain in the corresponding jobs; each `-record/` directory contains its manifest, wheel, and launcher. The consolidated summary is under `jobs/tb21-budget32768-20260909-7a610cb-record/` in `summary.json`, `results.en.md`, and `workflow/summarize.py`. These `jobs/` artifacts are Git-ignored and have not been uploaded. The associated containers have been cleaned up. - -**Extended deadline with a complete trajectory (2026-09-10).** Trial `regex-log__2A9PJgL` in new job `tb21-regex-3600s-trace-20260910-7a610cb` received **reward 1.0**, passing the official verifier's `test_regex_matches_dates`. Harbor records **580.4 seconds** of agent execution, excluding installation and verification. This success did not consume time beyond the original 900-second deadline, so it does not establish that extending the deadline resolved the earlier timeout. - -The run retained the same wheel, task hash, model, built-in `max_tokens=32768`, and 50-turn limit, with one task, one attempt, and no retries. Agent execution was allowed **3600 seconds**, followed by 120 seconds for interruption and export. Harbor's outer allowance was 3780 seconds (`--agent-timeout-multiplier 4.2`) to avoid preempting cleanup. Installation remained at 1080 seconds; the verifier deadline was unchanged. A temporary diagnostic wrapper saved an unshortened Journal, HTTP response bodies, per-chunk arrival times, and stack snapshots every 120 seconds directly to persistent logs, retaining the comparison's agent source, prompt, and tools. This run finished naturally without interruption or trajectory reconstruction. - -| Observation | Result | -| --- | --- | -| Model replies / tool calls | **5 / 6**; the first four replies ended with `tool_use`, the last with `end_turn`; no `max_tokens` truncation | -| Total model duration | **567.3 seconds**, approximately **97.9%** of native run time | -| First / subsequent reply durations | **502.4 seconds** / 35.1, 2.8, 4.2, 22.8 seconds | -| Total local tool duration | **0.023 seconds** | -| Complete usage | **74650** prompt tokens, including **53504** cached tokens; **17264** completion tokens | -| Complete cost | **$0.005338074**, with costs reconciled for all five calls | - -**The evidence points to sustained first-reply thinking generation as this run's main delay, rather than local tool execution.** Thinking began about 2.5 seconds after the first request and continued through 500.8 seconds; tool arguments began at 501.7 seconds. The longest interval between nonempty generation deltas was only **3.4 seconds**, with no long stream gap observed. That reply retained 47007 thinking characters. The provider reported **15123 output tokens**, with **12026** in `output_tokens_details.thinking_tokens`. Character counts and provider token accounting differ; subtracting those token fields does not independently measure visible-text usage. - -Integrity checks passed: **147 Journal entries, none truncated**; native ATIF passed Harbor schema validation and exactly matched reconstruction from the Journal. All five streams contained `message_stop`, and all five requests actually sent 32768. Stream analysis uses monotonic clocks to avoid small system-clock adjustments. The diagnostic wrapper also passed three offline scenarios: normal completion, interruption after a simulated stall, and recovery after forced termination. - -**The exact cause of the historical timeout remains undetermined.** Without that run's complete reply and stream timeline, prolonged generation, provider/network stalls, and a different solution path cannot be distinguished. The new run recorded Anthropic SDK **1.4.0**, httpx **0.28.1**, and httpx2 **2.12.0** in its container; corresponding versions were not retained for the earlier container. Equal wheel bytes therefore do not establish identical runtime conditions. This result demonstrates that this task can finish at 32768, but one success cannot establish a causal benefit from extending the deadline or generalize to the other task or full benchmark. - -Complete ATIF is in `regex-log__2A9PJgL/agent/trajectory.json` under the new job. Adjacent `journals/`, `http/`, `http-events.jsonl`, and `python-stacks.log` retain the raw diagnostic evidence. The corresponding `-record/` directory contains `results.en.md`, `summary.json`, `validation.json`, and `workflow/` for the report, integrity checks, and reproduction scripts. Artifacts remain local under Git-ignored `jobs/` and have not been uploaded. The container has been cleaned up, and earlier failure records remain intact. - -**Repeating the method for `write-compressor` (2026-09-11).** Trial `write-compressor__GTmsjFV` in new job `tb21-compressor-3600s-trace-20260911-7a610cb` received **reward 0**. All three official verifier tests failed because `/app/data.comp` was absent. Harbor records **1449.1 seconds** of agent execution (about 24 minutes), no infrastructure exception, and complete native trajectory export. The final outcome is **`response_truncated`**; the 3600-second deadline was not reached. - -The run retained the preceding diagnostic method: the same model and wheel, built-in `max_tokens=32768`, a 50-turn limit, one task, one attempt, and no retries. Execution, finalization, and Harbor's outer allowances were 3600, 120, and 3780 seconds respectively; installation remained at 1080 seconds. The diagnostic wrapper was byte-identical and continued saving an unshortened Journal, raw response streams, arrival times, and stack snapshots. The task hash matched the preceding `write-compressor` baseline. - -| Model reply | Duration | Output tokens | Provider-reported thinking tokens | Stop reason | -| --- | --- | --- | --- | --- | -| 1 | **5.8 seconds** | 118 | 16 | `tool_use` | -| 2 | **1414.2 seconds** | **32768** | **32767** | **`max_tokens`** | - -**This run directly showed sustained thinking outlasting the original deadline, exhausting the generation budget, and leaving no delivered file.** Reply 1 ran `read` and `bash` in just **0.004 seconds** combined. Reply 2 began returning thinking after about 0.9 seconds and continued through 1414.1 seconds. Its longest nonempty generation-delta gap was **2.2 seconds**, with no long silent interval observed. The complete reply retained **116281 thinking characters**, with no visible text or tool arguments. The current agent ends the run on `max_tokens` without automatic continuation, so no subsequent call could produce the file. - -At **900.4 seconds**, an independent read-only checkpoint near the original deadline confirmed that `data.comp` was absent. Only the first reply and two tool calls had completed; the second reply was still streaming thinking. This establishes that the observed execution could not deliver within the original window. The longer allowance let it continue until the generation budget was exhausted: **extending the deadline alone did not solve this failure**. This timeline explains the present execution, not the missing historical trajectory, and the earlier server interruption must remain distinct from this truncation. - -Complete usage was **3926 prompt tokens**, **0 cached tokens**, and **32886 completion tokens**, costing **$0.00615504**. Cumulative output exceeds 32768 because the budget applies per reply. Costs were reconciled for both calls; the second receipt returned HTTP 404 four times before succeeding on the fifth attempt, after the reply had already been truncated. Harbor installation and verification lasted 143.9 and 226.2 seconds respectively, outside the reported agent execution time. - -**Complete evidence and a truncated model reply are distinct.** All **13 Journal entries are unshortened**, including thinking beyond the default 100000-character logging threshold. Native ATIF passed Harbor schema validation and matched projection from the Journal. Both response streams were fully consumed, contained `message_stop`, and used an actual request budget of 32768. The container installed Anthropic SDK **1.5.0**, versus 1.4.0 in the preceding regex diagnostic. The wheel's dependency range permits newer versions, so using the same method does not fix every dependency or backend condition. - -Complete ATIF is in `write-compressor__GTmsjFV/agent/trajectory.json` under the new job, alongside the Journal, raw HTTP responses and timeline, stack snapshots, and runtime metadata. The corresponding `-record/` contains `results.en.md`, `summary.json`, `validation.json`, `checkpoints/`, and reproduction scripts, including the file check at the original 900-second deadline. Artifacts remain local, Git-ignored, and not uploaded. The container has been removed; all earlier failures and the successful regex record remain intact. - -**One further attempt per task (2026-09-11).** This pair ran `regex-log` and `write-compressor` sequentially, once each, without automatic retry. The second task started only after the first task's verification and container cleanup. Model, wheel, task hashes, the built-in 32768 budget, 50-turn limit, and 3600/120/3780-second allowances remained unchanged from the preceding diagnostics. - -| Task | Preceding complete diagnostic | New repeat | -| --- | --- | --- | -| `regex-log` | Reward **1**; 580.4 seconds; 5 replies | Reward **1**; **631.2 seconds**; 9 replies, ending with `end_turn` | -| `write-compressor` | Reward **0**; 1449.1 seconds; `max_tokens` in reply 2 | Reward **0**; **582.1 seconds**; `max_tokens` in reply 3 | - -Times are Harbor agent execution durations, excluding installation and verification. **Neither new run consumed agent time beyond the original 900-second window.** There was no deadline event, forced termination, or trajectory reconstruction. - -Regex's first reply still took about **517.4 seconds**, using 16445 output tokens, including 16346 provider-reported thinking tokens. It subsequently completed eight tool calls in 0.039 seconds combined. None of its nine replies was truncated, and the official `test_regex_matches_dates` passed again. - -Compressor's second reply completed a tool call after **172.5 seconds and 5463 output tokens**, allowing it to reach reply 3. That reply then took **394.8 seconds** and exhausted the **32768** total generation budget, with **28961** provider-reported thinking tokens. Its captured stream contains **112785 thinking characters** and no visible text or tool arguments; subtracting token fields cannot establish a visible-text count. The longest nonempty generation-delta gap was 1.5 seconds. All three tools took about 0.004 seconds combined. `/app/data.comp` was still absent, so all three official tests failed, with native outcome `response_truncated`. - -| Task | Complete prompt tokens (including cache) | Cached tokens | Completion tokens | Complete cost | -| --- | --- | --- | --- | --- | -| `regex-log` | 161870 | 137216 | 20400 | **$0.00720948** | -| `write-compressor` | 12704 | 1280 | 38335 | **$0.00764908** | - -The two tasks' **256 and 18 Journal entries are all unshortened**. Both native ATIF files passed Harbor schema validation and exactly matched projection from their Journals. All 12 model response streams were fully consumed and contained `message_stop`; every actual request sent 32768, and all generation costs were reconciled. Installation took 833.6/448.1 seconds and verification 114.9/264.6 seconds respectively, recorded separately from agent execution. - -**Both regex diagnostics in this comparison passed, and both compressor diagnostics exhausted the generation budget, but durations and stopping turns differed substantially.** The new compressor failure occurred before 900 seconds, confirming the per-reply cap as this execution's stopping condition. Two observations do not establish that future runs must pass or fail, or that a higher budget would succeed. Both new containers recorded Anthropic SDK 1.5.0 and matching versions of the other recorded packages; the earlier regex run used 1.4.0. Sampling, cache state, and backend conditions were not fixed, so timing differences cannot be attributed directly to one factor. - -The new jobs are `tb21-regex-3600s-trace-repeat2-20260911-7a610cb` and `tb21-compressor-3600s-trace-repeat2-20260911-7a610cb`, with trials `regex-log__NmpJwcQ` and `write-compressor__NqmJQWm`. Each trial's `agent/trajectory.json` and adjacent Journal/HTTP/stack records retain complete evidence. Each `-record/` contains its report and reproduction scripts. The combined comparison is in `results.en.md` and `comparison.json` under `jobs/tb21-3600s-trace-repeat2-20260911-7a610cb-record/`. Raw artifacts remain local, Git-ignored, and not uploaded. Both containers were cleaned up, and all earlier records remain intact. - -**Compressor rerun at 65536 (2026-09-12).** This attempt explicitly passed `--agent-kwarg max_tokens=65536`, retaining the preceding compressor model, wheel, task hash, 50-turn limit, and 3600/120/3780-second allowances. One task ran once without automatic retry; the project default remains 32768. It finished with **reward 1 and all three official tests passing**: `data.comp` is **2263 bytes** and decompresses exactly to the original file. - -| Measurement | Result | -| --- | --- | -| Agent execution | **1976.4 seconds**; installation 278.2 seconds and verifier 169.1 seconds are separate | -| Model replies / tool calls | **32 / 32**; final `end_turn`, outcome `completed` | -| Largest reply | **32073 output tokens**; all actual requests sent 65536, without truncation | -| Complete prompt / cache / completion tokens | **1528746 / 1471744 / 59563**; prompt includes cache | -| Complete cost | **$0.018819072**, with all 32 generation costs reconciled | - -Reply 2 remained the main delay: **1179.7 seconds**, 32073 output tokens including 27900 provider-reported thinking tokens, then a normal `tool_use`. Its complete stream contains 108811 thinking characters and a longest nonempty generation-delta gap of 2.2 seconds. All tools took 27.3 seconds combined. At 900.4 seconds the target file was absent; it measured 2727 bytes at 1299.7 seconds and 2388 bytes at 1449.9 seconds, before the final reduction to 2263. - -**This establishes a successful rerun configured at 65536, but does not establish that 32768 must be insufficient.** Every reply remained below 32768, and sampling, cache state, and backend conditions were not fixed, preventing direct attribution to the doubled cap. The recorded execution did use time beyond the original 900-second deadline. SDK 1.5.0 and the other recorded dependency versions match the preceding compressor diagnostic. There was no deadline event, forced kill, or trajectory reconstruction. - -All 498 Journal entries are unshortened, and native ATIF passes schema validation and exactly matches Journal projection. All 32 model response streams were fully consumed and contain `message_stop`. One cost-receipt GET encountered `ConnectError` before headers, then succeeded on retry without interrupting a model reply. The original analyzer's broad check that every HTTP request received a complete response is consequently false; `validation-notes.json` preserves and explains that distinction. - -Job: `tb21-compressor-65536-3600s-trace-20260912-7a610cb`; trial: `write-compressor__hzfqDFs`. Complete `agent/trajectory.json`, Journal, HTTP streams, and timelines are retained. The corresponding `-record/` holds reports, validation, checkpoints, and reproduction scripts; `task-output/` also preserves the final compressed file, input, decoder source, and hashes. Artifacts remain local, Git-ignored, and not uploaded. The container was cleaned up, and all earlier results remain intact. - -**Extending 65536 to the other 18 tasks (2026-09-12–13).** The initial batch took approximately 2 hours 57 minutes and produced **5 passes, 8 reward-zero results, and 5 unscored results**: 5/18 (27.8%), versus 8/18 for the same tasks in the 8192 baseline. The passing tasks were `pypi-server`, `openssl-selfsigned-cert`, `log-summary-date-ranges`, `pytorch-model-recovery`, and `merge-diff-arc-agi-task`. Five subsequent attempts across four tasks added no passes. Initial and supplemental attempts are reported separately, preserving every failure. - -The experiments retained the same verified wheel from `7a610cb`, DeepSeek V4 Flash 0731 through OpenRouter, and task hashes; cached image digests for all 18 tasks also matched the baseline. Explicit settings were `max_tokens=65536`, 50 turns, concurrency 2, 3600 seconds for agent execution, 120 seconds for finalization, a 3780-second Harbor outer allowance, and 1080 seconds for installation. Verifiers retained native task deadlines. Each task ran once per job, without automatic trial retries. **The project default remains 32768; 65536 is the experimental override.** - -In the table, `—` means no official score was obtained and is not counted as a pass. Reward zero can also result from infrastructure or recorder faults, so its cause must be considered. - -| Task | 8192 baseline | Initial 65536 | Main finding | -| --- | ---: | ---: | --- | -| `torch-tensor-parallelism` | 0 | — | First response stream interrupted; verifier timed out downloading Torch/CUDA dependencies | -| `schemelike-metacircular-eval` | 0 | 0 | Reply 19 interrupted; interpreter passed only 4/63 subcases | -| `kv-store-grpc` | 1 | 0 | `SetValRequest` lacks the required `value` field; 5/7 tests passed, including service availability | -| `pypi-server` | 1 | 1 | Local package installation and API test passed | -| `dna-assembly` | 0 | — | TLS download failure installing uv; no model call | -| `torch-pipeline-parallelism` | 0 | — | Exhausted 50 turns; verifier dependency installation timed out without functional test results | -| `qemu-alpine-ssh` | 1 | — | APT package returned 404 during setup; no model call | -| `openssl-selfsigned-cert` | 1 | 1 | All 6 tests passed | -| `regex-chess` | 0 | 0 | Exhausted 50 turns; invalid successor at the starting position; only the length check passed (1/4) | -| `log-summary-date-ranges` | 1 | 1 | Both tests passed | -| `model-extraction-relu-logits` | 0 | — | TLS failure fetching the uv installer; no model call | -| `path-tracing` | 0 | 0 | Exhausted 50 turns; `image.c` absent; 0/5 tests passed | -| `caffe-cifar-10` | 0 | 0 | Exhausted 50 turns; final build lacked `g++`, with binary and model absent; only the prototxt check passed (1/6) | -| `mteb-leaderboard` | 0 | 0 | Exhausted 50 turns; `result.txt` absent; 0/2 tests passed | -| `llm-inference-batching-scheduler` | 1 | 0 | Recorder received SIGSEGV during a periodic stack dump at approximately 120 seconds; output plans absent | -| `pytorch-model-recovery` | 1 | 1 | All 5 tests passed | -| `circuit-fibsqrt` | 0 | 0 | Incorrect circuit output; empty `bash` arguments triggered native `KeyError('command')`, stopping repair; 2/3 tests passed | -| `merge-diff-arc-agi-task` | 1 | 1 | All 5 tests passed | - -**Supplemental attempts addressed confirmed environment or recorder faults and do not overwrite the initial results.** The three setup failures first retried with cached uv and wheel files to bypass download/APT failures; the scheduler separately reran without periodic stack dumping. The cached installer omitted system CA certificates needed by the DNA image, so DNA received one further attempt with corrected installation. - -| Task | Additional attempts | Supplemental result | -| --- | ---: | --- | -| `dna-assembly` | 2 | First retry failed before response headers because of the omitted CA certificates and cannot evaluate the model. Restoring all original system dependencies allowed 3 replies, but malformed `bash` arguments triggered `KeyError('command')`; `primers.fasta` was absent and 0/1 tests passed | -| `qemu-alpine-ssh` | 1 | After 3 replies, malformed `bash` arguments triggered `KeyError('command')`. Verifier setup independently encountered another APT 404; pytest did not run, so its recorded reward zero is not a functional test failure | -| `model-extraction-relu-logits` | 1 | Completed 14 replies normally, but row 11 of the recovered matrix did not match and the official test failed | -| `llm-inference-batching-scheduler` | 1 | No SIGSEGV; after 15 replies, malformed `write` arguments triggered `KeyError('content')`; plans were absent, with only the input integrity check passing (1/6) | - -Recorder and installation differences are part of the evidence. Before any initial batch trial started, supervisor cleanup was corrected to preserve background task services on normal worker exit and clean the process group only at the deadline. Offline service tests passed, as did the `kv-store-grpc` service availability test. System core metadata, ELF files from the same image, and disassembly located the scheduler crash at `PyCode_Addr2Line+11` during periodic stack dumping. This path matches the [CPython timer implementation](https://github.com/python/cpython/blob/v3.13.7/Modules/faulthandler.c#L537) and [frame dumper](https://github.com/python/cpython/blob/v3.13.7/Python/traceback.c#L842), but the underlying memory-fault mechanism remains unproven. Its control retained the original installer and removed only periodic dumping; the crash did not recur. For DNA, a public HTTPS request from the same image, without credentials, failed certificate verification before installing CA certificates and returned HTTP 200 afterward. The corrected trial restored `curl`, `bash`, `git`, `python3`, and CA certificates before installing cached uv and wheel files. Neither diagnostic fault is evidence of insufficient generation budget. - -**Direct budget conclusion: increasing the cap further cannot serve as a universal fix for these failures.** - -- No run in this 65536 experiment reported `max_tokens` or reached the 3600-second agent deadline. Only Scheme (38496), regex-chess (60483), and corrected DNA (35730) actually exceeded 32768 output tokens in a reply, and all three failed. The passing compressor peaked at 32073; the other passing tasks peaked at no more than 6015. -- Pipeline, regex-chess, path-tracing, Caffe, and MTEB exhausted **50 turns** in the initial batch. This is the total call-count limit, distinct from the per-reply generation budget. -- Circuit, the QEMU retry, the scheduler control, and corrected DNA exposed another agent robustness issue: response streams ended completely with `tool_use` but supplied missing or malformed tool arguments, leaving the SDK with an empty dictionary. The native renderer indexed required fields and raised `KeyError`. Circuit's raw JSON was `{}`, QEMU/scheduler supplied `{`, and DNA supplied an unterminated string; these are not all valid empty JSON objects. Argument validation and error feedback should be addressed separately so the agent can attempt repair; that implementation was not changed in this experiment. - -These are diagnostic observations, **not a controlled comparison changing only the budget**. The 8192 baseline used source `2b83097` and native task deadlines; dependencies, sampling, cache state, and backend conditions were not fixed. The change from 8/18 to 5/18 therefore does not establish that a larger budget is harmful, and the compressor pass cannot be attributed to doubling the budget. The two preceding `regex-log` passes used 32768; it was not rerun at 65536, so these different conditions are not combined into a uniform 20-task pass rate. - -The reconciled billing lower bound for the other 18 tasks and their 5 supplemental attempts is **$0.261769566**; including compressor, all 24 attempts cost at least **$0.280588638**. Tensor-parallelism billing queries returned 404 on three checks, leaving complete total cost and token usage unknown. Interrupted Scheme/scheduler requests were priced separately, but those receipts do not repair missing native usage, and unknown values are not treated as zero. - -The [structured experiment results](../../../benchmarks/harbor/results/tb21-generation-budget-65536-20260912.json) contain configuration, source/wheel hashes, per-task results, accounting limits, and local job/trajectory paths. The 24 attempts cover 19 distinct tasks: **20 native ATIF files, 1 clearly marked post-crash reconstruction, and 3 setup failures without trajectories**. Every present ATIF validates against its schema; every native trajectory matches its full Journal projection, with no truncated Journal strings. Received HTTP streams, including interrupted streams, timelines, runtime versions, validation reports, and diagnostic scripts remain under `jobs/`. The main record directory is `tb21-remaining18-65536-3600s-trace-20260912-7a610cb-record`: `comparison.json` indexes every group, and `results.en.md` records the initial 18 tasks. Raw trajectories remain Git-ignored and unuploaded; all experiment and diagnostic containers have been cleaned up. - -#### Automatically continuing after truncation - -The current run ends on `max_tokens` even if model turns remain. The new behavior is to initiate further model calls automatically when configuration permits and sufficient budget remains, continuing the original task so headless execution does not depend on a user manually sending “continue.” - -Expected behavior and constraints: - -- Provide an enable/disable setting and a finite continuation limit. Each additional reply counts against the same run's `max_turns`; recovery must not reset the turn counter. Default enablement, continuation count, and other budget limits remain to be designed. -- Preserve successful tool results and task context while constructing valid request history. Continue skipping tools from truncated replies. Do not replay partial arguments or tool calls without matching results. Thinking-only replies without visible text also need a defined recovery method; continuation prompts and request construction remain to be designed. -- Preserve every `max_tokens` fact in the Journal/ATIF and record subsequent attempts, all usage, and all costs. Do not emit a run terminal event while continuation is still underway. Accurately record normal completion, continuation-limit exhaustion, turn exhaustion, or request failure. Terminal rules and protocol representation for recovery need to be designed together. -- Stop at configured limits, cancellation, or unrecoverable errors with explicit diagnostics, avoiding endless continuation. Continued execution still relies on the verifier to establish correctness. - -Automated acceptance must cover continuation to completion, repeated truncation reaching its limit, exhausted remaining turns, thinking-only replies, and truncated tool arguments neither executing nor being replayed incorrectly. For real-model acceptance, use these two tasks and new job names to compare continuation disabled/enabled at the same response budget, then evaluate larger budgets together with continuation. Report pass rate, additional model calls, total tokens, cost, and duration separately. Automatic continuation remains unimplemented; neither the preceding 0/2 result at 8192 nor the higher-budget experiment constitutes acceptance for automatic continuation. - -#### Crashes caused by missing tool arguments - -When a model omits a required tool argument, the agent raises a Python exception and fails the entire run instead of returning a tool error the model can correct. In this pilot, the final `edit` call in `pytorch-model-recovery` caused this failure. - -The [`edit` definition](../../../src/nanopycodeagent/edit_tool.py) requires: - -- `path`: the file to modify. -- `old_text`: the text to replace. -- `new_text`: its replacement. - -The call supplied only `old_text` and `new_text`, describing a replacement without identifying the file. The schema declares `path` required, but actual arguments still need validation before use. - -This error occurred while displaying the tool call, before any file edit. The missing target path caused even the display of the intended operation to raise `KeyError: 'path'`, ending the entire task. This happened after Harbor's timeout and cannot explain the preceding timeout. - -Both display and execution need coverage: the display must tolerate missing arguments, and execution must validate required fields. A missing field should prevent that tool from running and return a clear `tool_result` with `is_error=True`, for example: - -```text -Missing required argument: path. Provide the file path and retry. -``` - -The model can then supply the missing value in a later turn and continue within the remaining budget. Both display and execution must tolerate incomplete input; otherwise the crash is merely deferred to the next stage. The principle is to return model-correctable argument errors as tool results so the task can continue, rather than ending the entire task. Other tools should follow the same principle. - -**Implementation on 2026-09-13: tool argument validation and error feedback.** The implementation commit is `27a9503`. Before preview or execution, `read`, `write`, `edit`, and `bash` share validation of required fields, the string/integer/boolean types declared in their schemas, and NUL characters in paths and commands. Unknown tool names also return errors. Arguments are not coerced: for example, `replace_all="false"` is not treated as true, and booleans cannot substitute for read line numbers. Extra fields permitted by the schema, empty file contents, and empty replacement text remain supported. - -Invalid calls do not reach tool implementations. Instead, they receive a `tool_result` matching the original call ID, with `is_error=True` and correction guidance. The preview indicates that the call did not execute. Other valid calls in the same reply still run in order, and recovery does not repeat successful tools. The next model reply retains the task, history, and remaining turns, without a separate retry budget. Unexpected programming exceptions inside tools still propagate, avoiding misclassification of every `KeyError` as a model input error. - -The agent also checks the argument JSON actually received in the response stream. The SDK parses partial JSON during streaming; a dictionary containing required fields does not justify executing a command if its closing brace is missing. When a stream finishes with `tool_use` but incomplete argument JSON, the call is rejected with a request for a complete object. Journals and ATIF retain the error, raw argument JSON, and SDK-parsed input. Non-object inputs are saved separately as `raw_input` while preserving existing object field constraints. Follow-up requests use sendable objects instead of replaying partial JSON. Existing `max_tokens` handling remains unchanged: all tools in a truncated reply are skipped and the run ends as `response_truncated`. Automatic continuation is not implemented here. - -**Automated validation and independent review.** All 276 core tests pass with locked dependencies. The same 276 tests also pass in a temporary environment pinned to the preceding experiment's reported Anthropic SDK 1.5.0, HTTPX 0.28.1, HTTPX2 2.12.0, and Pydantic 2.13.5. All 22 Harbor adapter tests pass. New coverage checks that invalid arguments have no side effects, incomplete JSON through the real SDK, corrected calls after error feedback, valid sibling calls, interactive/headless behavior, turn limits, and trajectory evidence. An independent reviewer inspected the complete change in a new Herdr split using the `review-agent` skill and reported **No findings**, additionally verifying mixed-call recovery through the real SDK and Harbor compatibility. - -The four actual failed response streams from circuit, QEMU, scheduler, and DNA were also replayed unchanged through SDK 1.5.0 offline. Each rejected the invalid call, returned an error, and then completed one valid call from a scripted follow-up reply before finishing normally. Tool execution was stubbed and there were no paid model calls. This demonstrates changed handling of identical failed inputs; it does not establish that those four real tasks pass. - -**Known boundary.** Recovery depends on the SDK delivering the response successfully. Some JSON syntax errors, including invalid escapes and trailing commas, raise inside the SDK before recovery can run; independent review confirmed that this limitation also exists on the main branch. Interrupted streams, algorithm errors, exhausted turns, and evaluation environment failures require separate handling. - -**Real-task rerun settings.** The reruns use the same reviewed wheel from `27a9503`, with `deepseek/deepseek-v4-flash-0731` through OpenRouter. Task refs and cached image digests for all original 20 tasks match the 09-06 baseline. Every reply explicitly uses `max_tokens=65536`, with 50 turns per task and at most two simultaneous trials; the project default remains 32768. Agent execution has 3600 seconds, finalization 120 seconds, and the Harbor outer allowance 3780 seconds. Verifiers retain their native task deadlines. Each task runs once per job with no automatic Harbor retries; additional reruns have separate job names and preserve earlier failures. - -In the first targeted5 group on 09-13, QEMU encountered APT 404 errors and DNA exceeded the 1080-second installation limit, both before any model call. PyTorch was paused by the user during installation, and circuit had not started. Subsequent groups increase only the **installation deadline** to 3600 seconds and retry system dependency installation at most three times on HTTP 404. The scheduler agent in that first group had already finished normally before the pause. After resumption, its unchanged artifacts in the preserved container passed all six official tests, yielding reward 1 with **no additional model calls**. This resumed verification is recorded separately from an uninterrupted Harbor trial. - -QEMU installation in the 09-14 targeted4 group still encountered intermittent 404 errors, so a separate retry uses the same 20 packages selected by the original APT index, downloaded from official URLs and checked against their SHA256 hashes and sizes. They enter the cache after `apt-get update`, before the original dependency installation. Preloading them earlier allowed the image's APT cleanup hook to remove them; correcting that order made installation succeed in the original image. Package versions and repositories remain the same. - -In the same targeted4 group, the PyTorch agent finished normally, but the official verifier exceeded its native 900-second deadline downloading Torch/CUDA dependencies, leaving the task unscored. A uv 0.9.5 cache was prepared for a separate retry and the subsequent full20 run, using the official script's unchanged requirements: `pytest==8.4.1`, `torch==2.7.1`, and `pytest-json-ctrf==0.3.5`. After relocation into the original image, all 31 packages installed offline and Torch imported successfully. Preparation ran no task tests. Only `pytorch-model-recovery` receives this cache; its official verification script and 900-second deadline stay unchanged. This changes cache conditions and must be distinguished from the tool argument fix's effect. - -The original 8192 baseline also used another source revision and agent deadlines. The preceding 65536 initial18 group passed five tasks; later supplements and compressor were separate attempts, while `regex-log` had only been rerun at 32768. Comparisons therefore keep the original 20 tasks and the matching 18-task subset separate, without combining different settings into one pass rate. Sampling, routing, cache, and backend state remain uncontrolled. Identical-response offline replays, observed recovery events in real runs, and final task scores are three distinct forms of evidence. - -**Targeted rerun results (2026-09-14).** The table preserves each historical argument failure and its observed rerun result. Installation failures, verifier timeouts, and supplements remain separate; these different conditions are not combined into one formal five-task score. - -| Task | Historical argument crash | Rerun result | Observed argument feedback | -| --- | --- | --- | --- | -| `dna-assembly` | `bash` missing `command`, with an unterminated JSON string | Reward **0**, **0/1** official tests; `max_turns_exhausted` after 50 replies and 3533.7 seconds of agent execution | None | -| `qemu-alpine-ssh` | `bash` missing `command`, with raw JSON `{` | Installation failures retained separately; cached retry reward **1**, **1/1** tests, normal completion after 48 replies | None | -| `llm-inference-batching-scheduler` | `write` missing `content`, with raw JSON `{` | Normal completion after 47 replies before the pause; resumed official verification reward **1**, **6/6** tests, no additional model calls | **One** incomplete `bash` JSON rejection, followed by another model call and a successful `bash` call | -| `pytorch-model-recovery` | The 8192 baseline omitted `edit.path` after timeout | First agent run completed normally but verifier downloads timed out; cached retry reward **1**, **5/5** tests, normal completion after eight replies | None in either run | -| `circuit-fibsqrt` | `bash` missing `command`, with raw JSON `{}` | Reached the 3600-second execution deadline after 49 replies; stopped artifacts passed **3/3** tests with reward **1**; **not a normally completed pass** | None | - -The experimental supervisor interrupted circuit, which ended with `KeyboardInterrupt` and exit 130; Harbor recorded `NonZeroAgentExitCodeError`. Finalization retained a native trajectory without a forced kill, and verification began only after the agent stopped. Both the passing artifact and the exhausted execution budget must therefore remain visible; reward 1 alone does not establish normal completion. - -Scheduler is the only real run in this group that directly demonstrates argument error feedback followed by continued execution and passing verification. Its new error concerns `bash` JSON, rather than the historical `write.content` failure. The other newly sampled attempts produced no argument validation errors, so their passes cannot independently be attributed to this feature. The preceding offline replays provide identical-input evidence for the four historical failed responses. - -Including scheduler, the first PyTorch verifier timeout, and both cached supplements, the group retains **six native ATIF files**. All validate against Harbor, match their full Journal projections, and contain no truncated Journal strings. These six runs account for 222 model replies, 12,183,309 input tokens including 10,021,376 cached tokens, 427,835 output tokens, and **$0.243005564** in model costs, with complete usage and billing. Pure installation failures, model execution that had not started at the pause, and offline replays incurred no model charges. The cached PyTorch retry spent 432.8 seconds installing, 140.0 seconds executing the agent, and 23.2 seconds verifying. This reflects the changed cache preparation; it does not show that agent code accelerated the preceding 900-second verifier download. - -**Full 20-task rerun (2026-09-14–15).** The independent full group finished with -**10/20 passes (50%), nine zeros, and one unscored task**. **Nine tasks completed -normally and passed (45%)**. The original 8192 pilot passed 8/20 (40%), with seven -passing trials without a Harbor exception. Circuit's artifacts passed here, but -cost reconciliation reached the execution deadline, so it is not a normally -completed pass. “No Harbor exception” is also not identical to the new definition, -which requires a native `completed` outcome, no error, and no execution deadline. -[Structured results](../../../benchmarks/harbor/results/tb21-tool-input-recovery-20260914.json) -preserve separate attempts, configuration, billing completeness, and validation evidence. - -| Experiment | Tasks | Reward 1 | Reward 0 | Unscored | -| --- | --- | --- | --- | --- | -| Original 8192 pilot | 20 | 8 | 12 | 0 | -| Previous 65536 initial18 | 18 | 5 | 8 | 5 | -| Fresh matching18 subset | 18 | 8 | 9 | 1 | -| Fresh independent full group | 20 | 10 | 9 | 1 | - -Seven of the matching18 subset's eight passes completed normally. Table values are -rewards. “Outside this group” means the previous initial18 did not include the task, -rather than an unscored attempt. Compressor already passed a separate previous -65536 supplement, while regex-log passed a 32768 supplement; neither belongs in -the old initial18 score. - -| Task | Original 8192 | Previous 65536 initial18 | Fresh 65536 | Fresh result details | -| --- | --- | --- | --- | --- | -| `write-compressor` | 0 | Outside this group | 1 | Normal completion; 3/3 | -| `torch-tensor-parallelism` | 0 | Unscored | 0 | Normal completion; 1/3 | -| `schemelike-metacircular-eval` | 0 | 0 | 0 | Response stream interrupted in request 18 | -| `kv-store-grpc` | 1 | 0 | 1 | Normal completion; 7/7 | -| `pypi-server` | 1 | 1 | 1 | Normal completion; 1/1 | -| `dna-assembly` | 0 | Unscored | 0 | Provider unavailable in request 2 | -| `torch-pipeline-parallelism` | 0 | Unscored | 0 | 50 turns exhausted; 2/3 | -| `qemu-alpine-ssh` | 1 | Unscored | 1 | Normal completion; 1/1 | -| `openssl-selfsigned-cert` | 1 | 1 | 1 | Normal completion; 6/6 | -| `regex-chess` | 0 | 0 | 0 | 50 turns exhausted; 1/4 | -| `log-summary-date-ranges` | 1 | 1 | 1 | Normal completion; 2/2 | -| `model-extraction-relu-logits` | 0 | Unscored | Unscored | Agent completed; verifier exceeded 900 seconds | -| `path-tracing` | 0 | 0 | 0 | Provider unavailable in request 32 | -| `regex-log` | 0 | Outside this group | 1 | Normal completion; 1/1 | -| `caffe-cifar-10` | 0 | 0 | 0 | Normal completion; 2/6 | -| `mteb-leaderboard` | 0 | 0 | 0 | 50 turns exhausted; 0/2 | -| `llm-inference-batching-scheduler` | 1 | 0 | 0 | Response stream interrupted in request 3 | -| `pytorch-model-recovery` | 1 | 1 | 1 | Normal completion; 5/5 | -| `circuit-fibsqrt` | 0 | 0 | 1 | 3/3; cost reconciliation interrupted, trajectory reconstructed | -| `merge-diff-arc-agi-task` | 1 | 1 | 1 | Normal completion; 5/5 | - -Relative to the original pilot, compressor, regex-log, and circuit gained passes, -while scheduler changed from a pass to zero: a net gain of two tasks. Relative to -the previous 65536 initial18, KV-store changed from zero to a pass, QEMU from -unscored to a pass, and circuit from zero to passing artifacts with a finalization -failure. KV-store and QEMU already passed the original 8192 pilot, and compressor -and regex-log passed their earlier budget supplements. These score changes -therefore do not establish an independent benefit from argument validation. - -The fresh full group recorded **no `ToolInputError` events**. The four offline -replays still provide identical-input evidence, while the targeted scheduler -provides observed live error feedback followed by continued execution and passing -verification. The fresh scheduler failed with `RemoteProtocolError` in request -three, after two completed replies. Its targeted success must not replace this -separate failed attempt. - -Four requests did not return complete responses: Scheme and scheduler lost their -response streams; DNA and path-tracing received `provider_unavailable` errors -referring to SIGTERM and a graceful shutdown timeout. These are transport or -upstream availability failures. Pipeline, regex-chess, and MTEB exhausted fifty -turns; tensor and Caffe completed normally without passing all functional tests. -Model extraction completed twenty-seven replies, installed dependencies, and -entered its single pytest test before exceeding the native 900-second verifier -limit. It remains unscored. This was not a dependency download timeout, and its -deadline was not extended to replace the result. - -**Circuit finalization failure.** Reply fifty finished with `end_turn` at -18:46:26.965 UTC, followed by provider cost reconciliation. The supervisor enforced -the 3600-second execution deadline at 18:47:05.171, interrupting reconciliation -before either `run.completed` or `run.failed` had been written. Native ATIF export -raised `AtifProjectionError: Event Journal has no terminal event`. The experiment -wrapper preserved the original Journal and reconstructed a trajectory with an -explicitly marked synthetic terminal event; it does not claim native export. -Official verification began after the agent stopped and passed all three tests. -This cost-finalization control block is unchanged from the implementation commit's -parent and was not modified here. A follow-up needs to ensure that interrupted -billing reconciliation still records a terminal state and exports a trajectory. -A separate `Gate`-object `KeyError` appeared in model-generated program output and -was returned as a tool result; it was not an agent crash from missing tool arguments. - -Late in circuit, three detailed monitoring probes each exceeded twenty-five -seconds. A read-only check found the container still running at about 1.983/2 GiB -of memory, with Docker not reporting an OOM. Detailed in-container probes were -then stopped in favor of host result files and container state. Agent, verifier, -and resource limits stayed unchanged; no orphaned probe was found. The earlier -probes' contribution to memory pressure is unknown, so this monitoring change -remains an experimental limitation. - -**Trajectories and costs.** All twenty ATIF files validate against Harbor: nineteen -are native exports matching their full Journal projections, and one is the -explicit reconstruction above. No Journal strings were truncated. There were -491 model calls started and 487 complete replies. Fully recorded replies account -for **21,991,162 input tokens**, including **18,119,936 cached tokens**, and -**724,013 output tokens**. Native usage for four interrupted responses remains -incomplete; missing values must not be treated as zero. Per-reply output maxima -also cover completed replies only and do not describe an interrupted response's -actual output. QEMU additionally had one pre-connection `ConnectTimeout` followed -by an SDK retry: forty-one model replies and forty-two HTTP attempts. - -All model costs are covered by native records or separate provider receipts. -Supplemental accounting distinguishes completed replies from interrupted requests -and subtracts costs already recorded in the Journal, avoiding double-counting -when a reconstructed trajectory lacks the native terminal event. The full twenty -tasks cost **$0.462175819**. Including targeted attempts, cached supplements, and -the scheduler whose verification resumed after the pause, total experiment model -cost is **$0.705181383**. Complete billing does not imply complete native responses -or usage. - -#### Full 20-task retest of stream recovery - -**Run on 2026-09-21 to 22.** On commit -`577a1ab7f96fc3d220ca89c068988a479ac7b1aa` of the `fix/stream-recovery` branch, we -reran the original 20 tasks to check whether real model responses were interrupted -and whether an interruption could recover within the same run. The run started at -22:06 Beijing time on 09-21 and ended at 01:52 on 09-22, taking about **3 hours -46 minutes**; no transport faults were injected. - -**Configuration and recording.** Harbor 0.21.0 and Terminal-Bench 2.1, keeping the -original 20 task refs and verifying the cached image digests before the run. The -model was still `deepseek/deepseek-v4-flash-0731` on OpenRouter. Containers -installed the wheel built from this branch, and every Python source file in the -package was checked against the current commit; the running version was -`0.8.1.dev28+g577a1ab7f`. Dependencies were pinned to Anthropic SDK 1.5.0, HTTPX -0.28.1, HTTPX2 2.12.0, and Pydantic 2.13.5. - -Each reply was capped at 65536 tokens, with at most 50 complete replies per task -and concurrency 2; each task was attempted once, with Harbor whole-task automatic -retries set to 0. The agent was capped at 3600 seconds, with 120 seconds of -finalization grace and a 3780-second outer Harbor limit, a 3600-second install -cap, and the official verifier left at its original limit. We reused the earlier -dependency cache and passive HTTP recorder; the container did only lightweight -event counting, and full Journal / HTTP records were collected to the host for -analysis. The live counter was corrected once to accept JSON without spaces and -restarted separately; the evaluation was not restarted and the agent, tasks, and -budget were not changed. - -| Metric | This run | -| --- | --- | -| Finished trials | 20/20 | -| Actually entered agent execution | 18 tasks; the other 2 failed during install and made no model calls | -| Official scores | reward 1: **11 tasks (55%)**; reward 0: **6 tasks**; unscored: **3 tasks** | -| Completed normally and passed | **8 tasks (40%)**: native terminal state `completed`, no Harbor exception, execution limit not reached | -| Journal model calls | **576 started, 576 fully returned** | -| `model.failed` / interrupted HTTP response bodies | **0 / 0** | -| Stream recovery triggered / succeeded | **0 / 0**; the recovery branch was not exercised this run | - -Per-task results follow. "Turns exhausted" means the native terminal state was -`max_turns_exhausted`; whether the official verifier judges the artifact as passing -is a separate matter. - -| Task | Reward | Execution or verification result | -| --- | --- | --- | -| `write-compressor` | 1 | Completed normally and passed | -| `torch-tensor-parallelism` | Unscored | Agent completed normally; verifier exceeded the original 900-second limit | -| `schemelike-metacircular-eval` | 1 | 50 turns exhausted; artifact passed | -| `kv-store-grpc` | 0 | Agent completed normally, artifact failed | -| `pypi-server` | 1 | Completed normally and passed | -| `dna-assembly` | 0 | Agent completed normally, artifact failed | -| `torch-pipeline-parallelism` | Unscored | Install used Python 3.12.3, below the project's required Python >=3.13 | -| `qemu-alpine-ssh` | Unscored | Install downloaded Python 3.14.0 and still received HTTP 504 after three retries | -| `openssl-selfsigned-cert` | 1 | Completed normally and passed | -| `regex-chess` | 1 | 50 turns exhausted; artifact passed | -| `log-summary-date-ranges` | 0 | Agent completed normally, artifact failed | -| `model-extraction-relu-logits` | 1 | Completed normally and passed | -| `path-tracing` | 1 | Completed normally and passed | -| `regex-log` | 1 | Completed normally and passed | -| `caffe-cifar-10` | 0 | 50 turns exhausted, artifact failed | -| `mteb-leaderboard` | 0 | 50 turns exhausted, artifact failed | -| `llm-inference-batching-scheduler` | 1 | 50 turns exhausted; artifact passed | -| `pytorch-model-recovery` | 1 | Completed normally and passed | -| `circuit-fibsqrt` | 0 | 50 turns exhausted, artifact failed | -| `merge-diff-arc-agi-task` | 1 | Completed normally and passed | - -Both install failures were recorded by Harbor as `NonZeroAgentExitCodeError` and -happened before the agent started; the tensor exception was -`VerifierTimeoutError`. These are not model stream interruptions, and the 20 -finished trials must not be written as "all 20 tasks completed model execution". -Likewise, Scheme, regex-chess, and scheduler among the 11 reward-1 results passed -after exhausting turns and do not count toward the 8 completed-and-passed. - -**Evidence boundary for recovery.** All 576 model calls this run returned -completely, so no real stream interruption was observed; we can only record "no -stream interruption occurred this run", and cannot claim "recovery succeeded after -a live interruption". At the start of this run we separately ran -`tests/test_stream_recovery.py`, which reported **17 passed, 0 failed, 0 skipped**, -covering both HTTP exception families, retry exhaustion and the time window, -discarding tool calls from an interrupted reply, and preserving existing tool -results. These automated tests validate the recovery mechanism but cannot replace -a real interruption sample. Sampling, routing, caching, and server state were -uncontrolled; the score change from the earlier 20 tasks also cannot be attributed -to stream recovery. - -The local job is `jobs/tb21-stream-recovery-pilot20-20260921/` and the experiment -record directory is `jobs/tb21-stream-recovery-pilot20-20260921-record/`. There, -`manifest.json` stores source, wheel, task, and image information; `summary.json` -stores per-task statistics; `report.md` is the result summary; and -`stream-recovery-tests.json` stores the dedicated test result. Raw Journals, HTTP -records, trajectories, and verifier output are kept in each trial directory; these -raw data live in the Git-ignored `jobs/`. - -#### Stopping execution on timeout - -Timeout handling must ensure that the agent inside the container actually stops before verification begins. In this pilot, Harbor had already classified the task as timed out while the agent continued calling the model. - -The affected task, `pytorch-model-recovery`, has a 900-second (15-minute) agent limit. Relative to agent execution start: - -| Time | Observed event | -| --- | --- | -| About 900 sec | Harbor records `AgentTimeoutError` | -| About 901 sec | The verifier begins | -| About 929 and 939 sec | The agent starts two more model calls | -| About 1063 sec | Missing `path` in `edit` causes failure and `run.failed` | - -Harbor's timeout did not promptly stop container execution, with two consequences: - -- During verification, the agent could continue modifying files under examination, weakening result reliability. Reward 1 therefore does not make this a normally completed success sample. -- New model calls and costs occurred after the deadline, so the time boundary was not enforced. - -Proposed goals: - -1. Stop agent execution at the deadline and prevent further model or tool calls. -2. Terminate tool subprocesses that need cleanup so commands cannot keep modifying files after the agent exits. -3. Confirm execution has stopped, then start verification and record timeout state accurately. - -Continued execution after timeout is confirmed. Which cancellation or cleanup layer failed remains to be investigated. The goal is to enforce the time limit; extending the 900-second allowance would not fix execution continuing after timeout. - -#### Trajectory collection - -Trajectory collection must preserve the structured execution record and make it available to Harbor in time to aggregate steps, tokens, and costs. In this pilot, the file eventually existed but was unavailable when Harbor collected metrics. - -Normally, the agent continuously records model replies, tool calls, results, and usage, then exports those records as ATIF `trajectory.json` when execution ends. Harbor subsequently reads and validates the trajectory to aggregate steps, tokens, and costs. This creates three stages that must finish in order: the agent stops, the trajectory is saved, and the evaluator collects it. - -For `pytorch-model-recovery`, collection had a timing problem: - -1. Harbor began collection after timeout, found no trajectory, and recorded it as `missing`. -2. The agent continued running and only wrote valid ATIF during later finalization. -3. The file eventually appeared in the result directory, but original Harbor token metrics remained `null` and were not backfilled automatically. - -All 20 final ATIF files therefore validate, but only 19/20 original Harbor metrics match them. A file's eventual existence and validity do not establish successful collection at aggregation time. - -The proposed goal is to preserve as much execution history as possible up to stopping, whether the run completes, fails, or times out, and let Harbor read it before aggregation. Incomplete records must be labeled explicitly. - -Design this together with timeout termination: stop execution, finalize within a bounded time, then collect and aggregate. Trajectory export currently depends on the agent completing finalization. Forcibly terminating the process may leave no time to save it. Periodic snapshots or reconstruction from persisted journals therefore need consideration. The specific approach is undecided; the objective is consistency among actual execution, saved trajectories, and Harbor metrics. - -#### Improving delayed cost reconciliation - -Costs unavailable at task completion should remain queryable later so accounting can be completed. - -A model response may lack actual cost, and the billing endpoint may not yet return an amount. Save the call's generation ID as the association key and query its `total_cost` later. Temporary unavailability means pending or unknown cost, not free execution. - -During finalization, the agent already queries calls with generation IDs and missing costs individually. Each lookup defaults to six attempts, separated by `1, 2, 4, 8, 15` seconds. These delays total 30 seconds, plus request durations; 30 seconds is not a wall-clock cap on the complete reconciliation process. Exhausting the attempts preserves an incomplete-cost state. Finalization reconciliation is already a supported agent feature, while an independent later-reconciliation interface is not yet provided. - -In the pilot, 13 original trajectories had incomplete costs, with 18 calls unresolved during finalization. During and after the 2026-09-06 batch run, an independent script queried the same endpoint in batches and eventually obtained all 18 costs. - -**Those independent lookups used a temporary script that is not part of the supported workflow.** The script is `jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`, under Git-ignored `jobs/`, archived as a one-off workflow record. It must be run separately and is not integrated into the official CLI, Harbor adapter, or a standard evaluation post-processing stage. Completing costs through that script does not mean the agent automatically performs later lookups. - -| Accounting basis | Cost (USD) | -| --- | ---: | -| Known cost subtotal in final trajectories | 0.11053603 | -| Complete cost after independent reconciliation | 0.119200332 | - -The reconciled total covers all 267 recorded model replies. This shows that bounded finalization attempts can leave costs requiring later lookup; the known subtotal at that time is not a complete total. - -The proposed direction is to turn this one-off follow-up into a reusable supported capability that integrates with evaluation and can run independently after tasks finish. Read unresolved generation IDs, query later, preserve receipts, and produce updated aggregates. Preserve original results, avoid counting any charge twice, and distinguish known subtotals from complete totals. Unresolved amounts remain unknown rather than zero, and reconciliation failures must not change task scores. - -Whether this becomes a separate command or an automatic post-benchmark stage is undecided. The goal is to continue reconciliation beyond task finalization without delaying agent exit indefinitely while waiting for billing records. - -### Re-running the six failed tasks with a different model - -**Run on 2026-09-25.** In the previous stream-recovery retest, six of the twenty tasks scored reward 0 under `deepseek/deepseek-v4-flash-0731`: `kv-store-grpc`, `dna-assembly`, `log-summary-date-ranges`, `caffe-cifar-10`, `mteb-leaderboard`, and `circuit-fibsqrt`. To judge whether those failures were model-limited, this round re-ran exactly those tasks under the same conditions, changing only the model. This is not a controlled experiment: each task ran once, and routing, sampling, cache, and provider backend were not held fixed. - -**Model choice.** The goal was to switch to a DeepSeek Flash model. OpenRouter has no `deepseek-flash` slug, and aliases such as `deepseek/deepseek-v4-flash-latest` are rejected as invalid model IDs by the Anthropic Messages endpoint. The run therefore used **`deepseek/deepseek-v4.1-flash`** (V4.1 Flash), after a single minimal Messages request confirmed it works and supports tool use. An earlier attempt with `deepseek/deepseek-v4-flash` (V4 Flash 0423) was aborted before it finished; its artifacts are kept under `jobs/tb21-v4flash-6task-20260925/` and are not treated as an experiment result. Only the V4.1 Flash round is recorded here. - -**Fixed conditions.** - -| Item | This round | -| --- | --- | -| Job | `tb21-v41flash-6task-20260925` | -| Agent source | `main` commit `1386df48ef9c84d7b65c33fcf0f903fad7036a79`; wheel `0.8.1.dev30+g1386df48e` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| Dataset | `terminal-bench/terminal-bench-2-1`, `sha256:7d7bdc1c…a0699a` | -| Tasks | The six above, each with its pinned task ref | -| Generation / turns | At most 65536 tokens per reply; at most 50 complete replies per task | -| Deadlines | Agent 3600 s, finalization grace 120 s, Harbor outer limit 3780 s, setup 3600 s; verifiers keep their native task deadlines | -| Concurrency / retries | Concurrency 2; one attempt per task, zero automatic Harbor retries | -| Dependencies | Anthropic SDK 1.5.0, HTTPX 0.28.1, HTTPX2 2.12.0, Pydantic 2.13.5 | -| Images | Reused the cached baseline images, with RepoDigests checked before the run | - -**Results.** All six tasks finished and all received an official score: **four passed and two failed**. - -| Metric | This round | -| --- | --- | -| Passed / failed / unscored | **4 / 2 / 0** | -| Journal model calls | **206 started, 206 returned complete** | -| `model.failed` / HTTP response-body interruptions | **0 / 0** | -| Input / cache / output tokens | 8,122,046 / 7,444,992 / 219,443 | -| Model cost | **$0.278759041** (all per-call receipts complete) | -| Infrastructure exceptions | 0 | - -| Task | Reward | Outcome | Model replies | Cost (USD) | -| --- | ---: | --- | ---: | ---: | -| `kv-store-grpc` | 1 | `completed` | 17 | 0.008440065 | -| `dna-assembly` | 1 | `completed` | 34 | 0.069021966 | -| `log-summary-date-ranges` | 1 | `completed` | 6 | 0.005224672 | -| `circuit-fibsqrt` | 1 | `completed` | 49 | 0.080010864 | -| `caffe-cifar-10` | 0 | `max_turns_exhausted` | 50 | 0.065742942 | -| `mteb-leaderboard` | 0 | `max_turns_exhausted` | 50 | 0.050318532 | - -**The two remaining failures were both turn exhaustion, not crashes or transport faults.** - -- `caffe-cifar-10`: CPU training was too slow; within 50 turns it reached only `Iteration 200`, so `cifar10_quick_iter_500.caffemodel` and `Iteration 500` never existed. The official suite passed 2 of 6 tests and failed 3 (one more passed). The bottleneck is the turn limit combined with training time. -- `mteb-leaderboard`: `/app/result.txt` was never produced and both official tests failed. The model kept reading MTEB source and ran out of turns without writing the result file. Every historical run of this task has failed the same way. - -**Comparison with history.** All six tasks scored reward 0 in the 2026-09-21 run under 0731. Under V4.1 Flash: - -- `kv-store-grpc` and `log-summary-date-ranges` passed again (they had also passed in the earlier 8192 and 09-14/15 runs, so this is a recovery rather than a first pass). -- `dna-assembly` **passed for the first time**, with a clean `completed`; every prior attempt was either 0 or unscored. -- `circuit-fibsqrt` **passed cleanly for the first time**; its two earlier reward-1 results were both accompanied by an execution limit or trajectory reconstruction anomaly. -- `caffe-cifar-10` and `mteb-leaderboard` still failed, and have never passed. - -**Evidence boundary.** Routing, sampling, cache, and backend were not controlled while the model changed, and each task ran only once, so **0/6 → 4/6 cannot be attributed to the model difference**; it is only a signal to verify later. - -**Artifacts.** Raw trials are under `jobs/tb21-v41flash-6task-20260925/`, with the experiment record under `jobs/tb21-v41flash-6task-20260925-record/` (`manifest.json`, `summary.json`, `report.md`); these live in Git-ignored `jobs/` and were not uploaded. Version control stores the structured result [`tb21-v41flash-6task-20260925.json`](../../../benchmarks/harbor/results/tb21-v41flash-6task-20260925.json) and this summary. - -### Experiment 1: raising max_turns to 100 - -**Run on 2026-09-26.** In the previous run (`tb21-v41flash-6task-20260925`), both -`caffe-cifar-10` and `mteb-leaderboard` exhausted 50 turns -(`max_turns_exhausted`). The hard constraint Terminal-Bench / Harbor imposes on an -agent is **wall-clock time** (this task's native `[agent].timeout_sec = 3600`); it -says nothing about a turn limit — the 50 turns are nanoPyCodeAgent's own cost -guardrail (`adapter.py:18`, `agent.py:82`). So this run changes one thing only: -**`max_turns` goes from 50 to 100, everything else stays the same**. To keep the -run compliant, the Harbor-level `override_timeout_sec` and -`override_setup_timeout_sec` were **removed**, so the task-native limits apply; the -agent self-limits to 3480s inside the task's 3600s and reserves 120s to finalize. - -| Item | This run | -| --- | --- | -| Job | `tb21-exp1-turn100-20260926` | -| Agent source | branch `experiment/max-turns-and-time-budget`, commit `41476d451`; wheel `0.8.1.dev32+g41476d451` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| Dataset | `terminal-bench/terminal-bench-2-1`, `sha256:7d7bdc1c…a0699a` | -| Tasks | `caffe-cifar-10`, `mteb-leaderboard`, pinned task refs | -| Generation / turns | at most 65536 tokens per reply; at most **100** replies per task | -| Time limits | task-native agent 3600s; the agent self-limits to 3480s + 120s finalization; **no Harbor timeout overridden** | -| Concurrency / retries | concurrency 2; one attempt per task; zero automatic Harbor retries | - -**Result: both tasks passed, but they failed differently.** - -| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | -| --- | ---: | --- | ---: | ---: | ---: | -| `mteb-leaderboard` | 1 | `completed` | 70 | 1666 s | 0.054191917 | -| `caffe-cifar-10` | 1 | `DiagnosticDeadlineExceeded` | 69 | 3600 s | 0.039895559 | - -- `mteb-leaderboard`: **passed for the first time**, and cleanly — it wrote - `/app/result.txt` on turn 70 and stopped on its own. That confirms the previous - failure was **not a capability problem but the self-imposed 50-turn cap**: it had - already computed the right answer by turn 39, was still re-verifying at turn 50, - and finally converged before turn 100. -- `caffe-cifar-10`: all six official tests passed, but the agent did not wind down - on its own — after `Iteration 500` finished training and it had tested the - trained model, it went on to build pycaffe (`make py`) and install - `python3-skimage` and other extras until it hit the 3480s self-limit and the - 3600s hard limit and was killed (`deadline_reached=true`, - `forced_kill=true`). **The reward came from artifacts already on disk at the - timeout**, not from the agent finishing; the trajectory could only be - reconstructed by the supervisor from the journal - (`trajectory_source=reconstructed`). - -**Conclusion.** These two tasks show two sides of the same judgment: - -1. Raising the turn cap to 100 did let `mteb-leaderboard` pass — **the self-imposed - turn cap was the binding constraint**. -2. But `caffe-cifar-10` shows that **raising the cap alone does not teach the agent - to stop**: it keeps working until it is killed externally. That is exactly what - the time budget, budget injection, and stop-on-deadline (Experiment 2) are meant - to address. - -**Evidence boundary.** One trial per task, with routing, sampling, cache, and -backend uncontrolled; this is a targeted signal, not a controlled estimate of -pass-rate improvement. - -**Artifacts.** Raw trials are under `jobs/tb21-exp1-turn100-20260926/`, with the -experiment record under `jobs/tb21-exp1-turn100-20260926-record/`; these live in -Git-ignored `jobs/` and were not uploaded. Version control stores the structured -result -[`tb21-exp1-turn100-20260926.json`](../../../benchmarks/harbor/results/tb21-exp1-turn100-20260926.json) -and this summary. - -### Experiment 2: time budget, budget injection, and stop-on-deadline - -**Run on 2026-09-26.** Experiment 1 showed that raising max_turns lets both tasks -pass, but `caffe-cifar-10` was still killed externally (`forced_kill=true`, the -trajectory reconstructed). This run adds three things on top of Experiment 1 and -changes nothing else: - -- **Time budget**: a new `--time-budget-seconds` (Harbor kwarg - `time_budget_seconds`). This run uses 3420s, inside the task-native 3600s and - before the supervisor's 3480s self-limit. -- **Budget injection**: the system prompt carries the remaining time each turn, - and inside the finalization window (`max(15% of the budget, 180s)`) it - escalates to "stop investigating, write the output, then finish". -- **Stop-on-deadline**: when the budget runs out the run returns a new - `time_budget_exhausted` outcome, stopping before the next model call or tool - execution, and records it in the Journal and ATIF. - -| Item | This run | -| --- | --- | -| Job | `tb21-exp2-timebudget-20260926` | -| Agent source | branch `experiment/max-turns-and-time-budget`, commit `eb77fd578`; wheel `0.8.1.dev34+geb77fd578` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| Time limits | task-native agent 3600s; agent budget 3420s; supervisor self-limits to 3480s + 120s finalization; **no Harbor timeout overridden** | -| Everything else | at most 100 replies per task, at most 65536 tokens per reply, concurrency 2, one attempt, zero retries — same as Experiment 1 | - -**Result: both tasks passed, and this time both wound down on their own.** - -| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | -| --- | ---: | --- | ---: | ---: | ---: | -| `mteb-leaderboard` | 1 | `completed` | 50 | 865 s | 0.481624230 | -| `caffe-cifar-10` | 1 | `completed` | 61 | 2657 s | 0.374407898 | - -- `mteb-leaderboard`: finished in 50 replies and 865s, **twice as fast** as - Experiment 1's 70 replies and 1666s; its final message names the answer - `GritLM/GritLM-7B` and states it wrote `/app/result.txt`. -- `caffe-cifar-10`: 61 replies and 2657s, **stopping on its own** — the supervisor - reports `deadline_reached=false`, `forced_kill=false`, - `trajectory_source=native`, in contrast to Experiment 1, where it kept building - pycaffe after training, was force-killed, and its trajectory had to be - reconstructed. Its final message is "All requirements are met", with exactly 500 - training iterations and 55.17% test accuracy. - -**The two experiments side by side (two tasks each, one trial each):** - -| Metric | Experiment 1 (max_turns=100) | Experiment 2 (+ 3420s budget) | -| --- | ---: | ---: | -| Passed | 2 / 2 | 2 / 2 | -| Clean `completed` | 1 / 2 | **2 / 2** | -| Deadline hit / forced kill | 1 (caffe) | **0** | -| Trajectory reconstructed | 1 (caffe) | **0** | -| Total model calls | 139 | 111 | -| Input tokens | 340,804 | 2,919,588 | -| Cache tokens | 4,289,280 | 1,408 | -| Total cost (USD) | 0.094 | **0.856** | - -**Finding 1: the time budget makes the agent finish on its own.** Both tasks -stopped before the budget ran out, with `deadline_reached` and `forced_kill` both -false and native trajectories. This is exactly the "does the work but will not -stop" gap that Experiment 1 exposed. - -**Finding 2: rewriting the system prompt every turn breaks the prefix cache; the -total cost rose, but routing confounds it.** On the surface, Experiment 2 used -about 8.6× the input tokens of Experiment 1 (2.92M vs 0.34M), cache tokens fell -from 4.29M to 1.4K, and total cost rose from $0.094 to $0.856. Split by provider, -there are two causes: - -1. **The system prompt changes every turn, so the prefix cache misses.** DeepInfra - is the only provider that reports cache reads consistently in both runs. In - Experiment 1 its 121 calls averaged 29,116 cached tokens each; in Experiment 2 - its 23 calls averaged 61. On the same provider the prefix cache essentially - vanished, consistent with a system prompt that invalidates the - longest-common-prefix cache every turn. -2. **Routing shifted (uncontrolled).** Experiment 1 sent 121 of 139 calls to - DeepInfra; Experiment 2 spread its 111 calls across Together (48), Parasail - (29), DeepInfra (23), and Relace (10). Together, Parasail, and Relace reported - no cache reads in either run. So the 8.6× cost increase **cannot be attributed - to the injection alone**; the routing change is a confounder on top of it. - -Either way the fix is the same: **keep the system prompt fixed and append the -budget note as a user/tool message at the tail** (a tail change does not -invalidate the prefix), or inject only when crossing a threshold. This run -records the flaw as-is; the fix is left for the next round. - -**Evidence boundary.** Still one trial per task, with routing, sampling, cache, -and backend uncontrolled; "faster and cleaner" is a targeted signal, not a -controlled estimate, and run-to-run variance cannot be ruled out. The only thing -directly attributable is the mechanism itself: `deadline_reached=false`, -`forced_kill=false`, and `trajectory_source=native` are direct evidence that the -agent stopped itself. - -**Artifacts.** Raw trials are under `jobs/tb21-exp2-timebudget-20260926/`, with the -experiment record under `jobs/tb21-exp2-timebudget-20260926-record/`; these live -in Git-ignored `jobs/` and were not uploaded. Version control stores the -structured result -[`tb21-exp2-timebudget-20260926.json`](../../../benchmarks/harbor/results/tb21-exp2-timebudget-20260926.json) -and this summary. - -### Experiment 3: moving the budget reminder to the conversation tail - -**Run on 2026-09-27.** Experiment 2 found that rewriting the system prompt every -turn defeated the provider's prefix cache. The only change in this run is that the -**system prompt stays fixed and the budget reminder is appended to the tail of the -conversation** (after the task text on the first turn, after the tool results -afterwards); everything else matches Experiment 2 exactly (max_turns 100, a 3420s -budget, no Harbor timeout overrides). - -| Item | This run | -| --- | --- | -| Job | `tb21-exp3-timebudget-tail-20260926` | -| Agent source | branch `experiment/max-turns-and-time-budget`, commit `83e5c064d`; wheel `0.8.1.dev37+g83e5c064d` | -| Everything else | same as Experiment 2: 3420s budget, at most 100 replies per task, no timeout override | - -**Result: both tasks passed.** - -| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | -| --- | ---: | --- | ---: | ---: | ---: | -| `caffe-cifar-10` | 1 | `completed` | 59 | 3333 s | 0.048236604 | -| `mteb-leaderboard` | 1 | `max_turns_exhausted` | 100 | 1365 s | 0.138716976 | - -**The caching fix is verified.** The two runs side by side (two tasks each): - -| Metric | Experiment 2 (system injection) | Experiment 3 (tail injection) | -| --- | ---: | ---: | -| Input tokens | 2,919,588 | **108,646** | -| Cache tokens | 1,408 | **8,410,880** | -| Total cost (USD) | 0.856 | **0.187** | - -The clearest evidence is the same-provider before/after: in Experiment 2 Parasail's -29 calls reported zero cache reads; in Experiment 3 Parasail served 157 of 159 -replies with about **53,572** cached tokens each. Going from zero to fifty-odd -thousand on the same provider is consistent with "fixed system prompt plus prefix -cache". - -**But convergence got weaker.** Experiment 2's `mteb-leaderboard` stopped on its -own at 50 replies (`completed`); in Experiment 3 it wrote the correct -`/app/result.txt` (reward still 1) but kept working to the 100-reply cap -(`max_turns_exhausted`) and never produced a closing summary. Either the tail -reminder is less salient than a system-prompt instruction, or this is run-to-run -noise — with one trial per run, this run cannot tell the two apart. - -**Code review and fixes.** An independent codex `/review` (against `main`) audited -this branch's implementation and raised three issues, now fixed in the next -section: - -- **[P1] The deadline was checked once per batch**: when a reply contains several - tool calls, the rest still run after the first exhausts the budget. Now the - deadline is **rechecked before every tool**. -- **[P2] Injected reminders were not persisted**: the budget reminder is - behavior-changing input but appeared in neither the Event Journal nor ATIF. A new - `input.injected` event and matching ATIF step were added. -- **[P2] The new outcome was not versioned**: a run ending in - `time_budget_exhausted` still claimed schema v3, which old v3 readers reject, - while the updated reader wrongly accepted it in historical v1/v2 records. - Following the `response_truncated` precedent, the **schema is bumped to v4**. - -**Evidence boundary.** Still one trial per task, with routing uncontrolled -(Experiment 3 sent 157 of 159 calls to Parasail). The cache difference has a clear -direction, but "weaker convergence" is only a signal. - -**Artifacts.** Raw trials are under `jobs/tb21-exp3-timebudget-tail-20260926/`, with -the experiment record under `jobs/tb21-exp3-timebudget-tail-20260926-record/`. -Version control stores the structured result -[`tb21-exp3-timebudget-tail-20260926.json`](../../../benchmarks/harbor/results/tb21-exp3-timebudget-tail-20260926.json) -and this summary. diff --git a/docs/dev_notes/en/0.9.x.md b/docs/dev_notes/en/0.9.x.md new file mode 100644 index 0000000..a884494 --- /dev/null +++ b/docs/dev_notes/en/0.9.x.md @@ -0,0 +1,1076 @@ +# Development Notes — 0.9.x + +> Generated from the [Chinese source](../zh-CN/0.9.x.md), which is the source of truth. + +## 0.9.0 - 2026.10.05 + +### Running Terminal-Bench in batches and establishing a baseline + +Single-task acceptance established the task-container, agent, trajectory, cost-reconciliation, and verifier workflow. Next, expand to 20 tasks to observe failures and actual consumption before setting a budget for all 89 tasks. This pilot's pass rate is not the full Terminal-Bench 2.1 score. + +#### Fixed experimental configuration + +Prepared on 2026-09-06, this run fixes the following conditions. Later harness comparisons should reuse tasks and limits while recording each new agent revision. + +| Item | Configuration | +| --- | --- | +| Harbor | 0.21.0, using `benchmarks/harbor/uv.lock` | +| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | +| Dataset | `terminal-bench/terminal-bench-2-1`, 89 tasks | +| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | +| API endpoint | `https://openrouter.ai/api`, using Anthropic Messages | +| Provider routing | No request-level provider override; account routing settings are also experimental conditions | +| Pilot size | First 20 dataset entries, one attempt each | +| Environment | Local Docker, concurrency 2 | +| Agent limit | At most 50 model replies per task | +| Per-response limit | `MAX_TOKENS = 8192`; no explicit reasoning effort or temperature | +| Harbor retries | 0; Anthropic SDK internal request retries remain enabled | +| Timeouts and resources | Preserve task defaults | + +Harbor 0.21.0's `--n-tasks 20` selects the first 20 entries after filtering, rather than sampling randomly. Pinning the dataset hash fixes task versions, but the actual selection should still be saved and later selected with repeated `--include-task-name` arguments. OpenRouter provider routing and server behavior can change, so a pinned model slug does not make the experiment fully deterministic. + +Host Harbor uses a lockfile. Inside containers, `uv tool install` pins the agent revision but leaves dependency resolution unlocked. Public results retain Docker version and task-image RepoDigests for checking later environments; host hardware details stay in local records. + +#### Running the experiment + +Confirm that `docker info` succeeds, then run from the repository root. Supply credentials through environment variables, without placing them in arguments or experiment records. Because `ANTHROPIC_MODEL` overrides the adapter's model derived from `--model`, explicitly remove it: + +```bash +export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" +export ANTHROPIC_BASE_URL="https://openrouter.ai/api" +unset ANTHROPIC_MODEL + +BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" +BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" + +uv run --locked --project benchmarks/harbor harbor run \ + --dataset "$BENCH_DATASET" \ + --n-tasks 20 \ + --agent harbor_adapter:NanoPyCodeAgent \ + --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ + --agent-kwarg max_turns=50 \ + --model openrouter/deepseek/deepseek-v4-flash-0731 \ + --env docker \ + --n-concurrent 2 \ + --n-attempts 1 \ + --max-retries 0 \ + --job-name tb21-flash0731-pilot20-20260906 +``` + +If credentials are stored in `~/.nanoPyCodeAgent/settings.json`, call `nanopycodeagent.settings.load_settings_env()` in the Python process launching Harbor, remove `ANTHROPIC_MODEL`, and start the same command. Letting only the container CLI read settings is insufficient: host settings are not mounted automatically, so host Harbor must first receive the connection environment. This run used that method in a new right split in the current Herdr tab, preserving focus in the original pane. + +Use a new `--job-name` for each rerun. To run all 89 tasks, remove `--n-tasks 20`, choose a separate baseline job name, and preserve the other conditions. This reruns the entire dataset, with pilot costs counted separately. Analyze this run before executing all tasks or increasing attempts. + +#### Budget and recording plan + +The certificate task cost 0.00222441 USD, but a simple task cannot represent the dataset's average. According to [OpenRouter model prices](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) and [provider quotes](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints) checked on 2026-09-06, Baidu's uncached-input/cache-read/output prices were 0.04998/0.009996/0.09996 USD per million tokens. DeepSeek's base prices were 0.22/0.007/0.66 USD. Routing, promotions, and time affect actual quotes. + +At one million input and 30,000 output tokens per task, with 80% input cache hits, 20 tasks would cost about 0.4–1.4 USD at those two price levels. At three million input and 150,000 output tokens, the estimate becomes 1.4–5 USD. These are budget scenarios, not observed averages or cost ceilings. A 50-turn limit is not a dollar cap. These estimates cover only model API costs, excluding cloud containers, networking, and local execution. + +Record and verify the run in this order: + +1. Save `jobs//config.json`, `lock.json`, task content hashes, and the selected 20-task list. +2. Preserve each `result.json`, `agent/trajectory.json`, agent text log, and verifier log. +3. Count reward-1 tasks, reward-0 tasks, unscored tasks, and infrastructure exceptions separately. Report passes against planned tasks without silently reducing the denominator. +4. Check ATIF validation, usage/cost completeness, and Harbor metric backfill for every trajectory. Missing or partial costs stay unknown; known subtotals are not complete totals. +5. Record total input/cache/output tokens, actual USD cost, job wall time, and per-task durations. Input already includes cache tokens; do not add them again. +6. Distinguish incorrect solutions, exhausted turns, execution timeouts, API errors, and installation/image/verifier failures. Preserve first results and create separate records for reruns. +7. Adjust the full 89-task budget using pilot consumption, then establish a single-attempt baseline. If later measuring variation, retain every repeated experiment instead of replacing first results with the best rerun. + +`/jobs/` is Git-ignored. Archive raw results separately and preserve reviewable configuration, per-task summaries, and artifact locations in development notes. Recording only local directory names does not place a baseline under version control. + +#### Results of the 20-task pilot + +All 20 tasks in `tb21-flash0731-pilot20-20260906` have finished. The [machine-readable results](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) retain per-task versions, metrics, termination reasons, and cost completeness. Individual billing receipts remain local. + +| Metric | Observed result | +| --- | --- | +| Run date | 2026-09-06 | +| Job wall time | 111 min 10 sec | +| Passed / failed / unscored | 8 / 12 / 0 | +| Pass rate, with 20 planned tasks as denominator | 8 / 20 = 40% | +| Passed without Harbor exceptions | 7 / 20 = 35% | +| Harbor exceptions / automatic retries | 1 / 0 | +| Model replies / tool calls | 267 / 329 | +| Original Harbor input / cache / output tokens | 5,853,395 / 5,050,368 / 311,514 | +| Input / cache / output tokens recalculated from final ATIF | 6,207,708 / 5,362,944 / 331,041 | +| ATIF files validated | 20 / 20 | +| Original Harbor metrics consistent | 19 / 20 | +| Trajectories with incomplete costs before independent lookup | 13 / 20 | +| Original Harbor job cost total | 0.045695378 USD | +| Known cost subtotal across trajectories | 0.11053603 USD | +| Cost after independent reconciliation | 0.119200332 USD, complete | + +The table below uses final ATIF model metrics and independently reconciled costs. Original `result.json` files and trajectories were not rewritten. + +| Task | Reward | Model replies | Last stop reason | Cost (USD) | +| --- | ---: | ---: | --- | ---: | +| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | +| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | +| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | +| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | +| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | +| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | +| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | +| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | +| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | +| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | +| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | +| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | +| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | +| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | +| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | +| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | +| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | +| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | +| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | +| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | + +`pytorch-model-recovery` has both reward 1 and `AgentTimeoutError`. Preserve Harbor's raw pass rate of **8/20 (40%)**, and separately report **7/20 (35%) passing without Harbor exceptions**, both using the planned 20 tasks. This task is not a normally completed success sample. + +#### Problems exposed by the pilot + +1. **The final reply in 11 failed tasks has `max_tokens`.** The 8192-token response limit leaves some replies spending most of their budget on thinking before delivering a complete solution. At the time, the agent interpreted “no more tools requested” as the model finishing its work, without distinguishing replies interrupted by budget exhaustion. Those trajectories therefore still end with outcome `completed`. Explicit truncation handling and reasoning/output budget evaluation are needed; pass rates and costs at a higher limit require another experiment. +2. **`mteb-leaderboard` exhausts 50 replies.** Its outcome is `max_turns_exhausted`; the final reply still requests tools and the verifier-required `/app/result.txt` has not been created. This differs from truncating one response. +3. **The agent keeps running after timeout and encounters a tool-argument exception.** Measured from agent execution start, `pytorch-model-recovery` reaches its limit around second 900 and verification starts around second 901. Its journal records new model calls around seconds 929 and 939. The final `edit` supplies `old_text` and `new_text` but no `path`, causing a `KeyError` while displaying the tool call. The run failure is recorded around second 1063. This exception occurs after the timeout and cannot explain it. Execution overlaps verification, limiting how the reward can be interpreted. +4. **Harbor's original aggregate misses the timed-out task's metrics, and cost availability is delayed.** At timeout collection, its trajectory is marked missing. A valid ATIF file later appears and matches an independent backup, but Harbor tokens remain null. Only 19/20 original metrics match final ATIF. Thirteen trajectories initially have partial costs, with 18 generations unresolved by built-in reconciliation. Independent queries to the same OpenRouter endpoint retrieve all 18 receipts, obtaining costs for **all 267 recorded replies**. The original 0.045695378 USD job aggregate is incomplete; the full observed cost is **0.119200332 USD**, including both calls after timeout. + +Complete input is 6,207,708 tokens, including 5,362,944 cached tokens; output is 331,041. Costs come from generation `total_cost`, not advertised-price estimates or changes in the entire account balance. All 20 tasks have scores and valid ATIF. Apart from the timeout, Harbor records no other trial exceptions. + +The original job is under `jobs/tb21-flash0731-pilot20-20260906/`. Its local archive, `jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`, contains the task list, logs, trajectories, per-generation costs, supplemental receipts, Docker image identifiers, and one-off execution and aggregation scripts. It has not been uploaded; its SHA-256 is in the machine-readable results. Two original `torch-pipeline-parallelism` logs contain runtime API credentials. Their permissions are 0600 and archived copies are redacted; checksums for both remain in local records. + +Version control retains only concise experimental configuration, per-task scores, tokens, cost, durations, and exception summaries. Request IDs, internal run IDs, individual billing receipts, precise call times, host hardware, and complete conversations remain local. This minimization applies to the latest version; earlier pushed commits retain reconciliation metadata, and Git history was not rewritten. + +#### Next run and budget implications + +First fix truncation handling, missing-tool-argument crashes, timeout termination, and trajectory collection, then improve delayed cost lookup. These fixes belong to a new revision; the agent source remained pinned throughout this run. Rerun the same 20 task hashes under a new job name, comparing passes without exceptions, termination reasons, tokens, and costs. Preserve this pilot as the pre-fix baseline. Once execution and accounting are reliable, run all 89 tasks and record that result as the full-dataset baseline. + +This pilot costs about 0.12 USD. Multiplying mechanically by 89/20 gives about 0.53 USD, but 11 tasks truncate early and the sample is the first 20 list entries, so this cannot budget the full dataset after fixes. The earlier scenario of three million input tokens, 150,000 output tokens, and 80% input cache hits per task implies about 6–22 USD for 89 tasks. Adjust this to provider prices at the time and consumption in later pilots. Only 20 tasks have run; the full 89-task experiment remains pending. + +### Fixing problems found in batch Terminal-Bench runs + +The following notes explain the problems, implementations, and proposed fixes. Truncation handling, configurable model generation budgets, and recovery from missing tool arguments are implemented. The remaining sections describe problems and proposed work whose code has not yet been implemented. + +#### Truncation handling + +In the original pilot, each model reply had an 8192-token generation limit. If the model exhausts this budget before finishing, the API returns `stop_reason="max_tokens"`, indicating a forced stop at the generation limit. The model may still be analyzing a code change before producing a tool call or complete solution. Some pilot replies spent most of their budget on thinking; all 11 affected failed tasks ended with `max_tokens`. + +Previously, the agent interpreted “no more tools requested” as the model finishing its work. However, generation-budget exhaustion can also stop a reply before it produces a tool call. As a result, unfinished tasks were recorded as `completed`. Evaluating a run requires distinguishing a normal model finish, exhaustion of the task's turn budget, and truncation of an individual reply: each explains a different stopping reason. Even a normal model finish leaves correctness for the verifier to establish. + +This change first makes the reported result trustworthy: recognize truncation, preserve evidence, and explicitly say that the task remains unfinished. Stop the run after truncation. Larger generation budgets and automatic recovery are follow-up capabilities whose effectiveness and additional consumption will be evaluated separately. + +**Implemented on 2026-09-07: recognize truncation and stop.** + +Record the stopping reason as `response_truncated`, even on the final allowed turn, so users know that the last reply itself did not finish generating. The agent makes no further model calls and skips every tool call in that reply, including calls with apparently complete arguments, to avoid acting on an unfinished response. + +Generated text, thinking, tool arguments, and usage remain in the execution record, and cost reconciliation still runs. This allows analysis of where the budget went and where progress stopped. Unexecuted tools are not recorded as executed and have no fabricated results. Analysis must distinguish whether a run ended normally from whether its task was completed. + +Users see an explicit truncation diagnostic, and text already output remains visible. Headless still exits 0, leaving the verifier to judge task success. Interactive mode returns to the input prompt so the user can send another message. Subsequent conversation retains generated text and a truncation notice while removing unexecuted tool requests and incomplete thinking, avoiding invalid history in the next request. The original reply remains in the execution record for later inspection. + +The new stopping reason exceeds the old record protocol's allowed values, so new records use Journal v2. Existing v1 records remain readable and convertible, and public trajectories stay at ATIF-v1.7. A model reaching its generation limit is independent of logs shortening content to control storage size. See [Event Journal v2](../../dev_docs/en/event-journal-protocol-v2.md) for fields, event ordering, and compatibility rules. + +Regression tests cover text, thinking-only and empty replies, truncation on the last turn, skipped tools, the next interactive turn, and usage/cost preservation. A locally simulated SSE response exercises partial tool JSON through the real Anthropic SDK. Old v1 and new v2 truncated trajectories pass Harbor's pinned ATIF validator. These tests do not call paid models. At implementation time on 2026-09-07, no benchmark had been rerun and no pass-rate improvement was inferred from the automated tests. The real-model rerun on 2026-09-09 follows. + +Select `regex-log` and `write-compressor` from `tb21-flash0731-pilot20-20260906`. Both previously scored 0 and ended with `max_tokens`, while their old trajectories recorded outcome `completed`. This rerun checks both official verifier scores and accurate truncation reporting on the current branch. + +**Method and configuration.** Open a right split in the current Herdr tab, preserve focus in the original pane, and run from the repository root. Execute the two tasks concurrently with one attempt each, preserving the original job. + +| Item | Rerun configuration | +| --- | --- | +| Agent branch / commit | `fix/handle-truncated-model-responses` / `f7788616e11f3347300ac2fdef6c6400d4eb5611` | +| Installed container package | `0.8.1.dev11+gf7788616e` | +| Harbor | 0.21.0, run through `uv run --locked --project benchmarks/harbor` | +| Dataset | `terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model / API endpoint | `openrouter/deepseek/deepseek-v4-flash-0731` / `https://openrouter.ai/api` | +| Generation limits | 8192 tokens per reply; at most 50 model replies per task; no explicit reasoning effort or temperature | +| Environment and retries | Local Docker, concurrency 2, one attempt per task, Harbor `max_retries=0`; task-default resources and timeouts | +| Job | `tb21-truncation-rerun2-20260909-f778861` | + +Reuse the original task versions and verify identical task identities in the old and new trial configurations: + +| Task | Task hash | +| --- | --- | +| `terminal-bench/regex-log` | `sha256:802c16cfd132e6c457529cb864be5a757c1b23b6cadc57f2d01983cb0110292a` | +| `terminal-bench/write-compressor` | `sha256:d9ddd9a8e925e2c566b37b2492cbf995afecefe58874e4043ef78d7f3c892c7e` | + +The branch had not been pushed, so containers could not install its remote Git revision. Instead, run `uv build --wheel`, compare every packaged Python source file with the commit, and upload the wheel into each container's `/tmp/`. A temporary test adapter changes installation to use this local wheel. Model calls, tools, and trajectory export still use the current branch. The wheel SHA-256 is `3c4ebd9268680ba0798ccbc44e6e56ee91424c5773399171050b7ac24734e9e4`. + +The launcher reads host connection settings, removes the `ANTHROPIC_MODEL` override, and checks that the endpoint is OpenRouter. Credentials pass through the environment without appearing in command arguments. Task filters require the `terminal-bench/` prefix: + +```bash +--include-task-name terminal-bench/regex-log \ +--include-task-name terminal-bench/write-compressor +``` + +The actual local entry point was: + +```bash +uv run --locked --project benchmarks/harbor python \ + jobs/tb21-truncation-rerun2-20260909-f778861-record/workflow/run.py +``` + +The script prepares the wheel, records a manifest, and invokes Harbor. It and the temporary adapter are local experiment artifacts, not a committed general-purpose benchmark command. + +Related automated regressions also ran that day: all 54 core tests and 16 Harbor adapter tests passed: + +```bash +.venv/bin/python -m pytest -q \ + tests/test_truncation.py tests/test_event_journal.py tests/test_atif.py +benchmarks/harbor/.venv/bin/python -m pytest -q \ + -c benchmarks/harbor/pyproject.toml benchmarks/harbor/tests +``` + +**Results.** Both tasks completed verification without Harbor exceptions. The pass rate was **0/2**, and Harbor reported about **9 min 42 sec** of job wall time. Both truncations were correctly recorded, but neither task was completed. + +| Task | Previous / new reward | Model replies | Final reply tokens | Last stop reason | New terminal outcome | Verifier failure | +| --- | --- | ---: | ---: | --- | --- | --- | +| `regex-log` | 0.0 / 0.0 | 1 | 8192 | `max_tokens` | `response_truncated` | `/app/regex.txt` was not created; 1 test failed | +| `write-compressor` | 0.0 / 0.0 | 2 | 8192 | `max_tokens` | `response_truncated` | `/app/data.comp` was not created; 3 tests failed | + +| Task | Input tokens | Output tokens | Cost (USD) | +| --- | ---: | ---: | ---: | +| `regex-log` | 1678 | 8192 | 0.00158363 | +| `write-compressor` | 3927 | 8303 | 0.001742255 | +| Total | 5605 | 16495 | 0.003325885 | + +Costs come from actual accounting in the final trajectories and are complete for both tasks. Both ATIF files pass Harbor validation, startup versions contain `f778861`, both logs include explicit truncation diagnostics, and no model call follows the final reply. Neither final reply contains tool calls; skipping partial tool arguments and maintaining valid follow-up history are covered by the automated tests but were not triggered by these two real runs. Harbor's successful exit 0 does not establish task success; verifier rewards remain authoritative. + +This rerun verifies recognition of truncation followed by stopping. It neither increases the response limit nor resumes generation. Both tasks still exhaust their per-response budgets before delivering the required files. These results do not establish pass rates with larger budgets or automatic continuation; those need separate experiments. + +Raw trial logs, trajectories, and verifier output are under `jobs/tb21-truncation-rerun2-20260909-f778861/`. The corresponding `jobs/tb21-truncation-rerun2-20260909-f778861-record/` contains `manifest.json`, `summary.json`, `results.en.md`, the wheel, and run, temporary-installation, and summary scripts under `workflow/`. These directories are Git-ignored and have not been uploaded. This document preserves versions, methods, and results without treating local artifacts as version-controlled files. Future reruns must use new job names and preserve this original 0/2 result. + +#### Increasing the model generation budget + +**Per-reply generation budgets are now configurable, with the default raised from 8192 to 32768 for evaluation.** This budget is the **generated-token limit for one model reply**, not cumulative task usage or the context window. **In fully traced experiments, `regex-log` passed twice at 32768; `write-compressor` truncated twice at 32768, then passed a 09-12 rerun explicitly configured at 65536, producing a 2263-byte file.** However, the successful run's largest reply used only **32073 output tokens**, never consuming output space above 32768, so this observation alone cannot attribute success to doubling the budget. Agent execution took **1976.4 seconds**, with no target file at the 900-second checkpoint, establishing that this execution did require a longer deadline. Earlier installation failures, service interruptions, and timeouts remain recorded below. + +**The other 18 tasks passed 5/18 on their initial attempts at 65536, versus 8/18 in the same group's 8192 baseline; five additional attempts across four tasks added no passes.** These experiments did not terminate at `max_tokens` or the 3600-second agent deadline. They also exposed the 50-turn limit, missing or malformed tool arguments, and environment failures. Their failures cannot all be attributed to thinking budgets, nor do these observations establish that increasing the budget improved or reduced the pass rate. + +**Research conclusion: 32768 and 65536 are suitable experimental candidates, but public sources do not establish a universal default to copy.** TB 2.1 evaluations explicitly use both values. Code agents include defaults around 32k/64k, model-dependent limits, and implementations that omit an explicit limit. Evaluation settings and product defaults are recorded separately below so model capacity is not mistaken for an actual request parameter. + +**Settings used by other agents/models on Terminal-Bench.** These first-party evaluation descriptions were checked on 2026-09-09, focusing on this project's **TB 2.1**; TB 2.0 is supplementary. Values are per-generation limits disclosed by the publisher, not cumulative task consumption or independently captured production requests. + +| Benchmark | Model and agent | Disclosed per-generation limit | Comparison conditions and sources | +| --- | --- | --- | --- | +| **TB 2.1** | GLM-5.3 and GLM-5.3-Flash / Claude Code **2.1.207** | **65536** (`max_new_tokens`) | Z.ai specifies `temperature=1.0`, `top_p=1`, and a six-hour timeout. [GLM-5.3 notes](https://huggingface.co/zai-org/GLM-5.3/blob/main/README.md#footnotes), [Flash notes](https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/README.md#footnotes) | +| **TB 2.1** | GLM-5.3-Flash-NVFP4 / Terminus-2 | **32768** (`max_tokens`) | RadixArk's quantized-model evaluation uses `max` effort and **disables task deadlines**. Some trials could not run or finish, so its pass rate is not a comparison under this project's conditions. [Evaluation notes](https://huggingface.co/RadixArk/GLM-5.3-Flash-NVFP4#evaluation) | +| TB 2.0 (supplementary) | Qwen3.6-27B / Harbor + Terminus-2 | **80K** (`max_tokens`, preserving the original unit) | Qwen specifies 256K context, a three-hour timeout, 32 CPUs / 48 GB RAM, and a five-run average. [Model-card footnotes](https://huggingface.co/Qwen/Qwen3.6-27B#evaluation) | +| TB 2.0 (supplementary) | Kimi-K2.6 / Tinker Cookbook's simplified agent | **8192** (`max_tokens`) | Thinking Machines uses 32K context, 200 turns, and no context compaction, and reports many context-limit failures. This establishes that 8192 has been used, not that it suits this project. [Configuration and results](https://github.com/thinking-machines-lab/tinker-cookbook/blob/main/tinker_cookbook/recipes/harbor_rl/README.md#evaluation) | + +DeepSeek-V4 is closer to this project's model. Its technical report describes an internal bash + file-edit harness, at most 500 steps, and 512K **context**, but that section does not disclose per-reply `max_tokens` and evaluates TB 2.0. The 512K value therefore cannot be treated as a generation budget or establish the appropriate setting for `deepseek-v4-flash-0731` on TB 2.1. [Report §5.3.1](https://arxiv.org/html/2606.19348v1#S5.SS3.SSS1) + +**Defaults in other code agents.** Defaults require a version, model, and configuration path. Source links below are pinned to the checked commits; official documentation reflects the 2026-09-09 lookup. + +| Agent / inspected scope | Behavior without an explicit override | Evidence and limits | +| --- | --- | --- | +| Claude Code / Opus 4.6 | **64k** | The **v2.1.77 release notes** explicitly raise this model's default to 64k. The same entry's 128k is an available upper limit, not the default, and this finding cannot be generalized to every model. [Release notes](https://github.com/anthropics/claude-code/releases/tag/v2.1.77) | +| Claude Code / unrecognized model IDs, such as custom gateway names | **32000** | Current official documentation specifies this fallback, overridable with `CLAUDE_CODE_MAX_OUTPUT_TOKENS`. Defaults and ceilings for recognized models vary. [Environment variables](https://code.claude.com/docs/en/env-vars#variables) | +| OpenCode / `830d5eb` | **`min(model output limit, 32000)`**; missing or zero model limits fall back to 32000 | The default request-preparation path uses this calculation; environment variables and plugins can override it. [Calculation](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/provider/transform.ts#L1468), [request construction](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/session/llm/request.ts#L117) | +| pi / ordinary model-request path at `acaa253` | **Determined by the model's `maxTokens`**, then tightened to remaining context | `buildBaseOptions` uses `options.maxTokens ?? model.maxTokens` and reserves 4096 tokens in its context estimate. There is no common fixed value for all models. Thinking budgets also undergo provider-specific processing. [Source](https://github.com/earendil-works/pi/blob/acaa253cc8e3f159e6100b6f3874861b1f0bfc99/packages/ai/src/api/simple-options.ts#L11) | +| mini-swe-agent / default configuration and LiteLLM path at `04d809c` | **No explicit uniform `max_tokens`** | The default YAML omits it; the model layer forwards `model_kwargs`. The selected configuration, LiteLLM, and server determine the actual value. Omitting the parameter does not mean unlimited generation. [Default configuration](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/config/default.yaml), [model call](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/models/litellm_model.py#L64) | +| Codex CLI / official configuration reference | **No uniform default number confirmed in this research** | The inspected configuration reference does not give a default per-generation limit. API model-page output capacity cannot substitute for the CLI's actual default. [Configuration reference](https://learn.chatgpt.com/docs/config-file/config-reference) | + +**Record 32000 and 32768 separately**, rather than reducing both to “32k.” Preserve original units where sources disclose only `64k` or `80K`. Thinking limits and effort levels such as `high`/`max` also need to remain distinct from total generation limits; a separate thinking parameter has not been designed. + +**Implementation and automated acceptance.** Implementation commit: `7a610cbf8`. Configuration uses `--max-tokens`, `ANTHROPIC_MAX_TOKENS`, and the existing `settings.json` `env` object, with precedence **CLI > environment > settings file > 32768**. Both interactive and headless modes resolve the value once at startup and use it for every reply. The effective value must be a positive integer; invalid values fail before any request with exit code `2`. Settings entries retain the existing string rules, for example `"ANTHROPIC_MAX_TOKENS": "32768"`. + +Harbor forwards `--agent-kwarg max_tokens=N` and `ANTHROPIC_MAX_TOKENS`. Omitting the kwarg does not insert a CLI default, allowing environment configuration and the installed version's default to apply. Startup output, Journal `run.started.max_tokens`, and ATIF `agent.extra.max_tokens` record the effective limit separately from actual usage. Older journals without the field still convert; ATIF does not invent a historical budget. + +```bash +nanoPyCodeAgent --max-tokens 65536 -p "fix the failing tests" +# Harbor: add --agent-kwarg max_tokens=65536 to the run command +``` + +All **242 core tests** and **22 Harbor adapter tests** passed. Coverage includes precedence, budgets on every interactive/headless request, early rejection of invalid values, separation of recorded limits from usage, and real SDK request serialization. **Real-model results at 65536 appear in the 09-12 experiment below; successful parameter forwarding does not establish effectiveness attribution.** + +**Handling `stop_reason="max_tokens"` must remain even with a higher limit.** Any finite budget can be exhausted. Raising it gives the model more generation room; existing truncation handling records the stopping reason accurately. Both preceding cases exhausted 8192, providing a comparison point without guaranteeing success at a higher limit. + +Truncation regressions also pass at higher budgets: `max_tokens` still produces `response_truncated`, preserves usage and costs, and skips tools from that reply. This change adds neither a separate thinking parameter nor automatic continuation. + +**Real-model experiment at 32768.** New job `tb21-budget32768-20260909-7a610cb` compares against the 8192 results in `tb21-truncation-rerun2-20260909-f778861`. It retains the same TB 2.1 dataset, task hashes, OpenRouter `deepseek/deepseek-v4-flash-0731`, `max_turns=50`, native 900-second task timeouts, concurrency 2, one attempt per task, and no retries. A local wheel was built from the implementation commit, with each packaged Python source verified against that commit. The launcher removes `ANTHROPIC_MAX_TOKENS` and omits `--max-tokens` to test the built-in default. + +The first `regex-log` trial exceeded the default 360-second installation timeout before model execution and received no verifier score; that record remains in the original job. New job `tb21-budget32768-regex-setup-retry-20260909-7a610cb` reruns only that task with an installation timeout of 1080 seconds (`--agent-setup-timeout-multiplier 3`), retaining its 900-second task deadline. + +The initial `write-compressor` trial entered model execution and completed tool calls in its first reply. Its second call received the server error `stream closed before completion` after about 421 seconds of agent execution. Harbor records `UnknownApiError`, and the verifier reward is 0. The response did not return `max_tokens`, so this does not establish budget exhaustion. The completed first reply used 1528 input / 103 output tokens and cost $0.00011786; complete usage and cost for the interrupted call are unknown. That known subtotal must not be reported as the task's complete cost. + +To check whether the service failure was transient, one additional attempt uses the same model, budget, and task deadline under `tb21-budget32768-compressor-api-retry-20260909-7a610cb`, also with a 1080-second installation window. Both model runs are retained rather than replacing the initial failure. + +**Final results (executed 2026-09-09, verified and archived 09-10).** Times below are Harbor's agent execution durations, excluding installation and verification. The initial installation failure has no model execution time or score. + +| Task | 8192 baseline | Initial 32768 trial | 32768 retry | +| --- | --- | --- | --- | +| `regex-log` | Reward **0**; `response_truncated`; 187.3 sec | `AgentSetupTimeoutError`; no model execution or score | Reward **0**; `AgentTimeoutError`; 900.9 sec | +| `write-compressor` | Reward **0**; `response_truncated`; 304.4 sec | Reward **0**; `UnknownApiError`; 421.4 sec | Reward **0**; `AgentTimeoutError`; 900.9 sec | + +Both retry startup logs confirm `max tokens 32768`; agent versions and task hashes match the experimental configuration. Verifiers report missing `/app/regex.txt` and `/app/data.comp`, respectively. Including the initial server interruption, **three runs entered model execution and received scores, all 0**; one additional trial failed during installation. No retry result replaces an initial failure. + +**A complete usage and cost comparison was not possible.** Neither timed-out retry exported final ATIF. Harbor records trajectory status `missing` and `null` input/output tokens and costs. Model-call counts, final `stop_reason`, and actual truncation counts also cannot be fully reconstructed from the available records; missing values must not become zeroes. Complete baseline costs at 8192 were $0.00158363 and $0.001742255. At 32768, only the initial `write-compressor` trial's completed first reply has the known subtotal of $0.00011786, so no reliable total cost increase can be calculated. + +**Keep 32768 as the experimental default, without describing the 09-09 experiment as successful acceptance of the higher budget.** Configuration, request forwarding, and truncation regressions are verified. Real-model evaluation remains affected by a server interruption, task timeouts, and missing trajectories after timeout. Without complete final replies, the experiment cannot establish that a higher budget eliminates `max_tokens` truncation or imply a gain across the full benchmark. + +All three jobs used the same wheel, SHA-256 `91996b1d4c70af8062318a38b3af9235c85d93c9685e10ac1323f3bef3625425`. Raw logs, verifier output, and exceptions remain in the corresponding jobs; each `-record/` directory contains its manifest, wheel, and launcher. The consolidated summary is under `jobs/tb21-budget32768-20260909-7a610cb-record/` in `summary.json`, `results.en.md`, and `workflow/summarize.py`. These `jobs/` artifacts are Git-ignored and have not been uploaded. The associated containers have been cleaned up. + +**Extended deadline with a complete trajectory (2026-09-10).** Trial `regex-log__2A9PJgL` in new job `tb21-regex-3600s-trace-20260910-7a610cb` received **reward 1.0**, passing the official verifier's `test_regex_matches_dates`. Harbor records **580.4 seconds** of agent execution, excluding installation and verification. This success did not consume time beyond the original 900-second deadline, so it does not establish that extending the deadline resolved the earlier timeout. + +The run retained the same wheel, task hash, model, built-in `max_tokens=32768`, and 50-turn limit, with one task, one attempt, and no retries. Agent execution was allowed **3600 seconds**, followed by 120 seconds for interruption and export. Harbor's outer allowance was 3780 seconds (`--agent-timeout-multiplier 4.2`) to avoid preempting cleanup. Installation remained at 1080 seconds; the verifier deadline was unchanged. A temporary diagnostic wrapper saved an unshortened Journal, HTTP response bodies, per-chunk arrival times, and stack snapshots every 120 seconds directly to persistent logs, retaining the comparison's agent source, prompt, and tools. This run finished naturally without interruption or trajectory reconstruction. + +| Observation | Result | +| --- | --- | +| Model replies / tool calls | **5 / 6**; the first four replies ended with `tool_use`, the last with `end_turn`; no `max_tokens` truncation | +| Total model duration | **567.3 seconds**, approximately **97.9%** of native run time | +| First / subsequent reply durations | **502.4 seconds** / 35.1, 2.8, 4.2, 22.8 seconds | +| Total local tool duration | **0.023 seconds** | +| Complete usage | **74650** prompt tokens, including **53504** cached tokens; **17264** completion tokens | +| Complete cost | **$0.005338074**, with costs reconciled for all five calls | + +**The evidence points to sustained first-reply thinking generation as this run's main delay, rather than local tool execution.** Thinking began about 2.5 seconds after the first request and continued through 500.8 seconds; tool arguments began at 501.7 seconds. The longest interval between nonempty generation deltas was only **3.4 seconds**, with no long stream gap observed. That reply retained 47007 thinking characters. The provider reported **15123 output tokens**, with **12026** in `output_tokens_details.thinking_tokens`. Character counts and provider token accounting differ; subtracting those token fields does not independently measure visible-text usage. + +Integrity checks passed: **147 Journal entries, none truncated**; native ATIF passed Harbor schema validation and exactly matched reconstruction from the Journal. All five streams contained `message_stop`, and all five requests actually sent 32768. Stream analysis uses monotonic clocks to avoid small system-clock adjustments. The diagnostic wrapper also passed three offline scenarios: normal completion, interruption after a simulated stall, and recovery after forced termination. + +**The exact cause of the historical timeout remains undetermined.** Without that run's complete reply and stream timeline, prolonged generation, provider/network stalls, and a different solution path cannot be distinguished. The new run recorded Anthropic SDK **1.4.0**, httpx **0.28.1**, and httpx2 **2.12.0** in its container; corresponding versions were not retained for the earlier container. Equal wheel bytes therefore do not establish identical runtime conditions. This result demonstrates that this task can finish at 32768, but one success cannot establish a causal benefit from extending the deadline or generalize to the other task or full benchmark. + +Complete ATIF is in `regex-log__2A9PJgL/agent/trajectory.json` under the new job. Adjacent `journals/`, `http/`, `http-events.jsonl`, and `python-stacks.log` retain the raw diagnostic evidence. The corresponding `-record/` directory contains `results.en.md`, `summary.json`, `validation.json`, and `workflow/` for the report, integrity checks, and reproduction scripts. Artifacts remain local under Git-ignored `jobs/` and have not been uploaded. The container has been cleaned up, and earlier failure records remain intact. + +**Repeating the method for `write-compressor` (2026-09-11).** Trial `write-compressor__GTmsjFV` in new job `tb21-compressor-3600s-trace-20260911-7a610cb` received **reward 0**. All three official verifier tests failed because `/app/data.comp` was absent. Harbor records **1449.1 seconds** of agent execution (about 24 minutes), no infrastructure exception, and complete native trajectory export. The final outcome is **`response_truncated`**; the 3600-second deadline was not reached. + +The run retained the preceding diagnostic method: the same model and wheel, built-in `max_tokens=32768`, a 50-turn limit, one task, one attempt, and no retries. Execution, finalization, and Harbor's outer allowances were 3600, 120, and 3780 seconds respectively; installation remained at 1080 seconds. The diagnostic wrapper was byte-identical and continued saving an unshortened Journal, raw response streams, arrival times, and stack snapshots. The task hash matched the preceding `write-compressor` baseline. + +| Model reply | Duration | Output tokens | Provider-reported thinking tokens | Stop reason | +| --- | --- | --- | --- | --- | +| 1 | **5.8 seconds** | 118 | 16 | `tool_use` | +| 2 | **1414.2 seconds** | **32768** | **32767** | **`max_tokens`** | + +**This run directly showed sustained thinking outlasting the original deadline, exhausting the generation budget, and leaving no delivered file.** Reply 1 ran `read` and `bash` in just **0.004 seconds** combined. Reply 2 began returning thinking after about 0.9 seconds and continued through 1414.1 seconds. Its longest nonempty generation-delta gap was **2.2 seconds**, with no long silent interval observed. The complete reply retained **116281 thinking characters**, with no visible text or tool arguments. The current agent ends the run on `max_tokens` without automatic continuation, so no subsequent call could produce the file. + +At **900.4 seconds**, an independent read-only checkpoint near the original deadline confirmed that `data.comp` was absent. Only the first reply and two tool calls had completed; the second reply was still streaming thinking. This establishes that the observed execution could not deliver within the original window. The longer allowance let it continue until the generation budget was exhausted: **extending the deadline alone did not solve this failure**. This timeline explains the present execution, not the missing historical trajectory, and the earlier server interruption must remain distinct from this truncation. + +Complete usage was **3926 prompt tokens**, **0 cached tokens**, and **32886 completion tokens**, costing **$0.00615504**. Cumulative output exceeds 32768 because the budget applies per reply. Costs were reconciled for both calls; the second receipt returned HTTP 404 four times before succeeding on the fifth attempt, after the reply had already been truncated. Harbor installation and verification lasted 143.9 and 226.2 seconds respectively, outside the reported agent execution time. + +**Complete evidence and a truncated model reply are distinct.** All **13 Journal entries are unshortened**, including thinking beyond the default 100000-character logging threshold. Native ATIF passed Harbor schema validation and matched projection from the Journal. Both response streams were fully consumed, contained `message_stop`, and used an actual request budget of 32768. The container installed Anthropic SDK **1.5.0**, versus 1.4.0 in the preceding regex diagnostic. The wheel's dependency range permits newer versions, so using the same method does not fix every dependency or backend condition. + +Complete ATIF is in `write-compressor__GTmsjFV/agent/trajectory.json` under the new job, alongside the Journal, raw HTTP responses and timeline, stack snapshots, and runtime metadata. The corresponding `-record/` contains `results.en.md`, `summary.json`, `validation.json`, `checkpoints/`, and reproduction scripts, including the file check at the original 900-second deadline. Artifacts remain local, Git-ignored, and not uploaded. The container has been removed; all earlier failures and the successful regex record remain intact. + +**One further attempt per task (2026-09-11).** This pair ran `regex-log` and `write-compressor` sequentially, once each, without automatic retry. The second task started only after the first task's verification and container cleanup. Model, wheel, task hashes, the built-in 32768 budget, 50-turn limit, and 3600/120/3780-second allowances remained unchanged from the preceding diagnostics. + +| Task | Preceding complete diagnostic | New repeat | +| --- | --- | --- | +| `regex-log` | Reward **1**; 580.4 seconds; 5 replies | Reward **1**; **631.2 seconds**; 9 replies, ending with `end_turn` | +| `write-compressor` | Reward **0**; 1449.1 seconds; `max_tokens` in reply 2 | Reward **0**; **582.1 seconds**; `max_tokens` in reply 3 | + +Times are Harbor agent execution durations, excluding installation and verification. **Neither new run consumed agent time beyond the original 900-second window.** There was no deadline event, forced termination, or trajectory reconstruction. + +Regex's first reply still took about **517.4 seconds**, using 16445 output tokens, including 16346 provider-reported thinking tokens. It subsequently completed eight tool calls in 0.039 seconds combined. None of its nine replies was truncated, and the official `test_regex_matches_dates` passed again. + +Compressor's second reply completed a tool call after **172.5 seconds and 5463 output tokens**, allowing it to reach reply 3. That reply then took **394.8 seconds** and exhausted the **32768** total generation budget, with **28961** provider-reported thinking tokens. Its captured stream contains **112785 thinking characters** and no visible text or tool arguments; subtracting token fields cannot establish a visible-text count. The longest nonempty generation-delta gap was 1.5 seconds. All three tools took about 0.004 seconds combined. `/app/data.comp` was still absent, so all three official tests failed, with native outcome `response_truncated`. + +| Task | Complete prompt tokens (including cache) | Cached tokens | Completion tokens | Complete cost | +| --- | --- | --- | --- | --- | +| `regex-log` | 161870 | 137216 | 20400 | **$0.00720948** | +| `write-compressor` | 12704 | 1280 | 38335 | **$0.00764908** | + +The two tasks' **256 and 18 Journal entries are all unshortened**. Both native ATIF files passed Harbor schema validation and exactly matched projection from their Journals. All 12 model response streams were fully consumed and contained `message_stop`; every actual request sent 32768, and all generation costs were reconciled. Installation took 833.6/448.1 seconds and verification 114.9/264.6 seconds respectively, recorded separately from agent execution. + +**Both regex diagnostics in this comparison passed, and both compressor diagnostics exhausted the generation budget, but durations and stopping turns differed substantially.** The new compressor failure occurred before 900 seconds, confirming the per-reply cap as this execution's stopping condition. Two observations do not establish that future runs must pass or fail, or that a higher budget would succeed. Both new containers recorded Anthropic SDK 1.5.0 and matching versions of the other recorded packages; the earlier regex run used 1.4.0. Sampling, cache state, and backend conditions were not fixed, so timing differences cannot be attributed directly to one factor. + +The new jobs are `tb21-regex-3600s-trace-repeat2-20260911-7a610cb` and `tb21-compressor-3600s-trace-repeat2-20260911-7a610cb`, with trials `regex-log__NmpJwcQ` and `write-compressor__NqmJQWm`. Each trial's `agent/trajectory.json` and adjacent Journal/HTTP/stack records retain complete evidence. Each `-record/` contains its report and reproduction scripts. The combined comparison is in `results.en.md` and `comparison.json` under `jobs/tb21-3600s-trace-repeat2-20260911-7a610cb-record/`. Raw artifacts remain local, Git-ignored, and not uploaded. Both containers were cleaned up, and all earlier records remain intact. + +**Compressor rerun at 65536 (2026-09-12).** This attempt explicitly passed `--agent-kwarg max_tokens=65536`, retaining the preceding compressor model, wheel, task hash, 50-turn limit, and 3600/120/3780-second allowances. One task ran once without automatic retry; the project default remains 32768. It finished with **reward 1 and all three official tests passing**: `data.comp` is **2263 bytes** and decompresses exactly to the original file. + +| Measurement | Result | +| --- | --- | +| Agent execution | **1976.4 seconds**; installation 278.2 seconds and verifier 169.1 seconds are separate | +| Model replies / tool calls | **32 / 32**; final `end_turn`, outcome `completed` | +| Largest reply | **32073 output tokens**; all actual requests sent 65536, without truncation | +| Complete prompt / cache / completion tokens | **1528746 / 1471744 / 59563**; prompt includes cache | +| Complete cost | **$0.018819072**, with all 32 generation costs reconciled | + +Reply 2 remained the main delay: **1179.7 seconds**, 32073 output tokens including 27900 provider-reported thinking tokens, then a normal `tool_use`. Its complete stream contains 108811 thinking characters and a longest nonempty generation-delta gap of 2.2 seconds. All tools took 27.3 seconds combined. At 900.4 seconds the target file was absent; it measured 2727 bytes at 1299.7 seconds and 2388 bytes at 1449.9 seconds, before the final reduction to 2263. + +**This establishes a successful rerun configured at 65536, but does not establish that 32768 must be insufficient.** Every reply remained below 32768, and sampling, cache state, and backend conditions were not fixed, preventing direct attribution to the doubled cap. The recorded execution did use time beyond the original 900-second deadline. SDK 1.5.0 and the other recorded dependency versions match the preceding compressor diagnostic. There was no deadline event, forced kill, or trajectory reconstruction. + +All 498 Journal entries are unshortened, and native ATIF passes schema validation and exactly matches Journal projection. All 32 model response streams were fully consumed and contain `message_stop`. One cost-receipt GET encountered `ConnectError` before headers, then succeeded on retry without interrupting a model reply. The original analyzer's broad check that every HTTP request received a complete response is consequently false; `validation-notes.json` preserves and explains that distinction. + +Job: `tb21-compressor-65536-3600s-trace-20260912-7a610cb`; trial: `write-compressor__hzfqDFs`. Complete `agent/trajectory.json`, Journal, HTTP streams, and timelines are retained. The corresponding `-record/` holds reports, validation, checkpoints, and reproduction scripts; `task-output/` also preserves the final compressed file, input, decoder source, and hashes. Artifacts remain local, Git-ignored, and not uploaded. The container was cleaned up, and all earlier results remain intact. + +**Extending 65536 to the other 18 tasks (2026-09-12–13).** The initial batch took approximately 2 hours 57 minutes and produced **5 passes, 8 reward-zero results, and 5 unscored results**: 5/18 (27.8%), versus 8/18 for the same tasks in the 8192 baseline. The passing tasks were `pypi-server`, `openssl-selfsigned-cert`, `log-summary-date-ranges`, `pytorch-model-recovery`, and `merge-diff-arc-agi-task`. Five subsequent attempts across four tasks added no passes. Initial and supplemental attempts are reported separately, preserving every failure. + +The experiments retained the same verified wheel from `7a610cb`, DeepSeek V4 Flash 0731 through OpenRouter, and task hashes; cached image digests for all 18 tasks also matched the baseline. Explicit settings were `max_tokens=65536`, 50 turns, concurrency 2, 3600 seconds for agent execution, 120 seconds for finalization, a 3780-second Harbor outer allowance, and 1080 seconds for installation. Verifiers retained native task deadlines. Each task ran once per job, without automatic trial retries. **The project default remains 32768; 65536 is the experimental override.** + +In the table, `—` means no official score was obtained and is not counted as a pass. Reward zero can also result from infrastructure or recorder faults, so its cause must be considered. + +| Task | 8192 baseline | Initial 65536 | Main finding | +| --- | ---: | ---: | --- | +| `torch-tensor-parallelism` | 0 | — | First response stream interrupted; verifier timed out downloading Torch/CUDA dependencies | +| `schemelike-metacircular-eval` | 0 | 0 | Reply 19 interrupted; interpreter passed only 4/63 subcases | +| `kv-store-grpc` | 1 | 0 | `SetValRequest` lacks the required `value` field; 5/7 tests passed, including service availability | +| `pypi-server` | 1 | 1 | Local package installation and API test passed | +| `dna-assembly` | 0 | — | TLS download failure installing uv; no model call | +| `torch-pipeline-parallelism` | 0 | — | Exhausted 50 turns; verifier dependency installation timed out without functional test results | +| `qemu-alpine-ssh` | 1 | — | APT package returned 404 during setup; no model call | +| `openssl-selfsigned-cert` | 1 | 1 | All 6 tests passed | +| `regex-chess` | 0 | 0 | Exhausted 50 turns; invalid successor at the starting position; only the length check passed (1/4) | +| `log-summary-date-ranges` | 1 | 1 | Both tests passed | +| `model-extraction-relu-logits` | 0 | — | TLS failure fetching the uv installer; no model call | +| `path-tracing` | 0 | 0 | Exhausted 50 turns; `image.c` absent; 0/5 tests passed | +| `caffe-cifar-10` | 0 | 0 | Exhausted 50 turns; final build lacked `g++`, with binary and model absent; only the prototxt check passed (1/6) | +| `mteb-leaderboard` | 0 | 0 | Exhausted 50 turns; `result.txt` absent; 0/2 tests passed | +| `llm-inference-batching-scheduler` | 1 | 0 | Recorder received SIGSEGV during a periodic stack dump at approximately 120 seconds; output plans absent | +| `pytorch-model-recovery` | 1 | 1 | All 5 tests passed | +| `circuit-fibsqrt` | 0 | 0 | Incorrect circuit output; empty `bash` arguments triggered native `KeyError('command')`, stopping repair; 2/3 tests passed | +| `merge-diff-arc-agi-task` | 1 | 1 | All 5 tests passed | + +**Supplemental attempts addressed confirmed environment or recorder faults and do not overwrite the initial results.** The three setup failures first retried with cached uv and wheel files to bypass download/APT failures; the scheduler separately reran without periodic stack dumping. The cached installer omitted system CA certificates needed by the DNA image, so DNA received one further attempt with corrected installation. + +| Task | Additional attempts | Supplemental result | +| --- | ---: | --- | +| `dna-assembly` | 2 | First retry failed before response headers because of the omitted CA certificates and cannot evaluate the model. Restoring all original system dependencies allowed 3 replies, but malformed `bash` arguments triggered `KeyError('command')`; `primers.fasta` was absent and 0/1 tests passed | +| `qemu-alpine-ssh` | 1 | After 3 replies, malformed `bash` arguments triggered `KeyError('command')`. Verifier setup independently encountered another APT 404; pytest did not run, so its recorded reward zero is not a functional test failure | +| `model-extraction-relu-logits` | 1 | Completed 14 replies normally, but row 11 of the recovered matrix did not match and the official test failed | +| `llm-inference-batching-scheduler` | 1 | No SIGSEGV; after 15 replies, malformed `write` arguments triggered `KeyError('content')`; plans were absent, with only the input integrity check passing (1/6) | + +Recorder and installation differences are part of the evidence. Before any initial batch trial started, supervisor cleanup was corrected to preserve background task services on normal worker exit and clean the process group only at the deadline. Offline service tests passed, as did the `kv-store-grpc` service availability test. System core metadata, ELF files from the same image, and disassembly located the scheduler crash at `PyCode_Addr2Line+11` during periodic stack dumping. This path matches the [CPython timer implementation](https://github.com/python/cpython/blob/v3.13.7/Modules/faulthandler.c#L537) and [frame dumper](https://github.com/python/cpython/blob/v3.13.7/Python/traceback.c#L842), but the underlying memory-fault mechanism remains unproven. Its control retained the original installer and removed only periodic dumping; the crash did not recur. For DNA, a public HTTPS request from the same image, without credentials, failed certificate verification before installing CA certificates and returned HTTP 200 afterward. The corrected trial restored `curl`, `bash`, `git`, `python3`, and CA certificates before installing cached uv and wheel files. Neither diagnostic fault is evidence of insufficient generation budget. + +**Direct budget conclusion: increasing the cap further cannot serve as a universal fix for these failures.** + +- No run in this 65536 experiment reported `max_tokens` or reached the 3600-second agent deadline. Only Scheme (38496), regex-chess (60483), and corrected DNA (35730) actually exceeded 32768 output tokens in a reply, and all three failed. The passing compressor peaked at 32073; the other passing tasks peaked at no more than 6015. +- Pipeline, regex-chess, path-tracing, Caffe, and MTEB exhausted **50 turns** in the initial batch. This is the total call-count limit, distinct from the per-reply generation budget. +- Circuit, the QEMU retry, the scheduler control, and corrected DNA exposed another agent robustness issue: response streams ended completely with `tool_use` but supplied missing or malformed tool arguments, leaving the SDK with an empty dictionary. The native renderer indexed required fields and raised `KeyError`. Circuit's raw JSON was `{}`, QEMU/scheduler supplied `{`, and DNA supplied an unterminated string; these are not all valid empty JSON objects. Argument validation and error feedback should be addressed separately so the agent can attempt repair; that implementation was not changed in this experiment. + +These are diagnostic observations, **not a controlled comparison changing only the budget**. The 8192 baseline used source `2b83097` and native task deadlines; dependencies, sampling, cache state, and backend conditions were not fixed. The change from 8/18 to 5/18 therefore does not establish that a larger budget is harmful, and the compressor pass cannot be attributed to doubling the budget. The two preceding `regex-log` passes used 32768; it was not rerun at 65536, so these different conditions are not combined into a uniform 20-task pass rate. + +The reconciled billing lower bound for the other 18 tasks and their 5 supplemental attempts is **$0.261769566**; including compressor, all 24 attempts cost at least **$0.280588638**. Tensor-parallelism billing queries returned 404 on three checks, leaving complete total cost and token usage unknown. Interrupted Scheme/scheduler requests were priced separately, but those receipts do not repair missing native usage, and unknown values are not treated as zero. + +The [structured experiment results](../../../benchmarks/harbor/results/tb21-generation-budget-65536-20260912.json) contain configuration, source/wheel hashes, per-task results, accounting limits, and local job/trajectory paths. The 24 attempts cover 19 distinct tasks: **20 native ATIF files, 1 clearly marked post-crash reconstruction, and 3 setup failures without trajectories**. Every present ATIF validates against its schema; every native trajectory matches its full Journal projection, with no truncated Journal strings. Received HTTP streams, including interrupted streams, timelines, runtime versions, validation reports, and diagnostic scripts remain under `jobs/`. The main record directory is `tb21-remaining18-65536-3600s-trace-20260912-7a610cb-record`: `comparison.json` indexes every group, and `results.en.md` records the initial 18 tasks. Raw trajectories remain Git-ignored and unuploaded; all experiment and diagnostic containers have been cleaned up. + +#### Automatically continuing after truncation + +The current run ends on `max_tokens` even if model turns remain. The new behavior is to initiate further model calls automatically when configuration permits and sufficient budget remains, continuing the original task so headless execution does not depend on a user manually sending “continue.” + +Expected behavior and constraints: + +- Provide an enable/disable setting and a finite continuation limit. Each additional reply counts against the same run's `max_turns`; recovery must not reset the turn counter. Default enablement, continuation count, and other budget limits remain to be designed. +- Preserve successful tool results and task context while constructing valid request history. Continue skipping tools from truncated replies. Do not replay partial arguments or tool calls without matching results. Thinking-only replies without visible text also need a defined recovery method; continuation prompts and request construction remain to be designed. +- Preserve every `max_tokens` fact in the Journal/ATIF and record subsequent attempts, all usage, and all costs. Do not emit a run terminal event while continuation is still underway. Accurately record normal completion, continuation-limit exhaustion, turn exhaustion, or request failure. Terminal rules and protocol representation for recovery need to be designed together. +- Stop at configured limits, cancellation, or unrecoverable errors with explicit diagnostics, avoiding endless continuation. Continued execution still relies on the verifier to establish correctness. + +Automated acceptance must cover continuation to completion, repeated truncation reaching its limit, exhausted remaining turns, thinking-only replies, and truncated tool arguments neither executing nor being replayed incorrectly. For real-model acceptance, use these two tasks and new job names to compare continuation disabled/enabled at the same response budget, then evaluate larger budgets together with continuation. Report pass rate, additional model calls, total tokens, cost, and duration separately. Automatic continuation remains unimplemented; neither the preceding 0/2 result at 8192 nor the higher-budget experiment constitutes acceptance for automatic continuation. + +#### Crashes caused by missing tool arguments + +When a model omits a required tool argument, the agent raises a Python exception and fails the entire run instead of returning a tool error the model can correct. In this pilot, the final `edit` call in `pytorch-model-recovery` caused this failure. + +The [`edit` definition](../../../src/nanopycodeagent/edit_tool.py) requires: + +- `path`: the file to modify. +- `old_text`: the text to replace. +- `new_text`: its replacement. + +The call supplied only `old_text` and `new_text`, describing a replacement without identifying the file. The schema declares `path` required, but actual arguments still need validation before use. + +This error occurred while displaying the tool call, before any file edit. The missing target path caused even the display of the intended operation to raise `KeyError: 'path'`, ending the entire task. This happened after Harbor's timeout and cannot explain the preceding timeout. + +Both display and execution need coverage: the display must tolerate missing arguments, and execution must validate required fields. A missing field should prevent that tool from running and return a clear `tool_result` with `is_error=True`, for example: + +```text +Missing required argument: path. Provide the file path and retry. +``` + +The model can then supply the missing value in a later turn and continue within the remaining budget. Both display and execution must tolerate incomplete input; otherwise the crash is merely deferred to the next stage. The principle is to return model-correctable argument errors as tool results so the task can continue, rather than ending the entire task. Other tools should follow the same principle. + +**Implementation on 2026-09-13: tool argument validation and error feedback.** The implementation commit is `27a9503`. Before preview or execution, `read`, `write`, `edit`, and `bash` share validation of required fields, the string/integer/boolean types declared in their schemas, and NUL characters in paths and commands. Unknown tool names also return errors. Arguments are not coerced: for example, `replace_all="false"` is not treated as true, and booleans cannot substitute for read line numbers. Extra fields permitted by the schema, empty file contents, and empty replacement text remain supported. + +Invalid calls do not reach tool implementations. Instead, they receive a `tool_result` matching the original call ID, with `is_error=True` and correction guidance. The preview indicates that the call did not execute. Other valid calls in the same reply still run in order, and recovery does not repeat successful tools. The next model reply retains the task, history, and remaining turns, without a separate retry budget. Unexpected programming exceptions inside tools still propagate, avoiding misclassification of every `KeyError` as a model input error. + +The agent also checks the argument JSON actually received in the response stream. The SDK parses partial JSON during streaming; a dictionary containing required fields does not justify executing a command if its closing brace is missing. When a stream finishes with `tool_use` but incomplete argument JSON, the call is rejected with a request for a complete object. Journals and ATIF retain the error, raw argument JSON, and SDK-parsed input. Non-object inputs are saved separately as `raw_input` while preserving existing object field constraints. Follow-up requests use sendable objects instead of replaying partial JSON. Existing `max_tokens` handling remains unchanged: all tools in a truncated reply are skipped and the run ends as `response_truncated`. Automatic continuation is not implemented here. + +**Automated validation and independent review.** All 276 core tests pass with locked dependencies. The same 276 tests also pass in a temporary environment pinned to the preceding experiment's reported Anthropic SDK 1.5.0, HTTPX 0.28.1, HTTPX2 2.12.0, and Pydantic 2.13.5. All 22 Harbor adapter tests pass. New coverage checks that invalid arguments have no side effects, incomplete JSON through the real SDK, corrected calls after error feedback, valid sibling calls, interactive/headless behavior, turn limits, and trajectory evidence. An independent reviewer inspected the complete change in a new Herdr split using the `review-agent` skill and reported **No findings**, additionally verifying mixed-call recovery through the real SDK and Harbor compatibility. + +The four actual failed response streams from circuit, QEMU, scheduler, and DNA were also replayed unchanged through SDK 1.5.0 offline. Each rejected the invalid call, returned an error, and then completed one valid call from a scripted follow-up reply before finishing normally. Tool execution was stubbed and there were no paid model calls. This demonstrates changed handling of identical failed inputs; it does not establish that those four real tasks pass. + +**Known boundary.** Recovery depends on the SDK delivering the response successfully. Some JSON syntax errors, including invalid escapes and trailing commas, raise inside the SDK before recovery can run; independent review confirmed that this limitation also exists on the main branch. Interrupted streams, algorithm errors, exhausted turns, and evaluation environment failures require separate handling. + +**Real-task rerun settings.** The reruns use the same reviewed wheel from `27a9503`, with `deepseek/deepseek-v4-flash-0731` through OpenRouter. Task refs and cached image digests for all original 20 tasks match the 09-06 baseline. Every reply explicitly uses `max_tokens=65536`, with 50 turns per task and at most two simultaneous trials; the project default remains 32768. Agent execution has 3600 seconds, finalization 120 seconds, and the Harbor outer allowance 3780 seconds. Verifiers retain their native task deadlines. Each task runs once per job with no automatic Harbor retries; additional reruns have separate job names and preserve earlier failures. + +In the first targeted5 group on 09-13, QEMU encountered APT 404 errors and DNA exceeded the 1080-second installation limit, both before any model call. PyTorch was paused by the user during installation, and circuit had not started. Subsequent groups increase only the **installation deadline** to 3600 seconds and retry system dependency installation at most three times on HTTP 404. The scheduler agent in that first group had already finished normally before the pause. After resumption, its unchanged artifacts in the preserved container passed all six official tests, yielding reward 1 with **no additional model calls**. This resumed verification is recorded separately from an uninterrupted Harbor trial. + +QEMU installation in the 09-14 targeted4 group still encountered intermittent 404 errors, so a separate retry uses the same 20 packages selected by the original APT index, downloaded from official URLs and checked against their SHA256 hashes and sizes. They enter the cache after `apt-get update`, before the original dependency installation. Preloading them earlier allowed the image's APT cleanup hook to remove them; correcting that order made installation succeed in the original image. Package versions and repositories remain the same. + +In the same targeted4 group, the PyTorch agent finished normally, but the official verifier exceeded its native 900-second deadline downloading Torch/CUDA dependencies, leaving the task unscored. A uv 0.9.5 cache was prepared for a separate retry and the subsequent full20 run, using the official script's unchanged requirements: `pytest==8.4.1`, `torch==2.7.1`, and `pytest-json-ctrf==0.3.5`. After relocation into the original image, all 31 packages installed offline and Torch imported successfully. Preparation ran no task tests. Only `pytorch-model-recovery` receives this cache; its official verification script and 900-second deadline stay unchanged. This changes cache conditions and must be distinguished from the tool argument fix's effect. + +The original 8192 baseline also used another source revision and agent deadlines. The preceding 65536 initial18 group passed five tasks; later supplements and compressor were separate attempts, while `regex-log` had only been rerun at 32768. Comparisons therefore keep the original 20 tasks and the matching 18-task subset separate, without combining different settings into one pass rate. Sampling, routing, cache, and backend state remain uncontrolled. Identical-response offline replays, observed recovery events in real runs, and final task scores are three distinct forms of evidence. + +**Targeted rerun results (2026-09-14).** The table preserves each historical argument failure and its observed rerun result. Installation failures, verifier timeouts, and supplements remain separate; these different conditions are not combined into one formal five-task score. + +| Task | Historical argument crash | Rerun result | Observed argument feedback | +| --- | --- | --- | --- | +| `dna-assembly` | `bash` missing `command`, with an unterminated JSON string | Reward **0**, **0/1** official tests; `max_turns_exhausted` after 50 replies and 3533.7 seconds of agent execution | None | +| `qemu-alpine-ssh` | `bash` missing `command`, with raw JSON `{` | Installation failures retained separately; cached retry reward **1**, **1/1** tests, normal completion after 48 replies | None | +| `llm-inference-batching-scheduler` | `write` missing `content`, with raw JSON `{` | Normal completion after 47 replies before the pause; resumed official verification reward **1**, **6/6** tests, no additional model calls | **One** incomplete `bash` JSON rejection, followed by another model call and a successful `bash` call | +| `pytorch-model-recovery` | The 8192 baseline omitted `edit.path` after timeout | First agent run completed normally but verifier downloads timed out; cached retry reward **1**, **5/5** tests, normal completion after eight replies | None in either run | +| `circuit-fibsqrt` | `bash` missing `command`, with raw JSON `{}` | Reached the 3600-second execution deadline after 49 replies; stopped artifacts passed **3/3** tests with reward **1**; **not a normally completed pass** | None | + +The experimental supervisor interrupted circuit, which ended with `KeyboardInterrupt` and exit 130; Harbor recorded `NonZeroAgentExitCodeError`. Finalization retained a native trajectory without a forced kill, and verification began only after the agent stopped. Both the passing artifact and the exhausted execution budget must therefore remain visible; reward 1 alone does not establish normal completion. + +Scheduler is the only real run in this group that directly demonstrates argument error feedback followed by continued execution and passing verification. Its new error concerns `bash` JSON, rather than the historical `write.content` failure. The other newly sampled attempts produced no argument validation errors, so their passes cannot independently be attributed to this feature. The preceding offline replays provide identical-input evidence for the four historical failed responses. + +Including scheduler, the first PyTorch verifier timeout, and both cached supplements, the group retains **six native ATIF files**. All validate against Harbor, match their full Journal projections, and contain no truncated Journal strings. These six runs account for 222 model replies, 12,183,309 input tokens including 10,021,376 cached tokens, 427,835 output tokens, and **$0.243005564** in model costs, with complete usage and billing. Pure installation failures, model execution that had not started at the pause, and offline replays incurred no model charges. The cached PyTorch retry spent 432.8 seconds installing, 140.0 seconds executing the agent, and 23.2 seconds verifying. This reflects the changed cache preparation; it does not show that agent code accelerated the preceding 900-second verifier download. + +**Full 20-task rerun (2026-09-14–15).** The independent full group finished with +**10/20 passes (50%), nine zeros, and one unscored task**. **Nine tasks completed +normally and passed (45%)**. The original 8192 pilot passed 8/20 (40%), with seven +passing trials without a Harbor exception. Circuit's artifacts passed here, but +cost reconciliation reached the execution deadline, so it is not a normally +completed pass. “No Harbor exception” is also not identical to the new definition, +which requires a native `completed` outcome, no error, and no execution deadline. +[Structured results](../../../benchmarks/harbor/results/tb21-tool-input-recovery-20260914.json) +preserve separate attempts, configuration, billing completeness, and validation evidence. + +| Experiment | Tasks | Reward 1 | Reward 0 | Unscored | +| --- | --- | --- | --- | --- | +| Original 8192 pilot | 20 | 8 | 12 | 0 | +| Previous 65536 initial18 | 18 | 5 | 8 | 5 | +| Fresh matching18 subset | 18 | 8 | 9 | 1 | +| Fresh independent full group | 20 | 10 | 9 | 1 | + +Seven of the matching18 subset's eight passes completed normally. Table values are +rewards. “Outside this group” means the previous initial18 did not include the task, +rather than an unscored attempt. Compressor already passed a separate previous +65536 supplement, while regex-log passed a 32768 supplement; neither belongs in +the old initial18 score. + +| Task | Original 8192 | Previous 65536 initial18 | Fresh 65536 | Fresh result details | +| --- | --- | --- | --- | --- | +| `write-compressor` | 0 | Outside this group | 1 | Normal completion; 3/3 | +| `torch-tensor-parallelism` | 0 | Unscored | 0 | Normal completion; 1/3 | +| `schemelike-metacircular-eval` | 0 | 0 | 0 | Response stream interrupted in request 18 | +| `kv-store-grpc` | 1 | 0 | 1 | Normal completion; 7/7 | +| `pypi-server` | 1 | 1 | 1 | Normal completion; 1/1 | +| `dna-assembly` | 0 | Unscored | 0 | Provider unavailable in request 2 | +| `torch-pipeline-parallelism` | 0 | Unscored | 0 | 50 turns exhausted; 2/3 | +| `qemu-alpine-ssh` | 1 | Unscored | 1 | Normal completion; 1/1 | +| `openssl-selfsigned-cert` | 1 | 1 | 1 | Normal completion; 6/6 | +| `regex-chess` | 0 | 0 | 0 | 50 turns exhausted; 1/4 | +| `log-summary-date-ranges` | 1 | 1 | 1 | Normal completion; 2/2 | +| `model-extraction-relu-logits` | 0 | Unscored | Unscored | Agent completed; verifier exceeded 900 seconds | +| `path-tracing` | 0 | 0 | 0 | Provider unavailable in request 32 | +| `regex-log` | 0 | Outside this group | 1 | Normal completion; 1/1 | +| `caffe-cifar-10` | 0 | 0 | 0 | Normal completion; 2/6 | +| `mteb-leaderboard` | 0 | 0 | 0 | 50 turns exhausted; 0/2 | +| `llm-inference-batching-scheduler` | 1 | 0 | 0 | Response stream interrupted in request 3 | +| `pytorch-model-recovery` | 1 | 1 | 1 | Normal completion; 5/5 | +| `circuit-fibsqrt` | 0 | 0 | 1 | 3/3; cost reconciliation interrupted, trajectory reconstructed | +| `merge-diff-arc-agi-task` | 1 | 1 | 1 | Normal completion; 5/5 | + +Relative to the original pilot, compressor, regex-log, and circuit gained passes, +while scheduler changed from a pass to zero: a net gain of two tasks. Relative to +the previous 65536 initial18, KV-store changed from zero to a pass, QEMU from +unscored to a pass, and circuit from zero to passing artifacts with a finalization +failure. KV-store and QEMU already passed the original 8192 pilot, and compressor +and regex-log passed their earlier budget supplements. These score changes +therefore do not establish an independent benefit from argument validation. + +The fresh full group recorded **no `ToolInputError` events**. The four offline +replays still provide identical-input evidence, while the targeted scheduler +provides observed live error feedback followed by continued execution and passing +verification. The fresh scheduler failed with `RemoteProtocolError` in request +three, after two completed replies. Its targeted success must not replace this +separate failed attempt. + +Four requests did not return complete responses: Scheme and scheduler lost their +response streams; DNA and path-tracing received `provider_unavailable` errors +referring to SIGTERM and a graceful shutdown timeout. These are transport or +upstream availability failures. Pipeline, regex-chess, and MTEB exhausted fifty +turns; tensor and Caffe completed normally without passing all functional tests. +Model extraction completed twenty-seven replies, installed dependencies, and +entered its single pytest test before exceeding the native 900-second verifier +limit. It remains unscored. This was not a dependency download timeout, and its +deadline was not extended to replace the result. + +**Circuit finalization failure.** Reply fifty finished with `end_turn` at +18:46:26.965 UTC, followed by provider cost reconciliation. The supervisor enforced +the 3600-second execution deadline at 18:47:05.171, interrupting reconciliation +before either `run.completed` or `run.failed` had been written. Native ATIF export +raised `AtifProjectionError: Event Journal has no terminal event`. The experiment +wrapper preserved the original Journal and reconstructed a trajectory with an +explicitly marked synthetic terminal event; it does not claim native export. +Official verification began after the agent stopped and passed all three tests. +This cost-finalization control block is unchanged from the implementation commit's +parent and was not modified here. A follow-up needs to ensure that interrupted +billing reconciliation still records a terminal state and exports a trajectory. +A separate `Gate`-object `KeyError` appeared in model-generated program output and +was returned as a tool result; it was not an agent crash from missing tool arguments. + +Late in circuit, three detailed monitoring probes each exceeded twenty-five +seconds. A read-only check found the container still running at about 1.983/2 GiB +of memory, with Docker not reporting an OOM. Detailed in-container probes were +then stopped in favor of host result files and container state. Agent, verifier, +and resource limits stayed unchanged; no orphaned probe was found. The earlier +probes' contribution to memory pressure is unknown, so this monitoring change +remains an experimental limitation. + +**Trajectories and costs.** All twenty ATIF files validate against Harbor: nineteen +are native exports matching their full Journal projections, and one is the +explicit reconstruction above. No Journal strings were truncated. There were +491 model calls started and 487 complete replies. Fully recorded replies account +for **21,991,162 input tokens**, including **18,119,936 cached tokens**, and +**724,013 output tokens**. Native usage for four interrupted responses remains +incomplete; missing values must not be treated as zero. Per-reply output maxima +also cover completed replies only and do not describe an interrupted response's +actual output. QEMU additionally had one pre-connection `ConnectTimeout` followed +by an SDK retry: forty-one model replies and forty-two HTTP attempts. + +All model costs are covered by native records or separate provider receipts. +Supplemental accounting distinguishes completed replies from interrupted requests +and subtracts costs already recorded in the Journal, avoiding double-counting +when a reconstructed trajectory lacks the native terminal event. The full twenty +tasks cost **$0.462175819**. Including targeted attempts, cached supplements, and +the scheduler whose verification resumed after the pause, total experiment model +cost is **$0.705181383**. Complete billing does not imply complete native responses +or usage. + +#### Full 20-task retest of stream recovery + +**Run on 2026-09-21 to 22.** On commit +`577a1ab7f96fc3d220ca89c068988a479ac7b1aa` of the `fix/stream-recovery` branch, we +reran the original 20 tasks to check whether real model responses were interrupted +and whether an interruption could recover within the same run. The run started at +22:06 Beijing time on 09-21 and ended at 01:52 on 09-22, taking about **3 hours +46 minutes**; no transport faults were injected. + +**Configuration and recording.** Harbor 0.21.0 and Terminal-Bench 2.1, keeping the +original 20 task refs and verifying the cached image digests before the run. The +model was still `deepseek/deepseek-v4-flash-0731` on OpenRouter. Containers +installed the wheel built from this branch, and every Python source file in the +package was checked against the current commit; the running version was +`0.8.1.dev28+g577a1ab7f`. Dependencies were pinned to Anthropic SDK 1.5.0, HTTPX +0.28.1, HTTPX2 2.12.0, and Pydantic 2.13.5. + +Each reply was capped at 65536 tokens, with at most 50 complete replies per task +and concurrency 2; each task was attempted once, with Harbor whole-task automatic +retries set to 0. The agent was capped at 3600 seconds, with 120 seconds of +finalization grace and a 3780-second outer Harbor limit, a 3600-second install +cap, and the official verifier left at its original limit. We reused the earlier +dependency cache and passive HTTP recorder; the container did only lightweight +event counting, and full Journal / HTTP records were collected to the host for +analysis. The live counter was corrected once to accept JSON without spaces and +restarted separately; the evaluation was not restarted and the agent, tasks, and +budget were not changed. + +| Metric | This run | +| --- | --- | +| Finished trials | 20/20 | +| Actually entered agent execution | 18 tasks; the other 2 failed during install and made no model calls | +| Official scores | reward 1: **11 tasks (55%)**; reward 0: **6 tasks**; unscored: **3 tasks** | +| Completed normally and passed | **8 tasks (40%)**: native terminal state `completed`, no Harbor exception, execution limit not reached | +| Journal model calls | **576 started, 576 fully returned** | +| `model.failed` / interrupted HTTP response bodies | **0 / 0** | +| Stream recovery triggered / succeeded | **0 / 0**; the recovery branch was not exercised this run | + +Per-task results follow. "Turns exhausted" means the native terminal state was +`max_turns_exhausted`; whether the official verifier judges the artifact as passing +is a separate matter. + +| Task | Reward | Execution or verification result | +| --- | --- | --- | +| `write-compressor` | 1 | Completed normally and passed | +| `torch-tensor-parallelism` | Unscored | Agent completed normally; verifier exceeded the original 900-second limit | +| `schemelike-metacircular-eval` | 1 | 50 turns exhausted; artifact passed | +| `kv-store-grpc` | 0 | Agent completed normally, artifact failed | +| `pypi-server` | 1 | Completed normally and passed | +| `dna-assembly` | 0 | Agent completed normally, artifact failed | +| `torch-pipeline-parallelism` | Unscored | Install used Python 3.12.3, below the project's required Python >=3.13 | +| `qemu-alpine-ssh` | Unscored | Install downloaded Python 3.14.0 and still received HTTP 504 after three retries | +| `openssl-selfsigned-cert` | 1 | Completed normally and passed | +| `regex-chess` | 1 | 50 turns exhausted; artifact passed | +| `log-summary-date-ranges` | 0 | Agent completed normally, artifact failed | +| `model-extraction-relu-logits` | 1 | Completed normally and passed | +| `path-tracing` | 1 | Completed normally and passed | +| `regex-log` | 1 | Completed normally and passed | +| `caffe-cifar-10` | 0 | 50 turns exhausted, artifact failed | +| `mteb-leaderboard` | 0 | 50 turns exhausted, artifact failed | +| `llm-inference-batching-scheduler` | 1 | 50 turns exhausted; artifact passed | +| `pytorch-model-recovery` | 1 | Completed normally and passed | +| `circuit-fibsqrt` | 0 | 50 turns exhausted, artifact failed | +| `merge-diff-arc-agi-task` | 1 | Completed normally and passed | + +Both install failures were recorded by Harbor as `NonZeroAgentExitCodeError` and +happened before the agent started; the tensor exception was +`VerifierTimeoutError`. These are not model stream interruptions, and the 20 +finished trials must not be written as "all 20 tasks completed model execution". +Likewise, Scheme, regex-chess, and scheduler among the 11 reward-1 results passed +after exhausting turns and do not count toward the 8 completed-and-passed. + +**Evidence boundary for recovery.** All 576 model calls this run returned +completely, so no real stream interruption was observed; we can only record "no +stream interruption occurred this run", and cannot claim "recovery succeeded after +a live interruption". At the start of this run we separately ran +`tests/test_stream_recovery.py`, which reported **17 passed, 0 failed, 0 skipped**, +covering both HTTP exception families, retry exhaustion and the time window, +discarding tool calls from an interrupted reply, and preserving existing tool +results. These automated tests validate the recovery mechanism but cannot replace +a real interruption sample. Sampling, routing, caching, and server state were +uncontrolled; the score change from the earlier 20 tasks also cannot be attributed +to stream recovery. + +The local job is `jobs/tb21-stream-recovery-pilot20-20260921/` and the experiment +record directory is `jobs/tb21-stream-recovery-pilot20-20260921-record/`. There, +`manifest.json` stores source, wheel, task, and image information; `summary.json` +stores per-task statistics; `report.md` is the result summary; and +`stream-recovery-tests.json` stores the dedicated test result. Raw Journals, HTTP +records, trajectories, and verifier output are kept in each trial directory; these +raw data live in the Git-ignored `jobs/`. + +#### Stopping execution on timeout + +Timeout handling must ensure that the agent inside the container actually stops before verification begins. In this pilot, Harbor had already classified the task as timed out while the agent continued calling the model. + +The affected task, `pytorch-model-recovery`, has a 900-second (15-minute) agent limit. Relative to agent execution start: + +| Time | Observed event | +| --- | --- | +| About 900 sec | Harbor records `AgentTimeoutError` | +| About 901 sec | The verifier begins | +| About 929 and 939 sec | The agent starts two more model calls | +| About 1063 sec | Missing `path` in `edit` causes failure and `run.failed` | + +Harbor's timeout did not promptly stop container execution, with two consequences: + +- During verification, the agent could continue modifying files under examination, weakening result reliability. Reward 1 therefore does not make this a normally completed success sample. +- New model calls and costs occurred after the deadline, so the time boundary was not enforced. + +Proposed goals: + +1. Stop agent execution at the deadline and prevent further model or tool calls. +2. Terminate tool subprocesses that need cleanup so commands cannot keep modifying files after the agent exits. +3. Confirm execution has stopped, then start verification and record timeout state accurately. + +Continued execution after timeout is confirmed. Which cancellation or cleanup layer failed remains to be investigated. The goal is to enforce the time limit; extending the 900-second allowance would not fix execution continuing after timeout. + +#### Trajectory collection + +Trajectory collection must preserve the structured execution record and make it available to Harbor in time to aggregate steps, tokens, and costs. In this pilot, the file eventually existed but was unavailable when Harbor collected metrics. + +Normally, the agent continuously records model replies, tool calls, results, and usage, then exports those records as ATIF `trajectory.json` when execution ends. Harbor subsequently reads and validates the trajectory to aggregate steps, tokens, and costs. This creates three stages that must finish in order: the agent stops, the trajectory is saved, and the evaluator collects it. + +For `pytorch-model-recovery`, collection had a timing problem: + +1. Harbor began collection after timeout, found no trajectory, and recorded it as `missing`. +2. The agent continued running and only wrote valid ATIF during later finalization. +3. The file eventually appeared in the result directory, but original Harbor token metrics remained `null` and were not backfilled automatically. + +All 20 final ATIF files therefore validate, but only 19/20 original Harbor metrics match them. A file's eventual existence and validity do not establish successful collection at aggregation time. + +The proposed goal is to preserve as much execution history as possible up to stopping, whether the run completes, fails, or times out, and let Harbor read it before aggregation. Incomplete records must be labeled explicitly. + +Design this together with timeout termination: stop execution, finalize within a bounded time, then collect and aggregate. Trajectory export currently depends on the agent completing finalization. Forcibly terminating the process may leave no time to save it. Periodic snapshots or reconstruction from persisted journals therefore need consideration. The specific approach is undecided; the objective is consistency among actual execution, saved trajectories, and Harbor metrics. + +#### Improving delayed cost reconciliation + +Costs unavailable at task completion should remain queryable later so accounting can be completed. + +A model response may lack actual cost, and the billing endpoint may not yet return an amount. Save the call's generation ID as the association key and query its `total_cost` later. Temporary unavailability means pending or unknown cost, not free execution. + +During finalization, the agent already queries calls with generation IDs and missing costs individually. Each lookup defaults to six attempts, separated by `1, 2, 4, 8, 15` seconds. These delays total 30 seconds, plus request durations; 30 seconds is not a wall-clock cap on the complete reconciliation process. Exhausting the attempts preserves an incomplete-cost state. Finalization reconciliation is already a supported agent feature, while an independent later-reconciliation interface is not yet provided. + +In the pilot, 13 original trajectories had incomplete costs, with 18 calls unresolved during finalization. During and after the 2026-09-06 batch run, an independent script queried the same endpoint in batches and eventually obtained all 18 costs. + +**Those independent lookups used a temporary script that is not part of the supported workflow.** The script is `jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`, under Git-ignored `jobs/`, archived as a one-off workflow record. It must be run separately and is not integrated into the official CLI, Harbor adapter, or a standard evaluation post-processing stage. Completing costs through that script does not mean the agent automatically performs later lookups. + +| Accounting basis | Cost (USD) | +| --- | ---: | +| Known cost subtotal in final trajectories | 0.11053603 | +| Complete cost after independent reconciliation | 0.119200332 | + +The reconciled total covers all 267 recorded model replies. This shows that bounded finalization attempts can leave costs requiring later lookup; the known subtotal at that time is not a complete total. + +The proposed direction is to turn this one-off follow-up into a reusable supported capability that integrates with evaluation and can run independently after tasks finish. Read unresolved generation IDs, query later, preserve receipts, and produce updated aggregates. Preserve original results, avoid counting any charge twice, and distinguish known subtotals from complete totals. Unresolved amounts remain unknown rather than zero, and reconciliation failures must not change task scores. + +Whether this becomes a separate command or an automatic post-benchmark stage is undecided. The goal is to continue reconciliation beyond task finalization without delaying agent exit indefinitely while waiting for billing records. + +### Re-running the six failed tasks with a different model + +**Run on 2026-09-25.** In the previous stream-recovery retest, six of the twenty tasks scored reward 0 under `deepseek/deepseek-v4-flash-0731`: `kv-store-grpc`, `dna-assembly`, `log-summary-date-ranges`, `caffe-cifar-10`, `mteb-leaderboard`, and `circuit-fibsqrt`. To judge whether those failures were model-limited, this round re-ran exactly those tasks under the same conditions, changing only the model. This is not a controlled experiment: each task ran once, and routing, sampling, cache, and provider backend were not held fixed. + +**Model choice.** The goal was to switch to a DeepSeek Flash model. OpenRouter has no `deepseek-flash` slug, and aliases such as `deepseek/deepseek-v4-flash-latest` are rejected as invalid model IDs by the Anthropic Messages endpoint. The run therefore used **`deepseek/deepseek-v4.1-flash`** (V4.1 Flash), after a single minimal Messages request confirmed it works and supports tool use. An earlier attempt with `deepseek/deepseek-v4-flash` (V4 Flash 0423) was aborted before it finished; its artifacts are kept under `jobs/tb21-v4flash-6task-20260925/` and are not treated as an experiment result. Only the V4.1 Flash round is recorded here. + +**Fixed conditions.** + +| Item | This round | +| --- | --- | +| Job | `tb21-v41flash-6task-20260925` | +| Agent source | `main` commit `1386df48ef9c84d7b65c33fcf0f903fad7036a79`; wheel `0.8.1.dev30+g1386df48e` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| Dataset | `terminal-bench/terminal-bench-2-1`, `sha256:7d7bdc1c…a0699a` | +| Tasks | The six above, each with its pinned task ref | +| Generation / turns | At most 65536 tokens per reply; at most 50 complete replies per task | +| Deadlines | Agent 3600 s, finalization grace 120 s, Harbor outer limit 3780 s, setup 3600 s; verifiers keep their native task deadlines | +| Concurrency / retries | Concurrency 2; one attempt per task, zero automatic Harbor retries | +| Dependencies | Anthropic SDK 1.5.0, HTTPX 0.28.1, HTTPX2 2.12.0, Pydantic 2.13.5 | +| Images | Reused the cached baseline images, with RepoDigests checked before the run | + +**Results.** All six tasks finished and all received an official score: **four passed and two failed**. + +| Metric | This round | +| --- | --- | +| Passed / failed / unscored | **4 / 2 / 0** | +| Journal model calls | **206 started, 206 returned complete** | +| `model.failed` / HTTP response-body interruptions | **0 / 0** | +| Input / cache / output tokens | 8,122,046 / 7,444,992 / 219,443 | +| Model cost | **$0.278759041** (all per-call receipts complete) | +| Infrastructure exceptions | 0 | + +| Task | Reward | Outcome | Model replies | Cost (USD) | +| --- | ---: | --- | ---: | ---: | +| `kv-store-grpc` | 1 | `completed` | 17 | 0.008440065 | +| `dna-assembly` | 1 | `completed` | 34 | 0.069021966 | +| `log-summary-date-ranges` | 1 | `completed` | 6 | 0.005224672 | +| `circuit-fibsqrt` | 1 | `completed` | 49 | 0.080010864 | +| `caffe-cifar-10` | 0 | `max_turns_exhausted` | 50 | 0.065742942 | +| `mteb-leaderboard` | 0 | `max_turns_exhausted` | 50 | 0.050318532 | + +**The two remaining failures were both turn exhaustion, not crashes or transport faults.** + +- `caffe-cifar-10`: CPU training was too slow; within 50 turns it reached only `Iteration 200`, so `cifar10_quick_iter_500.caffemodel` and `Iteration 500` never existed. The official suite passed 2 of 6 tests and failed 3 (one more passed). The bottleneck is the turn limit combined with training time. +- `mteb-leaderboard`: `/app/result.txt` was never produced and both official tests failed. The model kept reading MTEB source and ran out of turns without writing the result file. Every historical run of this task has failed the same way. + +**Comparison with history.** All six tasks scored reward 0 in the 2026-09-21 run under 0731. Under V4.1 Flash: + +- `kv-store-grpc` and `log-summary-date-ranges` passed again (they had also passed in the earlier 8192 and 09-14/15 runs, so this is a recovery rather than a first pass). +- `dna-assembly` **passed for the first time**, with a clean `completed`; every prior attempt was either 0 or unscored. +- `circuit-fibsqrt` **passed cleanly for the first time**; its two earlier reward-1 results were both accompanied by an execution limit or trajectory reconstruction anomaly. +- `caffe-cifar-10` and `mteb-leaderboard` still failed, and have never passed. + +**Evidence boundary.** Routing, sampling, cache, and backend were not controlled while the model changed, and each task ran only once, so **0/6 → 4/6 cannot be attributed to the model difference**; it is only a signal to verify later. + +**Artifacts.** Raw trials are under `jobs/tb21-v41flash-6task-20260925/`, with the experiment record under `jobs/tb21-v41flash-6task-20260925-record/` (`manifest.json`, `summary.json`, `report.md`); these live in Git-ignored `jobs/` and were not uploaded. Version control stores the structured result [`tb21-v41flash-6task-20260925.json`](../../../benchmarks/harbor/results/tb21-v41flash-6task-20260925.json) and this summary. + +### Experiment 1: raising max_turns to 100 + +**Run on 2026-09-26.** In the previous run (`tb21-v41flash-6task-20260925`), both +`caffe-cifar-10` and `mteb-leaderboard` exhausted 50 turns +(`max_turns_exhausted`). The hard constraint Terminal-Bench / Harbor imposes on an +agent is **wall-clock time** (this task's native `[agent].timeout_sec = 3600`); it +says nothing about a turn limit — the 50 turns are nanoPyCodeAgent's own cost +guardrail (`adapter.py:18`, `agent.py:82`). So this run changes one thing only: +**`max_turns` goes from 50 to 100, everything else stays the same**. To keep the +run compliant, the Harbor-level `override_timeout_sec` and +`override_setup_timeout_sec` were **removed**, so the task-native limits apply; the +agent self-limits to 3480s inside the task's 3600s and reserves 120s to finalize. + +| Item | This run | +| --- | --- | +| Job | `tb21-exp1-turn100-20260926` | +| Agent source | branch `experiment/max-turns-and-time-budget`, commit `41476d451`; wheel `0.8.1.dev32+g41476d451` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| Dataset | `terminal-bench/terminal-bench-2-1`, `sha256:7d7bdc1c…a0699a` | +| Tasks | `caffe-cifar-10`, `mteb-leaderboard`, pinned task refs | +| Generation / turns | at most 65536 tokens per reply; at most **100** replies per task | +| Time limits | task-native agent 3600s; the agent self-limits to 3480s + 120s finalization; **no Harbor timeout overridden** | +| Concurrency / retries | concurrency 2; one attempt per task; zero automatic Harbor retries | + +**Result: both tasks passed, but they failed differently.** + +| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | +| --- | ---: | --- | ---: | ---: | ---: | +| `mteb-leaderboard` | 1 | `completed` | 70 | 1666 s | 0.054191917 | +| `caffe-cifar-10` | 1 | `DiagnosticDeadlineExceeded` | 69 | 3600 s | 0.039895559 | + +- `mteb-leaderboard`: **passed for the first time**, and cleanly — it wrote + `/app/result.txt` on turn 70 and stopped on its own. That confirms the previous + failure was **not a capability problem but the self-imposed 50-turn cap**: it had + already computed the right answer by turn 39, was still re-verifying at turn 50, + and finally converged before turn 100. +- `caffe-cifar-10`: all six official tests passed, but the agent did not wind down + on its own — after `Iteration 500` finished training and it had tested the + trained model, it went on to build pycaffe (`make py`) and install + `python3-skimage` and other extras until it hit the 3480s self-limit and the + 3600s hard limit and was killed (`deadline_reached=true`, + `forced_kill=true`). **The reward came from artifacts already on disk at the + timeout**, not from the agent finishing; the trajectory could only be + reconstructed by the supervisor from the journal + (`trajectory_source=reconstructed`). + +**Conclusion.** These two tasks show two sides of the same judgment: + +1. Raising the turn cap to 100 did let `mteb-leaderboard` pass — **the self-imposed + turn cap was the binding constraint**. +2. But `caffe-cifar-10` shows that **raising the cap alone does not teach the agent + to stop**: it keeps working until it is killed externally. That is exactly what + the time budget, budget injection, and stop-on-deadline (Experiment 2) are meant + to address. + +**Evidence boundary.** One trial per task, with routing, sampling, cache, and +backend uncontrolled; this is a targeted signal, not a controlled estimate of +pass-rate improvement. + +**Artifacts.** Raw trials are under `jobs/tb21-exp1-turn100-20260926/`, with the +experiment record under `jobs/tb21-exp1-turn100-20260926-record/`; these live in +Git-ignored `jobs/` and were not uploaded. Version control stores the structured +result +[`tb21-exp1-turn100-20260926.json`](../../../benchmarks/harbor/results/tb21-exp1-turn100-20260926.json) +and this summary. + +### Experiment 2: time budget, budget injection, and stop-on-deadline + +**Run on 2026-09-26.** Experiment 1 showed that raising max_turns lets both tasks +pass, but `caffe-cifar-10` was still killed externally (`forced_kill=true`, the +trajectory reconstructed). This run adds three things on top of Experiment 1 and +changes nothing else: + +- **Time budget**: a new `--time-budget-seconds` (Harbor kwarg + `time_budget_seconds`). This run uses 3420s, inside the task-native 3600s and + before the supervisor's 3480s self-limit. +- **Budget injection**: the system prompt carries the remaining time each turn, + and inside the finalization window (`max(15% of the budget, 180s)`) it + escalates to "stop investigating, write the output, then finish". +- **Stop-on-deadline**: when the budget runs out the run returns a new + `time_budget_exhausted` outcome, stopping before the next model call or tool + execution, and records it in the Journal and ATIF. + +| Item | This run | +| --- | --- | +| Job | `tb21-exp2-timebudget-20260926` | +| Agent source | branch `experiment/max-turns-and-time-budget`, commit `eb77fd578`; wheel `0.8.1.dev34+geb77fd578` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| Time limits | task-native agent 3600s; agent budget 3420s; supervisor self-limits to 3480s + 120s finalization; **no Harbor timeout overridden** | +| Everything else | at most 100 replies per task, at most 65536 tokens per reply, concurrency 2, one attempt, zero retries — same as Experiment 1 | + +**Result: both tasks passed, and this time both wound down on their own.** + +| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | +| --- | ---: | --- | ---: | ---: | ---: | +| `mteb-leaderboard` | 1 | `completed` | 50 | 865 s | 0.481624230 | +| `caffe-cifar-10` | 1 | `completed` | 61 | 2657 s | 0.374407898 | + +- `mteb-leaderboard`: finished in 50 replies and 865s, **twice as fast** as + Experiment 1's 70 replies and 1666s; its final message names the answer + `GritLM/GritLM-7B` and states it wrote `/app/result.txt`. +- `caffe-cifar-10`: 61 replies and 2657s, **stopping on its own** — the supervisor + reports `deadline_reached=false`, `forced_kill=false`, + `trajectory_source=native`, in contrast to Experiment 1, where it kept building + pycaffe after training, was force-killed, and its trajectory had to be + reconstructed. Its final message is "All requirements are met", with exactly 500 + training iterations and 55.17% test accuracy. + +**The two experiments side by side (two tasks each, one trial each):** + +| Metric | Experiment 1 (max_turns=100) | Experiment 2 (+ 3420s budget) | +| --- | ---: | ---: | +| Passed | 2 / 2 | 2 / 2 | +| Clean `completed` | 1 / 2 | **2 / 2** | +| Deadline hit / forced kill | 1 (caffe) | **0** | +| Trajectory reconstructed | 1 (caffe) | **0** | +| Total model calls | 139 | 111 | +| Input tokens | 340,804 | 2,919,588 | +| Cache tokens | 4,289,280 | 1,408 | +| Total cost (USD) | 0.094 | **0.856** | + +**Finding 1: the time budget makes the agent finish on its own.** Both tasks +stopped before the budget ran out, with `deadline_reached` and `forced_kill` both +false and native trajectories. This is exactly the "does the work but will not +stop" gap that Experiment 1 exposed. + +**Finding 2: rewriting the system prompt every turn breaks the prefix cache; the +total cost rose, but routing confounds it.** On the surface, Experiment 2 used +about 8.6× the input tokens of Experiment 1 (2.92M vs 0.34M), cache tokens fell +from 4.29M to 1.4K, and total cost rose from $0.094 to $0.856. Split by provider, +there are two causes: + +1. **The system prompt changes every turn, so the prefix cache misses.** DeepInfra + is the only provider that reports cache reads consistently in both runs. In + Experiment 1 its 121 calls averaged 29,116 cached tokens each; in Experiment 2 + its 23 calls averaged 61. On the same provider the prefix cache essentially + vanished, consistent with a system prompt that invalidates the + longest-common-prefix cache every turn. +2. **Routing shifted (uncontrolled).** Experiment 1 sent 121 of 139 calls to + DeepInfra; Experiment 2 spread its 111 calls across Together (48), Parasail + (29), DeepInfra (23), and Relace (10). Together, Parasail, and Relace reported + no cache reads in either run. So the 8.6× cost increase **cannot be attributed + to the injection alone**; the routing change is a confounder on top of it. + +Either way the fix is the same: **keep the system prompt fixed and append the +budget note as a user/tool message at the tail** (a tail change does not +invalidate the prefix), or inject only when crossing a threshold. This run +records the flaw as-is; the fix is left for the next round. + +**Evidence boundary.** Still one trial per task, with routing, sampling, cache, +and backend uncontrolled; "faster and cleaner" is a targeted signal, not a +controlled estimate, and run-to-run variance cannot be ruled out. The only thing +directly attributable is the mechanism itself: `deadline_reached=false`, +`forced_kill=false`, and `trajectory_source=native` are direct evidence that the +agent stopped itself. + +**Artifacts.** Raw trials are under `jobs/tb21-exp2-timebudget-20260926/`, with the +experiment record under `jobs/tb21-exp2-timebudget-20260926-record/`; these live +in Git-ignored `jobs/` and were not uploaded. Version control stores the +structured result +[`tb21-exp2-timebudget-20260926.json`](../../../benchmarks/harbor/results/tb21-exp2-timebudget-20260926.json) +and this summary. + +### Experiment 3: moving the budget reminder to the conversation tail + +**Run on 2026-09-27.** Experiment 2 found that rewriting the system prompt every +turn defeated the provider's prefix cache. The only change in this run is that the +**system prompt stays fixed and the budget reminder is appended to the tail of the +conversation** (after the task text on the first turn, after the tool results +afterwards); everything else matches Experiment 2 exactly (max_turns 100, a 3420s +budget, no Harbor timeout overrides). + +| Item | This run | +| --- | --- | +| Job | `tb21-exp3-timebudget-tail-20260926` | +| Agent source | branch `experiment/max-turns-and-time-budget`, commit `83e5c064d`; wheel `0.8.1.dev37+g83e5c064d` | +| Everything else | same as Experiment 2: 3420s budget, at most 100 replies per task, no timeout override | + +**Result: both tasks passed.** + +| Task | Reward | Terminal state | Model replies | Duration | Cost (USD) | +| --- | ---: | --- | ---: | ---: | ---: | +| `caffe-cifar-10` | 1 | `completed` | 59 | 3333 s | 0.048236604 | +| `mteb-leaderboard` | 1 | `max_turns_exhausted` | 100 | 1365 s | 0.138716976 | + +**The caching fix is verified.** The two runs side by side (two tasks each): + +| Metric | Experiment 2 (system injection) | Experiment 3 (tail injection) | +| --- | ---: | ---: | +| Input tokens | 2,919,588 | **108,646** | +| Cache tokens | 1,408 | **8,410,880** | +| Total cost (USD) | 0.856 | **0.187** | + +The clearest evidence is the same-provider before/after: in Experiment 2 Parasail's +29 calls reported zero cache reads; in Experiment 3 Parasail served 157 of 159 +replies with about **53,572** cached tokens each. Going from zero to fifty-odd +thousand on the same provider is consistent with "fixed system prompt plus prefix +cache". + +**But convergence got weaker.** Experiment 2's `mteb-leaderboard` stopped on its +own at 50 replies (`completed`); in Experiment 3 it wrote the correct +`/app/result.txt` (reward still 1) but kept working to the 100-reply cap +(`max_turns_exhausted`) and never produced a closing summary. Either the tail +reminder is less salient than a system-prompt instruction, or this is run-to-run +noise — with one trial per run, this run cannot tell the two apart. + +**Code review and fixes.** An independent codex `/review` (against `main`) audited +this branch's implementation and raised three issues, now fixed in the next +section: + +- **[P1] The deadline was checked once per batch**: when a reply contains several + tool calls, the rest still run after the first exhausts the budget. Now the + deadline is **rechecked before every tool**. +- **[P2] Injected reminders were not persisted**: the budget reminder is + behavior-changing input but appeared in neither the Event Journal nor ATIF. A new + `input.injected` event and matching ATIF step were added. +- **[P2] The new outcome was not versioned**: a run ending in + `time_budget_exhausted` still claimed schema v3, which old v3 readers reject, + while the updated reader wrongly accepted it in historical v1/v2 records. + Following the `response_truncated` precedent, the **schema is bumped to v4**. + +**Evidence boundary.** Still one trial per task, with routing uncontrolled +(Experiment 3 sent 157 of 159 calls to Parasail). The cache difference has a clear +direction, but "weaker convergence" is only a signal. + +**Artifacts.** Raw trials are under `jobs/tb21-exp3-timebudget-tail-20260926/`, with +the experiment record under `jobs/tb21-exp3-timebudget-tail-20260926-record/`. +Version control stores the structured result +[`tb21-exp3-timebudget-tail-20260926.json`](../../../benchmarks/harbor/results/tb21-exp3-timebudget-tail-20260926.json) +and this summary. diff --git a/docs/dev_notes/zh-CN/0.8.x.md b/docs/dev_notes/zh-CN/0.8.x.md index 31727e1..131d1a2 100644 --- a/docs/dev_notes/zh-CN/0.8.x.md +++ b/docs/dev_notes/zh-CN/0.8.x.md @@ -293,1449 +293,3 @@ uv run pytest - Trajectory：11 个 steps，其中 10 个模型 steps。 - Tokens：49,878 input、42,240 cache、5,845 output。 - Cost：0.00222441 USD，10 次成本对账均首次成功。 - -### 批量运行 Terminal-Bench 并建立 baseline - -单题验收已经证明任务容器、agent、trajectory、费用对账与官方 verifier 的链路可用。 -下一步先扩大到 20 题，观察不同任务上的失败方式和实际消耗，再决定完整 89 题的 -baseline 运行预算。这 20 题用于小规模试跑，不把它的通过率当作完整 Terminal-Bench 2.1 分数。 - -#### 固定实验配置 - -本轮于 2026-09-06 准备，固定如下条件；后续比较 harness 改进时，应复用题目和运行 -限制，并分别记录新的 agent revision。 - -| 项目 | 本轮配置 | -| --- | --- | -| Harbor | 0.21.0，使用 `benchmarks/harbor/uv.lock` | -| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | -| Dataset | `terminal-bench/terminal-bench-2-1`，共 89 题 | -| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | -| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | -| API endpoint | `https://openrouter.ai/api`，使用 Anthropic Messages 接口 | -| Provider routing | 请求不额外指定供应商；账户路由设置也属于实验条件 | -| 试跑规模 | 数据集列表前 20 题，每题 1 次 | -| Environment | 本地 Docker，并发 2 | -| Agent limit | 每题最多 50 次模型回复 | -| Per-response limit | `MAX_TOKENS = 8192`；请求未显式设置 reasoning effort 或 temperature | -| Harbor retries | 0；该设置不关闭 Anthropic SDK 内部的请求重试 | -| Timeouts and resources | 保留题目默认配置 | - -Harbor 0.21.0 的 `--n-tasks 20` 在过滤后截取列表前 20 项，不是随机抽样。固定数据集 -hash 可以固定任务版本，但仍应保存实际选中的任务清单，后续可用重复的 -`--include-task-name` 精确选题。模型在 OpenRouter 上的供应商路由和服务端行为仍可能 -变化，固定 model slug 不等于完全确定性的实验。 - -宿主机 Harbor 使用 lockfile；容器内 `uv tool install` 只固定 agent 源码 revision， -没有锁定依赖解析。公开结果保存 Docker 版本及任务镜像的 RepoDigests，用于核对后续 -实验环境；宿主机硬件信息仅保存在本地记录中。 - -#### 运行方法 - -先确认 `docker info` 成功，再从仓库根目录执行。凭据通过环境变量提供，不写入 -命令参数或实验记录。`ANTHROPIC_MODEL` 会覆盖 adapter 从 `--model` 推导的模型名， -因此本轮显式移除它。 - -```bash -export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" -export ANTHROPIC_BASE_URL="https://openrouter.ai/api" -unset ANTHROPIC_MODEL - -BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" -BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" - -uv run --locked --project benchmarks/harbor harbor run \ - --dataset "$BENCH_DATASET" \ - --n-tasks 20 \ - --agent harbor_adapter:NanoPyCodeAgent \ - --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ - --agent-kwarg max_turns=50 \ - --model openrouter/deepseek/deepseek-v4-flash-0731 \ - --env docker \ - --n-concurrent 2 \ - --n-attempts 1 \ - --max-retries 0 \ - --job-name tb21-flash0731-pilot20-20260906 -``` - -如果凭据已经保存在本机 `~/.nanoPyCodeAgent/settings.json`，可在启动 Harbor 的 Python -进程中调用 `nanopycodeagent.settings.load_settings_env()`，随后移除 -`ANTHROPIC_MODEL`，再启动同一条 Harbor 命令。仅由 task 容器内的 CLI 读取配置不够： -宿主机配置文件不会自动挂载到容器，必须先让宿主机 Harbor 获得连接环境变量。本次 -实跑采用这一方式，通过 Herdr 在当前 tab 新建右侧 split 执行，保持原 pane 的焦点。 - -复跑时使用新的 `--job-name`，避免与既有实验目录混淆。完整 89 题运行时删除 -`--n-tasks 20`，并另取 baseline job 名，其余条件保持一致；这会重新跑完整集合， -小规模试跑费用另计。暂不执行完整集合或增加每题 attempts，先分析本轮结果。 - -#### 预算与记录计划 - -之前的证书任务实际消耗为 0.00222441 USD，但简单任务不能代表整套集合的平均费用。 -按 2026-09-06 查到的 [OpenRouter 模型价格](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) -和[供应商报价](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints)， -Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 tokens -0.04998／0.009996／0.09996 USD，DeepSeek 当时基础价为 -0.22／0.007／0.66 USD；路由、优惠和时段会影响实际报价。 - -如果每题累计输入 100 万、输出 3 万 tokens，输入缓存命中率 80%，20 题按上述两档 -价格估算约 0.4–1.4 USD；如果增至每题累计输入 300 万、输出 15 万 tokens，则约 -1.4–5 USD。它们是预算场景，不是实测均值或费用上限。50 轮限制也不是美元硬上限。 -这里只估算模型 API 费用，不含云容器、网络或本机运行成本。 - -本轮按以下顺序记录与复核： - -1. 保存 `jobs//config.json`、`lock.json`、任务 content hash 和 20 题清单。 -2. 保存每题 `result.json`、`agent/trajectory.json`、agent 文本日志和 verifier 日志。 -3. 分别统计 reward 为 1 的题数、reward 为 0 的题数、未产生评分的题数和基础设施 - 异常；报告通过题数／计划题数，不能静默缩小分母。 -4. 检查所有 trajectory 的 ATIF 校验、usage 与 cost 完整性，以及 Harbor 指标回填。 - 缺失或部分费用保持未知，已知小计不能当成完整总额。 -5. 记录总 input／cache／output tokens、实际 USD 费用、job 墙钟耗时和逐题耗时。 - Input 已包含 cache tokens，不再把两者相加。 -6. 对失败任务区分解答错误、轮数用尽、执行超时、API 错误、安装／镜像／verifier - 环境故障；保留首次结果，任何后续重跑另建记录。 -7. 用小规模试跑的实测消耗调整完整 89 题预算，再建立单轮 baseline。未来如要评估波动， - 另做多次重复实验，并保留每次结果，不按重跑后的最好成绩替换首次结果。 - -`/jobs/` 已被 Git 忽略。原始结果应另行归档，开发笔记保存可审查的汇总、逐题结果及 -产物位置；不能只留下本机目录名就认为 baseline 已进入版本管理。 - -#### 20 题小规模试跑的实际结果 - -本轮 job 为 `tb21-flash0731-pilot20-20260906`，20 题均已结束；完整数据见 -[机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用完整性；逐次费用回执保存在本地。 - -| 指标 | 实测结果 | -| --- | --- | -| 运行日期 | 2026-09-06 | -| Job 墙钟耗时 | 111 分 10 秒 | -| 通过／未通过／未评分 | 8 ／ 12 ／ 0 | -| 通过率（计划 20 题为分母） | 8 / 20 = 40% | -| 无 Harbor 异常通过 | 7 / 20 = 35% | -| Harbor 异常／自动重试 | 1 ／ 0 | -| 模型回复／工具调用 | 267 ／ 329 | -| Harbor 原始 input／cache／output tokens | 5,853,395 ／ 5,050,368 ／ 311,514 | -| 从最终 ATIF 重新汇总的 input／cache／output tokens | 6,207,708 ／ 5,362,944 ／ 331,041 | -| ATIF 文件校验 | 20 / 20 | -| Harbor 原始指标一致 | 19 / 20 | -| 补查前费用不完整的 trajectory | 13 / 20 | -| Harbor 原始 job 费用汇总 | 0.045695378 USD | -| 所有 trajectory 已知费用小计 | 0.11053603 USD | -| 独立补查后费用 | 0.119200332 USD（完整） | - -下表使用最终 ATIF 中的模型计量信息，费用采用独立补查后的值；原始 -`result.json` 和 trajectory 没有被改写。 - -| 任务 | Reward | 模型回复数 | 末次 stop reason | 费用（USD） | -| --- | ---: | ---: | --- | ---: | -| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | -| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | -| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | -| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | -| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | -| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | -| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | -| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | -| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | -| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | -| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | -| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | -| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | -| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | -| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | -| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | -| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | -| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | -| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | -| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | - -`pytorch-model-recovery` 的 reward=1 与 `AgentTimeoutError` 同时存在。因此 Harbor -原始通过率保留为 **8/20（40%）**，另报**无 Harbor 异常通过 7/20（35%）**，两者都 -以计划的 20 题为分母。这题不能作为正常完成的成功样本。 - -#### 本轮暴露的问题 - -1. **11 道未通过题的末次回复均为 `max_tokens`。** 当前每次回复最多 8192 tokens， - 部分回复的预算主要消耗在 thinking，尚未交付完整解答就结束。 - 当时 agent 将“不再请求工具”视为模型完成处理，未区分因预算耗尽而中断的回复， - 所以这些轨迹的终态仍为 `completed`。这说明需要显式 - 处理截断，并评估 reasoning／output 预算；增加上限后的通过率和费用需要另行实测。 -2. **`mteb-leaderboard` 用尽 50 次模型回复。** 终态 outcome 为 - `max_turns_exhausted`，末次回复仍要求调用工具，verifier 所需的 `/app/result.txt` - 尚未生成。这与单次回复截断是两种不同的预算限制。 -3. **超时后 agent 仍在运行，且发生工具参数异常。** 以 Harbor 开始执行 agent 为 - 起点，`pytorch-model-recovery` 在约第 900 秒达到限制，verifier 约第 901 秒开始； - 内部 journal 记录 agent 在约第 929 秒和第 939 秒又启动两次模型调用。最后一次 - `edit` 只有 `old_text`、`new_text`，缺少 `path`，在显示工具调用信息时触发 - `KeyError`；运行失败记录约在第 1063 秒落盘。 - 因而该异常发生在超时之后，不能用它解释之前的超时。运行与验证发生重叠，该题的 - reward 需要带着这一限制解读。 -4. **Harbor 原始汇总漏掉超时题的计量，费用也有延迟。** 超时采集时 trajectory - 被标记为 missing，最终目录中却已有有效 ATIF，且与独立备份一致；Harbor 的 - tokens 仍为 null，所以原始指标只有 19/20 与最终 ATIF 一致。13 题原始费用为 - partial，共 18 个 generation 在原生对账中未取得费用。独立补查同一 OpenRouter - generation 接口后，18 个回执均成功，**267 次已记录模型回复全部获得费用**。 - 原始 job 的 0.045695378 USD 不能作为总费用；完整实测为 **0.119200332 USD**。 - 两次超时后的调用也计入实际消耗。 - -完整 input 为 6,207,708，其中 cache 为 5,362,944；output 为 331,041。费用依据 -generation 的 `total_cost`，没有按展示价格反推，也不是整个 OpenRouter 账户余额的 -差额。20 题均有评分且 ATIF 校验通过；除上述超时外，Harbor 没有记录其他 trial 异常。 - -原始 job 位于 `jobs/tb21-flash0731-pilot20-20260906/`。本地归档为 -`jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`，包含任务清单、日志、轨迹、 -generation 费用明细、补查回执、Docker 镜像标识和一次性运行／汇总脚本。归档未上传， -其 SHA-256 见机器可读结果。`torch-pipeline-parallelism` 的两份原始日志含运行时 -API 凭据，原件权限已设为 0600，归档副本已脱敏；原件与脱敏副本的校验值保存在 -本地记录中。 - -版本管理仅保存精简的实验配置、逐题评分、token、费用、耗时和异常摘要。请求 ID、 -内部运行 ID、逐次账单回执、精确调用时间、宿主机硬件信息及完整对话日志留在本地。 -这一精简修改作用于最新版；之前已推送的提交仍包含对账元数据，未改写 Git 历史。 - -#### 下一轮计划与费用判断 - -先修复截断处理、缺失工具参数导致的崩溃、超时停止及 trajectory 采集，再完善延迟 -费用补查；这些修复在新 revision 中进行。本轮固定源码版本且没有中途修改 agent。 -随后使用相同 20 个任务 hash 和新的 job 名复跑，对比无异常通过率、终止原因、token -和费用；保留本轮作为修复前的小规模试跑基准。确认运行与计量可靠后，再运行完整 -89 题，并把结果单独记录为完整集合的 baseline。 - -本轮约 0.12 USD，机械地乘以 89/20 约为 0.53 USD，但 11 题提前截断，且题目取自 -列表前 20 项，这个外推不能作为修复后完整集合的预算。按前述每题 300 万 input、 -15 万 output、80% 输入缓存命中的场景，89 题约为 6–22 USD；仍需按当时供应商报价 -和修复后小规模试跑的消耗调整。本轮只执行了 20 题，完整 89 题尚未运行。 - -### 修复批量运行 Terminal-Bench 时遇到的问题 - -以下记录本轮问题的含义、实现与修复方向。“截断处理”“增加模型生成预算”和 -“缺失工具参数导致的崩溃”已实现修复；其余小节仍描述问题与拟议方向，不表示 -对应代码已修复。 - -#### 截断处理 - -原始试跑中，每次模型回复最多生成 8192 tokens。模型尚未输出完就用尽这次预算时，接口 -返回 `stop_reason="max_tokens"`，表示达到生成长度上限，被迫停止。例如，模型 -可能仍在分析如何修改代码，后面的工具调用或完整解答还没有生成，预算就耗尽了。 -本轮部分回复的预算主要消耗在 thinking；11 道未通过题的末次回复均为 `max_tokens`。 - -过去 agent 将“不再请求工具”视为模型完成处理，但生成预算耗尽也会让回复停在 -没有工具调用的位置。结果是任务尚未做完，轨迹却将它记为 `completed`。判断运行 -结果时，需要区分模型正常结束、任务轮数用尽和单次回复被截断；它们说明的是不同 -的停止原因。即使模型正常结束，解答是否正确仍要由 verifier 判断。 - -本次先让运行结果可信：识别截断、保留证据，并明确告知任务尚未完成。遇到截断后 -停止本次运行；增加生成预算和自动恢复作为后续能力，分别评估效果与额外消耗。 - -**2026-09-07 实现：识别截断并停止。** - -截断后的停止原因记录为 `response_truncated`。即使它恰好发生在最后一轮，也保留 -这个原因，让使用者知道最后一次回复本身没有生成完。agent 不再发起新的模型调用， -并跳过该回复中的全部工具调用，包括参数看似完整的调用，避免根据未完成的回复 -执行操作。 - -已经生成的文本、思考内容、工具参数和用量仍保存在执行记录中，费用继续补查， -便于分析预算花在哪里、任务停在哪一步。未执行的工具不会被记成已执行，也不会 -产生虚构的结果。分析结果时，应把“运行是否正常结束”和“任务是否完成”分开判断。 - -使用者会看到明确的截断提示，已输出文字保持可见。Headless 仍以 `0` 退出，由 -verifier 判断任务是否通过。交互模式回到输入提示，用户可以继续发消息；后续对话 -保留已生成文字和截断说明，移除未执行的工具请求与不完整的思考内容，避免下次 -请求带入无法衔接的历史。原始回复仍保留在执行记录中供追溯。 - -新增停止原因超出了旧记录协议允许的范围，因此新记录升级为 Journal v2；已有 v1 -记录仍可读取和转换，公开轨迹继续使用 ATIF-v1.7。模型生成到达上限与日志为控制 -体积而缩短内容是两件独立的事。具体字段、事件顺序和兼容规则见 -[Event Journal v2](../../dev_docs/zh-CN/event-journal-protocol-v2.md)。 - -回归测试覆盖文本、仅 thinking、空回复、最后一轮截断、工具跳过、交互下一轮、 -usage 与费用保留，并通过真实 Anthropic SDK 的本地模拟 SSE 验证半截工具 JSON。 -旧 v1 与新 v2 截断轨迹均通过 Harbor 固定版本的 ATIF validator。测试没有调用付费 -模型；2026-09-07 实现时尚未重跑 benchmark，也没有将这些自动化测试解释为通过率 -提升。2026-09-09 的真实模型复跑记录如下。 - -从 `tb21-flash0731-pilot20-20260906` 中选择 `regex-log` 和 `write-compressor`。 -两题上次 reward 都为 0，末次回复都是 `max_tokens`，但旧轨迹将 outcome 记录为 -`completed`。本次同时检查官方 verifier 的评分，以及当前分支是否准确记录截断。 - -**测试方法与配置。** 在当前 Herdr tab 中向右新建 split，保留原 pane 的焦点， -从仓库根目录运行。两题并发执行，每题只尝试一次，原始 job 保持不变。 - -| 项目 | 本次配置 | -| --- | --- | -| Agent 分支／提交 | `fix/handle-truncated-model-responses` ／ `f7788616e11f3347300ac2fdef6c6400d4eb5611` | -| 容器中的包版本 | `0.8.1.dev11+gf7788616e` | -| Harbor | 0.21.0，通过 `uv run --locked --project benchmarks/harbor` 运行 | -| Dataset | `terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | -| Model／API endpoint | `openrouter/deepseek/deepseek-v4-flash-0731` ／ `https://openrouter.ai/api` | -| 生成限制 | 每次回复 8192 tokens；每题最多 50 次模型回复；未显式设置 reasoning effort 或 temperature | -| 环境与重试 | 本地 Docker，并发 2，每题 1 次尝试，Harbor `max_retries=0`；保留任务默认资源与超时 | -| Job | `tb21-truncation-rerun2-20260909-f778861` | - -两题沿用原始任务版本，并检查新旧 trial config 中的任务标识完全一致： - -| 任务 | Task hash | -| --- | --- | -| `terminal-bench/regex-log` | `sha256:802c16cfd132e6c457529cb864be5a757c1b23b6cadc57f2d01983cb0110292a` | -| `terminal-bench/write-compressor` | `sha256:d9ddd9a8e925e2c566b37b2492cbf995afecefe58874e4043ef78d7f3c892c7e` | - -当时分支尚未推送，容器无法按远端 Git revision 安装，因此先对当前提交执行 -`uv build --wheel`，逐文件核对 wheel 中的 Python 源码与该提交一致，再上传到各自 -容器的 `/tmp/`。测试通过临时适配器将安装来源改为这个本地 wheel；模型调用、 -工具执行和 trajectory 导出仍使用当前分支。 -wheel 的 SHA-256 为 -`3c4ebd9268680ba0798ccbc44e6e56ee91424c5773399171050b7ac24734e9e4`。 - -启动脚本先读取宿主机连接配置，移除 `ANTHROPIC_MODEL` -覆盖值并确认 endpoint 为 OpenRouter；凭据通过环境传递，不写入命令参数。 -Harbor 用以下过滤参数选择题目，名称必须包含 `terminal-bench/` 前缀： - -```bash ---include-task-name terminal-bench/regex-log \ ---include-task-name terminal-bench/write-compressor -``` - -本机实际运行入口如下。脚本内部准备 wheel、记录 manifest，再调用 Harbor；脚本与 -临时适配器都属于这次实验的本地产物，并非已提交的通用 benchmark 命令。 - -```bash -uv run --locked --project benchmarks/harbor python \ - jobs/tb21-truncation-rerun2-20260909-f778861-record/workflow/run.py -``` - -同日还运行了相关自动化回归测试，核心 54 项、Harbor 适配器 16 项全部通过： - -```bash -.venv/bin/python -m pytest -q \ - tests/test_truncation.py tests/test_event_journal.py tests/test_atif.py -benchmarks/harbor/.venv/bin/python -m pytest -q \ - -c benchmarks/harbor/pyproject.toml benchmarks/harbor/tests -``` - -**测试结果。** 两题均完成判题，无 Harbor 异常，通过率为 **0/2**；Harbor 报告的 -job 墙钟耗时约 **9 分 42 秒**。两题的截断状态都正确记录，但仍未完成任务。 - -| 任务 | 上次／本次 reward | 本次模型回复数 | 末次回复 tokens | 末次 stop reason | 本次 terminal outcome | Verifier 失败原因 | -| --- | --- | ---: | ---: | --- | --- | --- | -| `regex-log` | 0.0 ／ 0.0 | 1 | 8192 | `max_tokens` | `response_truncated` | 未生成 `/app/regex.txt`，1 项测试失败 | -| `write-compressor` | 0.0 ／ 0.0 | 2 | 8192 | `max_tokens` | `response_truncated` | 未生成 `/app/data.comp`，3 项测试失败 | - -| 任务 | Input tokens | Output tokens | 费用（USD） | -| --- | ---: | ---: | ---: | -| `regex-log` | 1678 | 8192 | 0.00158363 | -| `write-compressor` | 3927 | 8303 | 0.001742255 | -| 合计 | 5605 | 16495 | 0.003325885 | - -费用来自最终 trajectory 中的实际计量，两题费用均完整。两份 ATIF 都通过 Harbor -validator，启动版本包含 `f778861`，日志都有明确的截断提示，末次回复后没有继续 -调用模型。两题末次回复均无工具调用；半截工具参数的跳过与后续历史处理由上述 -自动化测试覆盖，未在这两次实测中触发。Harbor 正常结束且退出码为 0，不代表题目 -通过，评分仍以 verifier 的 reward 为准。 - -这次复跑验证了“识别截断并停止”的行为；它没有提高生成上限，也没有自动续写， -两题仍在交付所需文件前耗尽单次预算。这两次结果不能说明增加预算或自动续写后的 -通过率，后续需要分别实验。 - -原始 trial 日志、trajectory 与 verifier 输出保存在 -`jobs/tb21-truncation-rerun2-20260909-f778861/`。对应的 -`jobs/tb21-truncation-rerun2-20260909-f778861-record/` 保存 `manifest.json`、 -`summary.json`、`results.en.md`、wheel 和 `workflow/` 下的运行、临时安装与汇总脚本。 -这些目录被 Git 忽略，未上传；本文保存版本、方法与结果汇总，不将本地产物视为 -已经进入版本管理的文件。以后重跑须使用新的 job 名，保留这次 0/2 的原始结果。 - -#### 增加模型生成预算 - -**已实现单次生成预算配置，并将默认值从 8192 提高到 32768 进行试用。** -这里的预算指**单次模型回复的生成 token 上限**，不是整题累计 token 或上下文窗口。 -**完整轨迹实验中，`regex-log` 在 32768 下两次通过；`write-compressor` 在 -32768 下两次截断，09-12 显式改用 65536 重跑后通过，最终文件 2263 字节。** -但这次成功运行的最大单轮输出只有 **32073**，未实际用到 32768 以上的输出空间， -不能单凭它证明预算翻倍导致通过。解题耗时 **1976.4 秒**，900 秒检查点仍无目标 -文件，因此这次执行确实需要延长时限。此前的安装失败、服务中断及超时均在下文保留。 - -**其余 18 题在 65536 下首轮通过 5/18（同组 8192 基线为 8/18）；针对 4 题追加 -5 次补跑，没有新增通过。** 这批实验未出现 `max_tokens` 或 3600 秒解题时限终止， -还暴露了 50 轮限制、缺失或不完整工具参数、环境故障；不能把失败统一归因于 thinking -预算，也不能据此认定提高预算改善或降低了通过率。 - -**调研结论：32768、65536 适合作为下一轮实验的候选档位，但公开资料没有给出一个 -可以直接照搬的统一默认值。** TB 2.1 已有明确采用这两个值的评测；code agent 则 -既有约 32k／64k 的默认设置，也有按模型决定或不显式设置的实现。下面分别记录 -评测参数和产品默认行为，避免把模型能力上限当成实际请求参数。 - -**其他 agent／模型跑 Terminal-Bench 时设置多少。** 以下为 2026-09-09 查到的 -第一方评测说明，主看本项目使用的 **TB 2.1**；TB 2.0 仅作补充。数字是发布方 -披露的单次生成上限，不是整道题累计消耗，也不是对其线上请求的独立抓包验证。 - -| Benchmark | 模型与 agent | 披露的单次生成上限 | 影响比较的条件与来源 | -| --- | --- | --- | --- | -| **TB 2.1** | GLM-5.3、GLM-5.3-Flash／Claude Code **2.1.207** | **65536**（`max_new_tokens`） | Z.ai 披露 `temperature=1.0`、`top_p=1`、6 小时 timeout。[GLM-5.3 说明](https://huggingface.co/zai-org/GLM-5.3/blob/main/README.md#footnotes)、[Flash 说明](https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/README.md#footnotes) | -| **TB 2.1** | GLM-5.3-Flash-NVFP4／Terminus-2 | **32768**（`max_tokens`） | RadixArk 的量化版本评测，effort 为 `max`；**关闭了任务 deadline**，存在无法运行及未完成的 trial，不能视为本项目同条件的通过率对照。[评测说明](https://huggingface.co/RadixArk/GLM-5.3-Flash-NVFP4#evaluation) | -| TB 2.0（补充） | Qwen3.6-27B／Harbor + Terminus-2 | **80K**（`max_tokens`，保留原文单位） | Qwen 披露 256K context、3 小时 timeout、32 CPU／48 GB RAM，取 5 次运行平均。[模型卡脚注](https://huggingface.co/Qwen/Qwen3.6-27B#evaluation) | -| TB 2.0（补充） | Kimi-K2.6／Tinker Cookbook 的简化 agent | **8192**（`max_tokens`） | Thinking Machines 的实验使用 32K context、200 轮、无上下文压缩；说明中记录了大量上下文超限。它证明 8192 也被实际采用过，不代表这一预算适合本项目。[实验配置与结果](https://github.com/thinking-machines-lab/tinker-cookbook/blob/main/tinker_cookbook/recipes/harbor_rl/README.md#evaluation) | - -与本项目更接近的 DeepSeek-V4，技术报告披露了代码 agent 使用内部 bash + file-edit -harness、最多 500 步和 512K **上下文**，但该段没有披露单次 `max_tokens`，评测 -还是 TB 2.0。因此，不能把 512K 当生成预算，也不能用它确认本项目 -`deepseek-v4-flash-0731` 在 TB 2.1 上应设置多少。[报告 §5.3.1](https://arxiv.org/html/2606.19348v1#S5.SS3.SSS1) - -**其他 code agent 的默认值。** 默认值必须同时注明版本、模型和配置路径。 -下面的源码链接固定到本次检查的 commit；官方文档按 2026-09-09 查询结果记录。 - -| Agent／检查范围 | 未主动覆盖时的行为 | 依据与边界 | -| --- | --- | --- | -| Claude Code／Opus 4.6 | **64k** | **v2.1.77 发布说明**明确将该模型的默认值提高到 64k；同条中的 128k 是可提高到的上界，不能当成默认值，也不能将该版本结论推广到所有模型。[发布说明](https://github.com/anthropics/claude-code/releases/tag/v2.1.77) | -| Claude Code／无法识别的模型 ID，例如网关自定义名称 | **32000** | 当前官方文档明确列出这个 fallback；可用 `CLAUDE_CODE_MAX_OUTPUT_TOKENS` 覆盖，已识别模型的默认值和上界因模型而异。[环境变量说明](https://code.claude.com/docs/en/env-vars#variables) | -| OpenCode／`830d5eb` | **`min(模型输出上限, 32000)`**；模型输出上限缺失或为 0 时回退到 32000 | 默认请求准备路径使用这一计算结果；环境变量和插件可覆盖。[计算函数](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/provider/transform.ts#L1468)、[请求构造](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/session/llm/request.ts#L117) | -| pi／`acaa253` 的普通模型请求路径 | **按模型的 `maxTokens` 决定**，再按剩余上下文收紧 | 当前 `buildBaseOptions` 采用 `options.maxTokens ?? model.maxTokens`，并为上下文估算留出 4096 tokens；不是所有模型共用一个固定数值。thinking 的预算还会经过对应 provider 的处理。[源码](https://github.com/earendil-works/pi/blob/acaa253cc8e3f159e6100b6f3874861b1f0bfc99/packages/ai/src/api/simple-options.ts#L11) | -| mini-swe-agent／`04d809c` 的默认配置与 LiteLLM 路径 | **不显式设置统一的 `max_tokens`** | 默认 YAML 未指定，模型层透传 `model_kwargs`；实际值由选用配置、LiteLLM 和服务端共同决定，省略参数不等于无限生成。[默认配置](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/config/default.yaml)、[模型调用](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/models/litellm_model.py#L64) | -| Codex CLI／官方配置参考 | **本次未确认统一的默认数字** | 所查官方配置参考未给出单次生成上限的默认值；不能拿 API 模型页上的最大输出能力代替 CLI 的实际默认设置。[配置参考](https://learn.chatgpt.com/docs/config-file/config-reference) | - -这些数字也提醒我们：**32000 与 32768 要分别记录**，不能都简写成“32k”后当成同一 -配置；只披露 `64k`、`80K` 的来源则保留原文单位。thinking 上限、`high`／`max` -这类 effort 档位也要与总生成上限分开记录；本次尚未确定独立 thinking 参数的设计。 - -**实现与自动化验收。** 实现提交为 `7a610cbf8`。配置入口采用 `--max-tokens`、 -`ANTHROPIC_MAX_TOKENS` 和现有 `settings.json` 的 `env`，优先级为 -**CLI > 环境变量 > 配置文件 > 32768**；交互和 headless 都生效，启动时解析一次， -每轮请求使用同一值。最终值必须是正整数，非法值在请求前以退出码 `2` 报错。 -配置文件沿用现有 `env` 字符串规则，例如 `"ANTHROPIC_MAX_TOKENS": "32768"`。 - -Harbor 支持 `--agent-kwarg max_tokens=N` 和 `ANTHROPIC_MAX_TOKENS` 透传；省略 -kwarg 时不强制插入 CLI 默认值，以便环境配置及所安装版本的默认值生效。 -启动提示、Journal 的 `run.started.max_tokens` 和 ATIF 的 `agent.extra.max_tokens` -都记录生效上限，实际 usage 另行记录。旧 Journal 缺少此字段时仍可转换，ATIF 不会 -为历史记录补写一个推测的预算。 - -```bash -nanoPyCodeAgent --max-tokens 65536 -p "fix the failing tests" -# Harbor: add --agent-kwarg max_tokens=65536 to the run command -``` - -核心 **242 项测试**、Harbor adapter **22 项测试**通过，覆盖配置优先级、交互与 -headless 每轮请求的预算、非法值提前失败、记录与实际 usage 分离，以及真实 SDK -请求序列化。**65536 的真实模型结果见下文 09-12 实验；参数透传通过不等于效果归因成立。** - -**即使增大上限，也必须保留 `stop_reason="max_tokens"` 的处理。** 任何有限预算 -都有可能耗尽。提高上限可以给模型更多生成空间；现有截断处理负责准确识别停止原因。 -上次两题均用尽 8192 tokens，为评估更高预算提供了对照，但不能保证提高后一定通过。 - -增大预算后的截断回归也已通过：遇到 `max_tokens` 仍记录 `response_truncated`， -保留 usage 与费用，跳过该回复中的工具调用。本次没有新增独立 thinking 参数或 -自动续写。 - -**32768 的真实模型实验。** 使用新 job -`tb21-budget32768-20260909-7a610cb`，以 -`tb21-truncation-rerun2-20260909-f778861` 的 8192 结果作为对照。保留同一 -TB 2.1 dataset、两题 hash、OpenRouter 的 `deepseek/deepseek-v4-flash-0731`、 -`max_turns=50`、每题 900 秒原生 timeout、并发 2、每题 1 次且不重试。 -从实现提交构建本地 wheel，并逐个核对包内 Python 源文件与该提交一致。 -实验清除 `ANTHROPIC_MAX_TOKENS`，也不传 `--max-tokens`，验证内置默认值。 -`regex-log` 首次在安装阶段超过默认 360 秒，尚未进入模型执行，也没有 verifier -评分；该记录保留在原 job。另建 -`tb21-budget32768-regex-setup-retry-20260909-7a610cb` 仅补跑这题，将安装 timeout -放大为 1080 秒（`--agent-setup-timeout-multiplier 3`），解题 timeout 仍为 900 秒。 -`write-compressor` 首次进入模型执行，第一轮正常完成工具调用；整次执行约 421 秒 -时，第二轮收到服务端 `stream closed before completion`，Harbor 记录 `UnknownApiError`， -verifier reward 为 0。它没有返回 `max_tokens`，不能作为预算耗尽的证据。 -已完成首轮的 usage 为 input 1528／output 103，费用为 $0.00011786；中断调用的 -完整 usage 与费用未知，不能把这一已知小计当成整题总费用。 - -为排除一次性服务故障，再以相同模型、预算和解题时限补跑一次，保存为 -`tb21-budget32768-compressor-api-retry-20260909-7a610cb`；安装窗口同样为 1080 秒。 -两次模型运行均保留，不以补跑覆盖首次失败。 - -**最终结果（2026-09-09 执行，09-10 核对归档）。** 下表的时间是 Harbor 记录的 -agent 执行时间，不含安装与 verifier；首次安装失败没有模型执行时间或评分。 - -| 题目 | 8192 基线 | 32768 首次运行 | 32768 补跑 | -| --- | --- | --- | --- | -| `regex-log` | reward **0**；`response_truncated`；187.3 秒 | `AgentSetupTimeoutError`；未调用模型，未评分 | reward **0**；`AgentTimeoutError`；900.9 秒 | -| `write-compressor` | reward **0**；`response_truncated`；304.4 秒 | reward **0**；`UnknownApiError`；421.4 秒 | reward **0**；`AgentTimeoutError`；900.9 秒 | - -两次补跑的启动日志均确认 `max tokens 32768`，agent 版本和题目 hash 与实验配置 -相符。verifier 分别报告 `/app/regex.txt` 和 `/app/data.comp` 不存在。 -包括首次服务端中断在内，共有 **3 次进入模型执行且获评分的运行，全部为 0**；另有 -1 次安装失败。没有挑选一次补跑结果来替换原始失败。 - -**完整用量与成本比较未能完成。** 两次超时均未导出最终 ATIF，Harbor 将 trajectory -记为 `missing`，input／output tokens 和 cost 都为 `null`。模型调用次数、末次 -`stop_reason` 和实际截断次数也无法从现存记录完整恢复，不能将缺失值写成 0。 -8192 基线的完整费用分别为 $0.00158363、$0.001742255；32768 仅有前述首次 -`write-compressor` 已完成首轮的 $0.00011786 小计，无法计算可靠的总费用增幅。 - -因此，**保留 32768 作为本轮试用默认值，但不将 09-09 的实验写成提高预算后的成功验收**。 -参数配置、请求透传和截断回归已验证；真实模型实验仍受服务端中断、任务 timeout -和超时后 trajectory 缺失影响。没有完整的末次回复，不能断言更高预算消除了 -`max_tokens` 截断，也不能由这两题推导整套 benchmark 的收益。 - -三个 job 的 wheel 完全相同，SHA-256 为 -`91996b1d4c70af8062318a38b3af9235c85d93c9685e10ac1323f3bef3625425`。 -原始日志、verifier 与异常保存在上述各 job；对应的 `-record/` 保存 manifest、 -wheel 和运行脚本。统一汇总位于 -`jobs/tb21-budget32768-20260909-7a610cb-record/summary.json`、`results.en.md` -及 `workflow/summarize.py`。这些 `jobs/` 产物被 Git 忽略，未上传；相关容器已清理。 - -**延长时限并保留完整 trajectory（2026-09-10）。** 新 job -`tb21-regex-3600s-trace-20260910-7a610cb` 的 `regex-log__2A9PJgL` 得到 -**reward 1.0**，官方 verifier 的 `test_regex_matches_dates` 通过。 -Harbor 记录 agent 执行 **580.4 秒**，不含安装和验证；因此，这次成功并未用到 -原 900 秒以外的时间，不能据此认定延长时限解决了此前超时。 - -继续使用上述同一 wheel、题目 hash、模型、内置 `max_tokens=32768` 和 50 轮上限， -单题、单次、不重试。将解题执行窗口改为 **3600 秒**，另留 120 秒用于中断和导出； -Harbor 外层窗口设为 3780 秒（`--agent-timeout-multiplier 4.2`），避免提前杀死 -清理过程。安装仍为 1080 秒，verifier 时限不变。临时诊断包装器将未截短的 Journal、 -HTTP 响应体、逐块到达时间和每 120 秒的线程栈直接写入持久日志目录；agent 源码、 -提示词和工具保持本次对照配置。本次自然完成，未触发中断或轨迹重建。 - -| 观测项 | 结果 | -| --- | --- | -| 模型回复／工具调用 | **5 轮／6 次**；前 4 轮为 `tool_use`，末轮为 `end_turn`，无 `max_tokens` 截断 | -| 模型累计耗时 | **567.3 秒**，占原生 run 时间约 **97.9%** | -| 首轮／后续各轮耗时 | **502.4 秒**／35.1、2.8、4.2、22.8 秒 | -| 本地工具累计耗时 | **0.023 秒** | -| 完整 usage | prompt **74650**（含 cache **53504**），completion **17264** | -| 完整费用 | **$0.005338074**，5 次调用均已完成费用核对 | - -**证据指向本次首轮持续生成 thinking，而非本地工具耗时。** 首轮在请求后约 -2.5 秒开始返回 thinking，持续到第 500.8 秒，第 501.7 秒才开始输出工具参数； -非空生成片段之间最长间隔仅 **3.4 秒**，没有观察到长时间断流。该轮保留了 -47007 个 thinking 字符，服务端报告 output **15123 tokens**，其中 -`output_tokens_details.thinking_tokens` 为 **12026**。字符数与服务端 token -口径不同，不能把两项 token 数的差额直接当成可见文本量。 - -完整性检查通过：**147 条 Journal 记录均未截短**，原生 ATIF 通过 Harbor schema -校验，并与 Journal 重建结果完全一致；5 个响应流都有 `message_stop`，5 次请求均 -实际发送 32768。记录使用单调时钟分析流式耗时，避免系统时钟微调影响。诊断包装器的 -正常结束、模拟卡住后中断、强制终止后恢复三个离线场景也均通过。 - -**仍未确定旧超时的真正原因。** 旧运行缺少完整回复与流式时间线，无法区分当时的 -长时间生成、服务端／网络停滞或不同解题路径。这次记录了实际容器依赖 -Anthropic SDK **1.4.0**、httpx **0.28.1**、httpx2 **2.12.0**；旧容器未保存对应 -版本，因此不能认为同一 wheel 就等于全部运行条件相同。该结果证明这一题在 32768 -下能够完成，但单次成功不能证明延长时限的因果收益，也不能推广到另一题或整套评测。 - -完整 ATIF 位于上述 job 的 `regex-log__2A9PJgL/agent/trajectory.json`；同级 -`journals/`、`http/`、`http-events.jsonl` 和 `python-stacks.log` 保留原始诊断证据。 -对应 `-record/` 下的 `results.en.md`、`summary.json`、`validation.json` 和 -`workflow/` 保存诊断报告、完整性核对和复现脚本。产物保留在本地 `jobs/`，被 Git -忽略且未上传；容器已清理，此前失败记录保留。 - -**同法补跑 `write-compressor`（2026-09-11）。** 新 job -`tb21-compressor-3600s-trace-20260911-7a610cb` 的 `write-compressor__GTmsjFV` -得到 **reward 0**。官方 verifier 的 3 项测试均因 `/app/data.comp` 不存在失败。 -Harbor 记录 agent 执行 **1449.1 秒**（约 24 分钟），无基础设施异常，原生 trajectory -完整导出；最终 outcome 为 **`response_truncated`**，没有触发 3600 秒超时。 - -方法沿用上次诊断：同一模型和 wheel、内置 `max_tokens=32768`、50 轮上限、单题 -单次且不重试；执行／清理／Harbor 外层窗口分别为 3600／120／3780 秒，安装仍为 -1080 秒。诊断包装器与上次逐字节一致，继续保存未截短的 Journal、原始响应流、 -到达时间和线程栈。题目 hash 与此前 `write-compressor` 基线一致。 - -| 模型回复 | 耗时 | output tokens | 服务端报告的 thinking tokens | 停止原因 | -| --- | --- | --- | --- | --- | -| 第 1 轮 | **5.8 秒** | 118 | 16 | `tool_use` | -| 第 2 轮 | **1414.2 秒** | **32768** | **32767** | **`max_tokens`** | - -**这次明确观察到“持续长思考 → 超过原时限 → 用尽生成预算 → 未交付文件”。** -第 1 轮执行了 `read` 和 `bash`，合计仅 **0.004 秒**。第 2 轮约 0.9 秒开始返回 -thinking，持续到第 1414.1 秒；非空生成片段最长间隔 **2.2 秒**，没有长时间断流 -迹象。该轮完整保留 **116281 个 thinking 字符**，没有可见文本或工具参数输出。 -当前 agent 在 `max_tokens` 后结束 run，未自动续写，因此没有后续机会生成文件。 - -在原时限附近的 **第 900.4 秒**，独立只读检查确认 `data.comp` 不存在，仅完成 -首轮回复和两次工具调用，第二轮仍在输出 thinking。这证明本次执行无法在原窗口内 -交付；延长窗口让它继续到生成预算耗尽，**单独延长时限没有解决本次失败**。 -这条时间线解释了本次过程，不能反推旧运行中缺失的轨迹，也不能将此前服务端中断 -与本次截断混为同一个原因。 - -完整 usage 为 prompt **3926**、cache **0**、completion **32886**，费用 -**$0.00615504**。累计输出大于 32768，是因为预算按单次回复计算。两轮费用均已 -核对；第二轮费用查询先返回 4 次 HTTP 404，第 5 次成功，该等待发生在回复截断后。 -Harbor 的安装和 verifier 分别耗时 143.9、226.2 秒，均不计入上述 agent 执行时间。 - -**完整轨迹与模型回复被截断是两回事。** 本次 **13 条 Journal 均未截短**，包括 -超过默认 10 万字符日志阈值的 thinking；原生 ATIF 通过 Harbor schema 校验，并与 -Journal 重建结果一致。两个响应流均完整接收、有 `message_stop`，实际请求均为 -32768。实际容器的 Anthropic SDK 为 **1.5.0**，上次 regex 诊断为 1.4.0；同一 -wheel 的依赖范围允许安装到新版本，不能把相同方法等同于全部依赖和后端条件固定。 - -完整 ATIF 位于该 job 的 `write-compressor__GTmsjFV/agent/trajectory.json`，同级 -目录保留 Journal、HTTP 原始响应与时间线、线程栈和运行信息。对应 `-record/` 下 -保存 `results.en.md`、`summary.json`、`validation.json`、`checkpoints/` 和复现 -脚本，其中包含原 900 秒截止点的文件检查。产物仍在本地、被 Git 忽略且未上传， -容器已清理；此前所有失败及 regex 通过记录保留。 - -**再次各跑一题（2026-09-11）。** 本轮串行运行 `regex-log`、`write-compressor`， -各一次、不自动重试；第一题验证及容器清理完成后才启动第二题。模型、wheel、题目 -hash、32768 内置预算、50 轮上限及 3600／120／3780 秒窗口均沿用前次诊断。 - -| 题目 | 前次完整诊断 | 本次重复实验 | -| --- | --- | --- | -| `regex-log` | reward **1**；580.4 秒；5 轮 | reward **1**；**631.2 秒**；9 轮，末轮 `end_turn` | -| `write-compressor` | reward **0**；1449.1 秒；第 2 轮 `max_tokens` | reward **0**；**582.1 秒**；第 3 轮 `max_tokens` | - -时间均为 Harbor 的 agent 执行时间，不含安装与 verifier。**本次两题都未用到 -原 900 秒以外的解题时间**，没有 deadline、强制终止或轨迹重建。 - -regex 首轮仍耗时约 **517.4 秒**，使用 16445 output tokens，其中服务端报告 -16346 为 thinking tokens；之后完成 8 次工具调用，累计仅 0.039 秒。9 轮回复 -均未截断，官方 `test_regex_matches_dates` 再次通过。 - -compressor 本次第 2 轮用 **172.5 秒、5463 output tokens** 正常完成工具调用， -走到了第 3 轮；第 3 轮再用 **394.8 秒**耗尽 **32768** 总生成预算，服务端报告 -其中 **28961** 为 thinking tokens。捕获流包含 **112785 个 thinking 字符**， -没有可见文本或工具参数；不能把 token 字段的差额推算为可见文本。该轮非空生成 -片段最长间隔 1.5 秒。全部 3 次工具调用合计约 0.004 秒，最终仍没有 `data.comp`， -官方 3 项测试因此失败，outcome 为 `response_truncated`。 - -| 题目 | 完整 prompt tokens（含 cache） | Cache tokens | Completion tokens | 完整费用 | -| --- | --- | --- | --- | --- | -| `regex-log` | 161870 | 137216 | 20400 | **$0.00720948** | -| `write-compressor` | 12704 | 1280 | 38335 | **$0.00764908** | - -两题的 **256／18 条 Journal 均未截短**；原生 ATIF 均通过 Harbor schema 校验， -并与 Journal 重建结果一致。全部 12 个模型响应流均完整接收、有 `message_stop`， -请求实际发送 32768，所有调用费用均已核对。安装分别耗时 833.6／448.1 秒， -verifier 分别耗时 114.9／264.6 秒，均单列而不混入解题时间。 - -**对照中的两次 regex 均通过，两次 compressor 均耗尽生成预算，但耗时和停止轮次 -明显不同。** 本次 compressor 在 900 秒以内就失败，进一步确认这一次的停止条件是 -单次生成上限。两次观察不足以断言以后必过／必败，也没有验证更高预算必定有效。 -本轮两容器均记录 Anthropic SDK 1.5.0，其余已记录包版本一致；前次 regex 为 1.4.0。 -采样、缓存和后端条件没有固定，不能将时间差异直接归因于某一个因素。 - -本轮 job 分别为 `tb21-regex-3600s-trace-repeat2-20260911-7a610cb` 和 -`tb21-compressor-3600s-trace-repeat2-20260911-7a610cb`，trial 分别为 -`regex-log__NmpJwcQ`、`write-compressor__NqmJQWm`；各自 `agent/trajectory.json` -及同级 Journal／HTTP／线程栈保留完整证据。各 `-record/` 保存报告和复现脚本， -统一对照在 `jobs/tb21-3600s-trace-repeat2-20260911-7a610cb-record/` 的 -`results.en.md` 与 `comparison.json`。原始产物仍在本地、被 Git 忽略且未上传， -两容器已清理，此前记录全部保留。 - -**65536 重跑 compressor（2026-09-12）。** 本次显式传入 -`--agent-kwarg max_tokens=65536`，其余沿用前次 compressor 的模型、wheel、题目 -hash、50 轮及 3600／120／3780 秒窗口；单题一次、不自动重试，项目默认值仍为 -32768。最终 **reward 1，官方 3 项测试全部通过**：`data.comp` 为 **2263 字节**， -解压内容与原文件一致。 - -| 项目 | 本次结果 | -| --- | --- | -| 解题时间 | **1976.4 秒**；安装 278.2 秒、verifier 169.1 秒另计 | -| 模型回复／工具调用 | **32 轮／32 次**；末轮 `end_turn`，outcome `completed` | -| 最大单轮输出 | **32073 tokens**；所有请求实际发送 65536，无截断 | -| 完整 prompt／cache／completion tokens | **1528746／1471744／59563**；prompt 含 cache | -| 完整费用 | **$0.018819072**，32 次调用费用全部核对完成 | - -主要耗时仍是第 2 轮：**1179.7 秒**、32073 output tokens，其中服务端报告 -27900 thinking tokens；随后正常返回 `tool_use`。完整流含 108811 个 thinking -字符，非空生成片段最长间隔 2.2 秒。全部工具累计 27.3 秒。900.4 秒检查点尚无 -目标文件，1299.7 秒时为 2727 字节、1449.9 秒时为 2388 字节，最终再缩至 2263。 - -**本次证明“65536 配置下重跑通过”，但没有证明“32768 一定不够”。** 所有单轮 -输出都低于 32768，采样、缓存和后端未固定，不能把通过直接归因于翻倍预算。 -相比之下，本次轨迹确实用到了原 900 秒以外的时间。运行环境记录的 SDK 1.5.0 -及其他依赖版本与前次 compressor 一致；没有 deadline、强杀或轨迹重建。 - -498 条 Journal 均未截短，原生 ATIF 通过 schema 校验并与 Journal 投影一致。 -32 个模型响应流均完整接收并含 `message_stop`；一次费用查询 GET 在响应头前 -`ConnectError`，重试后成功，未中断模型回复。原分析器对“所有 HTTP 请求均收到 -完整响应”的检查因此为 false，`validation-notes.json` 保留并解释该区别。 - -job 为 `tb21-compressor-65536-3600s-trace-20260912-7a610cb`，trial 为 -`write-compressor__hzfqDFs`；完整 `agent/trajectory.json`、Journal、HTTP 流与 -时间线均保留。对应 `-record/` 保存报告、校验、检查点和复现脚本，`task-output/` -另存最终压缩文件及输入／解压器源码和 hash。产物仍在本地、被 Git 忽略且未上传， -容器已清理，先前结果全部保留。 - -**65536 扩展到其余 18 题（2026-09-12～13）。** 首轮耗时约 2 小时 57 分， -结果为 **5 题通过、8 题 reward 0、5 题未评分**，即 5/18（27.8%）；同组 -8192 基线为 8/18。通过的是 `pypi-server`、`openssl-selfsigned-cert`、 -`log-summary-date-ranges`、`pytorch-model-recovery` 和 `merge-diff-arc-agi-task`。 -随后 4 题共补跑 5 次，均未新增通过。首轮与补跑分别记录，保留所有失败。 - -继续使用 `7a610cb` 的同一已校验 wheel、DeepSeek V4 Flash 0731／OpenRouter、 -相同题目 hash；18 题的缓存镜像 digest 也与基线一致。显式设 `max_tokens=65536`、 -最多 50 轮、并发 2；解题 3600 秒、收尾 120 秒、Harbor 外层 3780 秒、安装 -1080 秒，verifier 保留题目原时限。每题每个 job 一次，不自动重试整题。 -**项目默认值仍为 32768，65536 是本次实验覆盖值。** - -表中 `—` 表示未得到官方评分，不计作通过；reward 0 也可能来自基础设施或记录器 -故障，需结合原因判断。 - -| 题目 | 8192 基线 | 65536 首轮 | 主要结果 | -| --- | ---: | ---: | --- | -| `torch-tensor-parallelism` | 0 | — | 首个响应流中断；verifier 下载 Torch／CUDA 依赖超时 | -| `schemelike-metacircular-eval` | 0 | 0 | 第 19 轮响应流中断；解释器仅通过 4/63 子例 | -| `kv-store-grpc` | 1 | 0 | `SetValRequest` 缺少要求的 `value` 字段；通过 5/7 测试，服务存活检查通过 | -| `pypi-server` | 1 | 1 | 本地包安装及 API 测试通过 | -| `dna-assembly` | 0 | — | 安装 uv 时 TLS 下载失败，未调用模型 | -| `torch-pipeline-parallelism` | 0 | — | 耗尽 50 轮；verifier 下载依赖超时，无功能测试结果 | -| `qemu-alpine-ssh` | 1 | — | 安装依赖时 APT 包返回 404，未调用模型 | -| `openssl-selfsigned-cert` | 1 | 1 | 6/6 测试通过 | -| `regex-chess` | 0 | 0 | 耗尽 50 轮；初始局面生成非法后继，仅通过长度检查（1/4） | -| `log-summary-date-ranges` | 1 | 1 | 2/2 测试通过 | -| `model-extraction-relu-logits` | 0 | — | 获取 uv 安装脚本时 TLS 失败，未调用模型 | -| `path-tracing` | 0 | 0 | 耗尽 50 轮，缺少 `image.c`；0/5 测试通过 | -| `caffe-cifar-10` | 0 | 0 | 耗尽 50 轮；末次构建缺少 `g++`，二进制及模型未生成；仅通过 prototxt 检查（1/6） | -| `mteb-leaderboard` | 0 | 0 | 耗尽 50 轮，缺少 `result.txt`；0/2 测试通过 | -| `llm-inference-batching-scheduler` | 1 | 0 | 记录器在约 120 秒定时转储栈时 SIGSEGV，计划文件缺失 | -| `pytorch-model-recovery` | 1 | 1 | 5/5 测试通过 | -| `circuit-fibsqrt` | 0 | 0 | 电路计算错误；空 `bash` 参数触发原生 `KeyError('command')`，中止修复；通过 2/3 测试 | -| `merge-diff-arc-agi-task` | 1 | 1 | 5/5 测试通过 | - -**补跑只用于排除已确认的环境／记录器问题，结果不覆盖首轮。** 3 个安装失败任务 -先用缓存 uv 和 wheel 绕过下载／APT 故障；scheduler 单独移除定时栈转储后再跑。 -缓存安装方案遗漏了 DNA 镜像需要的系统 CA 证书，因此 DNA 又修正安装步骤重跑一次。 - -| 题目 | 追加次数 | 补跑结果 | -| --- | ---: | --- | -| `dna-assembly` | 2 | 第一次因上述 CA 遗漏，在收到响应头前连接失败，不能评价模型；恢复原有全部系统依赖后，完成 3 轮，但不完整 `bash` 参数触发 `KeyError('command')`，缺少 `primers.fasta`，0/1 测试通过 | -| `qemu-alpine-ssh` | 1 | 完成 3 轮后，不完整 `bash` 参数触发 `KeyError('command')`；verifier 又遇 APT 404，pytest 未运行，脚本虽记 reward 0，不能视为功能测试失败 | -| `model-extraction-relu-logits` | 1 | 正常完成 14 轮，但恢复矩阵的第 11 行不匹配，官方测试失败 | -| `llm-inference-batching-scheduler` | 1 | 无 SIGSEGV；完成 15 轮后，不完整 `write` 参数触发 `KeyError('content')`，计划文件缺失，仅通过输入完整性检查（1/6） | - -记录器和安装方法的差异也纳入证据。18 题开跑前已修正 supervisor 的正常退出清理： -保留 agent 启动的后台服务供 verifier 使用，仅在 deadline 时清理进程组；离线服务 -测试通过，`kv-store-grpc` 的服务存活测试也通过。首轮 scheduler 的系统 core 元数据、 -同镜像 ELF 与反汇编定位到定时栈转储中的 `PyCode_Addr2Line+11`；该调用路径与 -[CPython 定时转储实现](https://github.com/python/cpython/blob/v3.13.7/Modules/faulthandler.c#L537) -及[栈帧转储实现](https://github.com/python/cpython/blob/v3.13.7/Python/traceback.c#L842) -一致，但未确定底层内存错误机制。补跑保留原安装方法，只去掉定时转储，未再崩溃。 -DNA 则用同镜像、不带密钥的公开 HTTPS 请求验证：缺 CA 时证书验证失败，补装后 -返回 HTTP 200；修正后的正式补跑恢复 `curl`、`bash`、`git`、`python3` 和 CA -证书，再安装缓存 uv／wheel。这两类诊断错误不能算作模型预算不足。 - -**对生成预算的直接结论：继续加大上限不能统一解决当前失败。** - -- 所有本次 65536 运行均未报告 `max_tokens`，也没有触及 3600 秒解题时限。 - 实际单轮超过 32768 的只有 Scheme（38496）、regex-chess（60483）和修正后 - 的 DNA（35730），三题仍未通过。通过的 compressor 峰值为 32073，其余通过题 - 均不超过 6015。 -- pipeline、regex-chess、path-tracing、Caffe、MTEB 首轮耗尽 **50 轮**；这是 - 总调用轮数限制，与单次生成预算不同。 -- circuit、QEMU、scheduler 补跑和 DNA 修正补跑暴露了另一项 agent 健壮性问题： - 响应流完整结束于 `tool_use`，却给出缺失或不完整的工具参数，SDK 得到空字典； - 原生渲染器直接读取必需字段而抛出 `KeyError`。其中 circuit 的原始 JSON 是 - `{}`，QEMU／scheduler 是 `{`，DNA 是未闭合字符串，不能全部称为合法空 JSON。 - 下一步应单独处理参数校验和错误反馈，使 agent 有机会修复；本次没有修改这部分实现。 - -这些是诊断观察，**不是只改变预算的受控实验**：8192 基线源码为 `2b83097`， -使用原题目时限，依赖、采样、缓存和后端也未固定。因此既不能从 8/18 → 5/18 -认定增大预算有害，也不能把 compressor 的一次通过归功于预算翻倍。`regex-log` -此前两次通过使用的是 32768，本次未在 65536 下重跑；不把这些不同条件拼成统一的 -20 题通过率。 - -其余 18 题及 5 次补跑的已核对费用下界为 **$0.261769566**；加上 compressor， -本次 24 次尝试合计至少 **$0.280588638**。tensor-parallelism 的费用查询三次 -返回 404，完整总费用和 token 用量仍未知；中断的 Scheme／scheduler 调用已单独 -补查账单，不能用账单修补缺失的原生 usage，也不把未知值当作 0。 - -[结构化实验结果](../../../benchmarks/harbor/results/tb21-generation-budget-65536-20260912.json) -收录配置、源码／wheel hash、逐题结果、费用边界及各 job／trajectory 的本地路径。 -24 次尝试覆盖 19 题：**20 份原生 ATIF、1 份明确标记的崩溃后重建 ATIF、3 次 -安装未完成而无轨迹**；所有现存 ATIF 均通过 schema 校验，原生轨迹与完整 Journal -投影一致，无 Journal 字符串截短。实际收到的 HTTP 流（含中断流）、时间线、运行时 -版本、校验报告及诊断脚本均保留在 `jobs/`；主记录目录为 -`tb21-remaining18-65536-3600s-trace-20260912-7a610cb-record`，其中 -`comparison.json` 索引全部分组，`results.en.md` 记录首轮 18 题。 -原始轨迹被 Git 忽略且未上传，所有实验及诊断容器已清理。 - -#### 截断后自动续写 - -当前收到 `max_tokens` 就结束本次 run，即使还有剩余模型轮数也 -不会继续。新需求是在配置允许且仍有剩余预算时，由 agent 自动发起后续模型调用， -继续完成原任务，使 headless 运行不必依赖用户手动发送“继续”。 - -预期行为与约束： - -- 提供自动续写的配置开关及有限的续写次数上限。每次后续模型回复计入同一次 run - 的 `max_turns`，不能通过恢复流程重置轮数；默认开关、次数与其他预算限制待设计。 -- 保留已经成功执行的工具结果和任务上下文，为后续请求构造有效历史。截断回复中的 - 工具调用仍不执行，不把半截参数或无匹配结果的工具调用直接重放；只有 thinking、 - 没有可见文本的截断也需要有明确恢复方式。具体续写提示和请求构造方式待设计。 -- 在 Journal／ATIF 中保留每一次 `max_tokens` 的事实，记录后续尝试及全部 usage、 - 费用。继续生成期间不提前发出 run 终态；最终正常结束、达到续写限制、轮数耗尽或 - 请求失败时，准确记录原因。恢复后的终态规则与协议表达需要一并设计。 -- 达到配置上限、收到取消或发生无法恢复的错误时停止，并给出明确诊断，避免无限 - 续写。自动续写能继续处理任务，是否做对仍由 verifier 判断。 - -自动化验收至少覆盖：截断后继续并完成、连续截断后达到限制、剩余轮数耗尽、只有 -thinking 的回复，以及截断工具参数不被执行或错误重放。真实模型验收使用上述两题 -和新的 job 名，在相同单次预算下比较关闭／开启续写的结果，再评估“增加预算＋ -自动续写”的组合；分别报告通过率、追加模型调用数、总 tokens、费用和耗时。 -自动续写尚未实现；前述 8192 下的 0/2 结果及提高预算后的实验均不属于自动续写验收。 - -#### 缺失工具参数导致的崩溃 - -模型调用工具时漏传了必填参数，agent 没有把它作为可反馈给模型的调用错误处理， -而是抛出 Python 异常，导致整次运行失败。本轮具体发生在 `pytorch-model-recovery` -任务的最后一次 `edit` 调用中。 - -[`edit` 工具定义](../../../src/nanopycodeagent/edit_tool.py) 要求三个必填参数： - -- `path`：修改哪个文件。 -- `old_text`：要替换的原文。 -- `new_text`：替换后的内容。 - -本次调用只有 `old_text` 和 `new_text`，缺少 `path`，相当于模型说明了如何替换 -文字，却没有指定文件。工具 schema 已经把 `path` 声明为必填，但本次收到的实际 -参数仍然缺失，因此程序需要在使用参数前检查。 - -这次错误发生在显示工具调用信息时，尚未执行文件修改。因为缺少目标路径，连展示 -这次调用的意图都触发了 `KeyError: 'path'`，导致整个任务退出。 -异常发生在 Harbor 超时之后，不能用它解释此前的超时。 - -需要同时覆盖预览和执行两处：打印预览时能容忍缺失参数；执行工具前校验参数， -缺少必填项时不执行该工具，而是返回明确的 `tool_result`，标记 `is_error=True`， -例如： - -```text -Missing required argument: path. Provide the file path and retry. -``` - -这样模型可以在后续轮次看到错误，补齐参数重新调用，agent 在剩余预算内继续工作。 -展示和执行两个环节都需要容忍不完整输入，否则只是把崩溃推迟到下一步。处理原则 -是把模型可纠正的参数错误反馈为工具结果，让任务继续，而不是直接结束整个任务； -其他工具也应遵循这一原则。 - -**2026-09-13 实现：工具参数校验与错误反馈。** 实现提交为 `27a9503`。 -`read`、`write`、`edit`、`bash` 在预览和执行前共用参数校验，检查必填字段、 -schema 中声明的字符串／整数／布尔类型，以及路径和命令中的 NUL；未知工具名 -也返回错误。参数不做隐式类型转换，例如 `replace_all="false"` 不会被当作真值， -布尔值也不能充当读取行号。schema 允许的额外字段、空文件内容和空替换文本仍可使用。 - -无效调用不进入工具实现，而是返回匹配原调用 ID 的 `tool_result`，设置 -`is_error=True` 并说明如何纠正。预览显示该调用未执行；同轮其他有效调用仍按顺序 -处理，已经成功的工具不会因为恢复而重新执行。下一次模型回复沿用原任务、历史 -和剩余轮数，不新增独立重试预算。工具内部意外抛出的编程错误仍向外传播，避免 -把所有 `KeyError` 都误报为模型参数错误。 - -同时检查响应流中实际收到的参数 JSON。SDK 会在流式接收期间解析部分 JSON；即使 -缺少结束括号时已经得到包含必填字段的字典，也不能据此执行命令。流完整结束于 -`tool_use`、但参数 JSON 不完整时，本次调用被拒绝并要求重发完整对象。Journal／ -ATIF 保留错误、原始参数 JSON，以及 SDK 已解析的输入；非对象输入在兼容现有 -object 字段约束的同时，另存为 `raw_input`。后续请求使用可发送的对象形式， -不会把半截 JSON 重放给接口。`max_tokens` 的原有处理保持不变：整条截断回复中的 -工具仍全部跳过，运行记录为 `response_truncated`，本次没有实现自动续写。 - -**自动化与独立审查。** 锁定依赖下的 276 项核心测试全部通过；临时环境固定为 -上一轮实跑报告的 Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0 和 -Pydantic 2.13.5，再跑同一核心套件也为 276 项通过。Harbor adapter 的 22 项 -测试通过。新测试覆盖错误参数不产生副作用、真实 SDK 接收不完整 JSON、错误 -反馈后纠正调用、同轮有效调用、交互／headless 模式、轮数上限与轨迹证据。 -通过 Herdr 新建 split，独立 reviewer 按 `review-agent` 技能审查完整差异， -结论为 **No findings**，并额外验证了真实 SDK 中混合调用的恢复和 Harbor 兼容性。 - -此外，将上一轮 circuit、QEMU、scheduler、DNA 的四份实际失败响应流原样交给 -SDK 1.5.0 离线重放，四次都把坏调用转为错误反馈，并在脚本提供的后续回复中 -完成一次有效调用、正常结束。该验证的工具执行被替身替代，没有付费模型调用， -证明同一失败输入的处理行为已经改变，不能当作四题真实解答通过。 - -**已知边界。** 本次恢复依赖 SDK 成功交付响应。非法转义和尾随逗号等部分 JSON -语法错误会先在 SDK 内部抛出异常，尚不能进入恢复流程；独立审查已确认主分支也 -存在这一限制。流中断、任务算法错误、轮数耗尽和评测环境故障也需要分别处理。 - -**实题复测设置。** 复测使用同一份已审查的 `27a9503` wheel，模型仍为 OpenRouter -上的 `deepseek/deepseek-v4-flash-0731`；原始 20 题的 task ref 和缓存镜像摘要 -均与 09-06 基线一致。每次回复显式设置 `max_tokens=65536`，每题最多 50 轮， -同时运行最多 2 题；项目默认值仍为 32768。agent 执行限时 3600 秒，收尾宽限 -120 秒，Harbor 外层限时 3780 秒，验证器保留各题原始时限。每题在每个 job 中 -只尝试一次，Harbor 自动重试为 0；额外补测使用独立 job，保留原失败记录。 - -09-13 首组 targeted5 中，QEMU 安装依赖遇到 APT 404，DNA 安装超过 1080 秒， -均未调用模型；PyTorch 在安装期间被用户暂停，circuit 尚未启动。后续仅将 -**安装时限**增加到 3600 秒，并对 HTTP 404 最多重试三次系统依赖安装。 -同组 scheduler 的 agent 已在暂停前正常结束，恢复任务后使用保留容器中的原始 -产物补跑官方验证：6/6 通过、reward 为 1，**没有新增模型调用**。这是暂停后 -补验证的结果，单独记录，不当作一次完整、不中断的 Harbor 运行。 - -09-14 targeted4 的 QEMU 安装仍遇到间歇性 404，因此另建补测：从原 APT 索引 -选中的官方地址下载同版本 20 个包，核对 SHA256 和大小，在 `apt-get update` -之后放入缓存，再执行原依赖安装。提前放入会被镜像的 APT 清理钩子删除；修正 -顺序后，原始镜像中的安装验证成功。包版本和软件源保持一致。 - -同一 targeted4 中，PyTorch 的 agent 正常结束，但官方验证器下载 Torch／CUDA -依赖超过原始 900 秒，未得到评分。为独立补测和后续完整 20 题准备了 uv 0.9.5 -缓存，依赖仍为官方脚本指定的 `pytest==8.4.1`、`torch==2.7.1` 和 -`pytest-json-ctrf==0.3.5`。缓存迁移到原始镜像后，31 个包离线安装成功，Torch -可正常导入；准备阶段没有运行题目测试。后续仅为 `pytorch-model-recovery` -预置这份缓存，官方验证脚本和 900 秒时限不变。这改变了缓存条件，不能与工具 -参数修复的效果混为一谈。 - -原始 8192 基线还使用不同源码和 agent 时限；上一轮 65536 的首批 18 题为 -5 题通过，之后的补测及 compressor 是独立尝试，`regex-log` 则仅在 32768 下 -复测过。因此分别比较原始 20 题和相同 18 题子集，不拼接不同设置生成通过率。 -采样、路由、缓存和服务端状态未受控；相同失败响应的离线重放、实跑中的错误 -恢复事件、最终题目评分，应作为三类不同证据解读。 - -**针对性复测结果（2026-09-14）。** 下表保留每题的历史报错来源和实际复测结果； -安装失败、验证超时及补测不相互覆盖，也不将这些不同条件合成一次正式的 5 题评分。 - -| 题目 | 历史参数崩溃 | 本次结果 | 本次观察到的参数反馈 | -| --- | --- | --- | --- | -| `dna-assembly` | `bash` 缺 `command`，原始 JSON 为未闭合字符串 | reward **0**，官方测试 **0/1**；50 次模型回复后 `max_turns_exhausted`，agent 执行 3533.7 秒 | 0 次 | -| `qemu-alpine-ssh` | `bash` 缺 `command`，原始 JSON 为 `{` | 安装失败单独保留；缓存补测 reward **1**、**1/1** 通过，48 次回复后正常结束 | 0 次 | -| `llm-inference-batching-scheduler` | `write` 缺 `content`，原始 JSON 为 `{` | 暂停前 47 次回复后正常结束；恢复后官方验证 reward **1**、**6/6** 通过，无新增模型调用 | **1 次**不完整 `bash` JSON 被拒绝，随后继续调用模型，并出现成功的 `bash` 调用 | -| `pytorch-model-recovery` | 8192 基线在超时后发生 `edit` 缺 `path` | 首次 agent 正常结束，验证下载超时未评分；缓存补测 reward **1**、**5/5** 通过，8 次回复后正常结束 | 两次均为 0 次 | -| `circuit-fibsqrt` | `bash` 缺 `command`，原始 JSON 为 `{}` | 49 次回复后达到 3600 秒执行时限，停止后产物通过 **3/3** 测试、reward **1**；**不计为正常结束的通过** | 0 次 | - -circuit 的中断由实验 supervisor 发出，最终为 `KeyboardInterrupt`／退出码 130， -Harbor 记录 `NonZeroAgentExitCodeError`。收尾完成且保留原生轨迹，没有强制杀进程； -验证在 agent 停止之后才开始。因此应同时保留“产物通过”和“执行预算耗尽”， -不能仅凭 reward 1 就把该运行记为正常完成。 - -本组实跑中，直接观察到参数错误反馈后继续并通过验证的只有 scheduler，且本次 -错误是 `bash` JSON，不是历史上的 `write.content`。其他题目的新采样没有产生 -参数校验错误；它们的通过不能单独归因于本功能。四份历史坏输入能否恢复，由 -前述原始响应离线重放提供同输入证据。 - -含 scheduler、首次 PyTorch 验证超时和两次缓存补测在内，共保留 **6 份原生 ATIF**， -全部通过 Harbor 校验、与完整 Journal 投影一致，且没有 Journal 字符串截断。 -这 6 次实跑共 222 次模型回复，输入 12,183,309 tokens（含缓存命中 10,021,376）， -输出 427,835 tokens，模型费用 **$0.243005564**，用量与费用均完整；纯安装失败、 -暂停时尚未启动的模型及离线重放没有模型费用。PyTorch 缓存补测的安装、agent、 -验证分别耗时 432.8、140.0、23.2 秒；这反映了缓存准备条件，不能解释为 agent -代码使原先 900 秒的验证下载变快。 - -**完整 20 题复测（2026-09-14～15）。** 独立完整组最终为 **10/20 通过（50%）、 -9 题零分、1 题未评分**；其中 **9 题正常完成并通过（45%）**。原始 8192 基线 -为 8/20 通过（40%），其中 7 次没有 Harbor 异常。本次 circuit 的产物通过了 -验证，但费用对账阶段达到执行上限，不能计为正常结束的通过。“无 Harbor 异常” -与本次要求原生 `completed`、无异常、未触及执行上限的定义也不完全相同。 -[结构化结果](../../../benchmarks/harbor/results/tb21-tool-input-recovery-20260914.json) -保留各次尝试、配置、计费完整性与验证证据。 - -| 实验组 | 题数 | reward 1 | reward 0 | 未评分 | -| --- | --- | --- | --- | --- | -| 原始 8192 基线 | 20 | 8 | 12 | 0 | -| 上一轮 65536 首批 18 题 | 18 | 5 | 8 | 5 | -| 本次相同 18 题子集 | 18 | 8 | 9 | 1 | -| 本次独立完整组 | 20 | 10 | 9 | 1 | - -相同 18 题子集的 8 次通过中，7 次正常结束。下表的数字为 reward；“不在此组” -表示上一轮首批 18 题没有包含该题，并不表示未评分。compressor 上一轮在独立 -65536 补测中已经通过，regex-log 则在 32768 补测中通过，均不能混入旧首批 18 题。 - -| 题目 | 原始 8192 | 上轮 65536 首批 18 | 本次 65536 | 本次结果说明 | -| --- | --- | --- | --- | --- | -| `write-compressor` | 0 | 不在此组 | 1 | 正常结束；3/3 | -| `torch-tensor-parallelism` | 0 | 未评分 | 0 | 正常结束；1/3 | -| `schemelike-metacircular-eval` | 0 | 0 | 0 | 第 18 次请求响应流中断 | -| `kv-store-grpc` | 1 | 0 | 1 | 正常结束；7/7 | -| `pypi-server` | 1 | 1 | 1 | 正常结束；1/1 | -| `dna-assembly` | 0 | 未评分 | 0 | 第 2 次请求服务商不可用 | -| `torch-pipeline-parallelism` | 0 | 未评分 | 0 | 50 轮耗尽；2/3 | -| `qemu-alpine-ssh` | 1 | 未评分 | 1 | 正常结束；1/1 | -| `openssl-selfsigned-cert` | 1 | 1 | 1 | 正常结束；6/6 | -| `regex-chess` | 0 | 0 | 0 | 50 轮耗尽；1/4 | -| `log-summary-date-ranges` | 1 | 1 | 1 | 正常结束；2/2 | -| `model-extraction-relu-logits` | 0 | 未评分 | 未评分 | agent 正常结束；验证 900 秒超时 | -| `path-tracing` | 0 | 0 | 0 | 第 32 次请求服务商不可用 | -| `regex-log` | 0 | 不在此组 | 1 | 正常结束；1/1 | -| `caffe-cifar-10` | 0 | 0 | 0 | 正常结束；2/6 | -| `mteb-leaderboard` | 0 | 0 | 0 | 50 轮耗尽；0/2 | -| `llm-inference-batching-scheduler` | 1 | 0 | 0 | 第 3 次请求响应流中断 | -| `pytorch-model-recovery` | 1 | 1 | 1 | 正常结束；5/5 | -| `circuit-fibsqrt` | 0 | 0 | 1 | 3/3；费用对账中断，轨迹重建 | -| `merge-diff-arc-agi-task` | 1 | 1 | 1 | 正常结束；5/5 | - -与原始基线相比，新增通过 compressor、regex-log、circuit，scheduler 从通过 -变为零分；净增加 2 题。与上一轮 65536 首批 18 题相比，KV-store 从零分变为通过， -QEMU 从未评分变为通过，circuit 从零分变为产物通过但收尾异常。前两题在原始 -8192 基线中本就通过，compressor 和 regex-log 在此前各自的预算补测中也已通过， -因此不能把这些分数变化当作参数校验的独立收益。 - -本次完整组 **没有触发 `ToolInputError`**。同一历史坏输入能否恢复，仍由四份 -原始响应的离线重放证明；实跑中观察到错误反馈后继续并通过的证据，来自前述 -定向 scheduler。本次完整组的 scheduler 在第 3 次请求发生 `RemoteProtocolError`， -只完成 2 次回复、得到零分；不能用定向组的成功覆盖这次独立失败。 - -完整组共有 4 次请求未完整返回：Scheme 和 scheduler 的响应流中断，DNA 和 -path-tracing 则收到 `provider_unavailable`，提示上游收到 SIGTERM、停止服务超时。 -这些属于传输或服务可用性问题。pipeline、regex-chess、MTEB 耗尽 50 轮；tensor -和 Caffe 正常结束但功能测试未全通过。模型提取完成 27 次回复，依赖已安装， -pytest 已收集并开始执行单个测试，但超过原始 900 秒验证时限，因此保留未评分； -这次不能归因于依赖下载，也没有延长时限来替换结果。 - -**circuit 的收尾问题。** 第 50 次回复在 UTC 18:46:26.965 以 `end_turn` 结束， -之后继续查询费用。supervisor 在 18:47:05.171 触发 3600 秒执行上限，打断费用 -对账；此时尚未写入 `run.completed` 或 `run.failed`，原生 ATIF 导出抛出 -`AtifProjectionError: Event Journal has no terminal event`。实验包装器保留原始 -Journal，另加明确标注的合成终止事件后重建轨迹；没有把重建文件冒充原生导出。 -agent 停止后官方测试 3/3 通过。这个费用收尾控制分支与实现提交的父提交相同， -本次没有修改它；后续需要处理“对账中断仍能记录终止状态并导出轨迹”的问题。 -工具输出中另有模型生成程序的 `Gate` 对象 `KeyError`，它作为工具结果返回， -不是 agent 因缺失工具参数而崩溃。 - -circuit 后段的三次详细监控探针各自超过 25 秒。只读检查时，容器仍在运行， -内存约 1.983/2 GiB，Docker 未报告 OOM；随后停用容器内详细探针，改读宿主机 -结果文件和容器状态，没有修改 agent、验证器或资源上限，也没有发现遗留探针。 -先前探针对内存压力的影响无法量化，因此把这次监控变化列为实验限制。 - -**轨迹与费用。** 20 份 ATIF 均通过 Harbor 校验，其中 19 份是原生导出且与完整 -Journal 投影一致，1 份是上述明确标注的重建文件；全部 Journal 均无字符串截断。 -共启动 491 次模型调用，487 次完整返回。已完整记录的回复合计输入 -**21,991,162 tokens**（含缓存命中 **18,119,936**）、输出 **724,013 tokens**； -4 次中断响应的原生用量仍不完整，不能把缺失值当零。逐回复输出最大值也只统计 -完整回复，不能用它代表中断响应的实际输出。QEMU 另有 1 次建连阶段 -`ConnectTimeout`，SDK 重试后正常通过：41 次模型回复、42 次 HTTP 尝试。 - -所有模型费用已由原生记录或独立账单回执补齐。补查区分已完整返回的调用与中断 -请求，并扣除 Journal 中已经记录的费用，避免重建轨迹缺少终止事件时重复计费。 -完整 20 题费用为 **$0.462175819**；连同定向组、缓存补测和恢复验证的 scheduler， -本次实验模型费用合计 **$0.705181383**。费用完整不等于原生响应或用量完整。 - -#### 流中断恢复的完整 20 题复测 - -**2026-09-21～22 实跑。** 在 `fix/stream-recovery` 分支提交 -`577a1ab7f96fc3d220ca89c068988a479ac7b1aa` 上重新运行原始 20 题，检查真实模型 -响应是否中断，以及中断后能否在同一次 run 内恢复。北京时间 09-21 22:06 开始， -09-22 01:52 结束，总耗时约 **3 小时 46 分钟**；没有人为注入传输故障。 - -**配置与记录。** 使用 Harbor 0.21.0、Terminal-Bench 2.1，保留原始 20 题的 -task ref，并在运行前核对缓存镜像摘要。模型仍为 OpenRouter 上的 -`deepseek/deepseek-v4-flash-0731`。容器安装本分支构建的 wheel，逐个核对包内 -Python 源文件与当前提交一致；运行版本为 `0.8.1.dev28+g577a1ab7f`。依赖固定为 -Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0、Pydantic 2.13.5。 - -每次回复上限 65536 tokens，每题最多 50 次完整回复，并发 2；每题只尝试一次， -Harbor 整题自动重试为 0。agent 执行上限 3600 秒、收尾宽限 120 秒、Harbor -外层上限 3780 秒，安装上限 3600 秒，官方 verifier 保留原始时限。沿用此前的 -依赖缓存和被动 HTTP 记录器；容器内仅做轻量事件计数，完整 Journal／HTTP -记录收集到宿主机后分析。实时计数器曾修正为兼容无空格的 JSON 并单独重启， -没有重启评测或修改 agent、题目和预算。 - -| 指标 | 本次结果 | -| --- | --- | -| 已结束的 trial | 20/20 | -| 实际进入 agent 执行 | 18 题；另外 2 题安装失败，未调用模型 | -| 官方评分 | reward 1：**11 题（55%）**；reward 0：**6 题**；未评分：**3 题** | -| 正常完成且通过 | **8 题（40%）**：原生终态为 `completed`，无 Harbor 异常，未触及执行时限 | -| Journal 模型调用 | **576 次启动，576 次完整返回** | -| `model.failed` / HTTP 响应体中断 | **0 / 0** | -| 触发流恢复 / 恢复成功 | **0 / 0**；本轮没有触发恢复分支 | - -逐题结果如下。“轮数耗尽”指原生终态为 `max_turns_exhausted`；它与官方 verifier -是否判定产物通过是两件事。 - -| 题目 | Reward | 执行或验证结果 | -| --- | --- | --- | -| `write-compressor` | 1 | 正常完成并通过 | -| `torch-tensor-parallelism` | 未评分 | agent 正常完成；verifier 超过原始 900 秒时限 | -| `schemelike-metacircular-eval` | 1 | 50 轮耗尽；产物通过 | -| `kv-store-grpc` | 0 | agent 正常完成，产物未通过 | -| `pypi-server` | 1 | 正常完成并通过 | -| `dna-assembly` | 0 | agent 正常完成，产物未通过 | -| `torch-pipeline-parallelism` | 未评分 | 安装阶段使用 Python 3.12.3，不满足项目要求的 Python >=3.13 | -| `qemu-alpine-ssh` | 未评分 | 安装阶段下载 Python 3.14.0，三次重试后仍收到 HTTP 504 | -| `openssl-selfsigned-cert` | 1 | 正常完成并通过 | -| `regex-chess` | 1 | 50 轮耗尽；产物通过 | -| `log-summary-date-ranges` | 0 | agent 正常完成，产物未通过 | -| `model-extraction-relu-logits` | 1 | 正常完成并通过 | -| `path-tracing` | 1 | 正常完成并通过 | -| `regex-log` | 1 | 正常完成并通过 | -| `caffe-cifar-10` | 0 | 50 轮耗尽，产物未通过 | -| `mteb-leaderboard` | 0 | 50 轮耗尽，产物未通过 | -| `llm-inference-batching-scheduler` | 1 | 50 轮耗尽；产物通过 | -| `pytorch-model-recovery` | 1 | 正常完成并通过 | -| `circuit-fibsqrt` | 0 | 50 轮耗尽，产物未通过 | -| `merge-diff-arc-agi-task` | 1 | 正常完成并通过 | - -两次安装失败均被 Harbor 记为 `NonZeroAgentExitCodeError`，发生在 agent 启动前； -tensor 的异常为 `VerifierTimeoutError`。这些不是模型流中断，不能把 20 个 trial -结束写成“20 题都完成了模型执行”。同样，11 次 reward 1 中的 Scheme、regex-chess、 -scheduler 是轮数耗尽后产物通过，不计入 8 次正常完成且通过。 - -**恢复能力的证据边界。** 本轮 576 次模型调用均完整返回，没有观察到真实流中断， -因此只能记录“此次未出现流中断”，不能据此声称“实跑中断后恢复成功”。本轮启动时 -另行运行 `tests/test_stream_recovery.py`，结果为 **17 passed，0 failed，0 skipped**， -覆盖两种 HTTP 异常族、重试耗尽和时间窗口、丢弃中断回复中的工具调用，以及保留 -已有工具结果等行为。这些自动化测试提供恢复机制的验证，但不能替代真实中断样本。 -采样、路由、缓存和服务端状态未受控；与此前 20 题的分数变化也不能归因于流恢复。 - -本地 job 为 `jobs/tb21-stream-recovery-pilot20-20260921/`，实验记录目录为 -`jobs/tb21-stream-recovery-pilot20-20260921-record/`。其中 `manifest.json` 保存源码、 -wheel、任务与镜像信息，`summary.json` 保存逐题统计，`report.md` 为结果摘要， -`stream-recovery-tests.json` 保存专项测试结果。原始 Journal、HTTP 记录、trajectory -和 verifier 输出保留在各 trial 目录；这些原始数据位于 Git 忽略的 `jobs/` 中。 - -#### 超时停止 - -“超时停止”指的是：任务达到规定的运行时间后，要确保容器里的 agent 真正停止 -工作，再开始验证结果。本轮 Harbor 已经判定任务超时，但 agent 仍在继续调用模型。 - -具体发生在 `pytorch-model-recovery`，该题的 agent 运行时限为 900 秒，即 15 分钟。 -以 agent 开始执行为起点，实际时间线如下： - -| 时间 | 实际发生的事情 | -| --- | --- | -| 约第 900 秒 | Harbor 判定超时，记录 `AgentTimeoutError` | -| 约第 901 秒 | verifier 开始验证任务结果 | -| 约第 929、939 秒 | agent 又启动了两次模型调用 | -| 约第 1063 秒 | agent 因 `edit` 缺少 `path` 异常而失败，记录 `run.failed` | - -Harbor 的“已经超时”没有及时转化为容器内 agent 的“停止执行”，带来两个后果： - -- 验证期间 agent 仍在运行，可能继续修改 verifier 正在检查的文件，影响结果的 - 可靠性。因此这题虽然得到 `reward=1`，也不能当作正常完成的成功样本。 -- 超过时限后仍产生新的模型调用和费用，任务的时间边界没有得到落实。 - -拟议修复目标是： - -1. 到达任务时限后，停止 agent 的执行，阻止它继续发起模型和工具调用。 -2. 清理需要终止的工具子进程，避免 agent 退出后仍有命令继续修改文件。 -3. 确认执行已停止，再启动 verifier，并准确记录超时状态。 - -目前确认的是超时后 agent 仍在运行；具体是哪一层的取消或进程清理没有生效， -还需要进一步定位。这项修复的重点是让时间限制真正生效，单纯延长 900 秒无法 -解决超时后继续执行的问题。 - -#### trajectory 采集 - -“trajectory 采集”指的是：确保 agent 的结构化执行记录能被保存，并被 Harbor -及时读取，用来汇总执行步骤、token 和费用。本轮暴露的问题是文件后来有了， -但 Harbor 汇总时没有拿到。 - -正常情况下，agent 运行时持续记录模型回复、工具调用、执行结果和用量，结束时 -将这些记录导出为 ATIF 格式的 `trajectory.json`。Harbor 随后读取并校验轨迹， -据此汇总步骤、token 和费用。这意味着“agent 已停止”“轨迹已保存”“评测已采集” -是需要按顺序完成的三个环节。 - -本轮 `pytorch-model-recovery` 的采集过程出现了时序问题： - -1. Harbor 判定超时后开始采集，此时没有拿到 trajectory，记录为 `missing`。 -2. agent 仍在运行，后来收尾才写出有效的 ATIF 文件。 -3. 文件最终出现在结果目录里，但 Harbor 原始结果中的 token 仍为 `null`，没有随 - 文件出现而自动补齐。 - -因此，最终 20/20 份 ATIF 都通过校验，但只有 19/20 题的 Harbor 原始指标与最终 -ATIF 一致。文件最终存在、格式有效，并不表示评测汇总时已经成功采集到它。 - -拟议修复目标是：正常完成、发生异常或超时时，都应尽可能保留截至停止时的执行 -记录，并让 Harbor 在汇总前读到它；记录不完整时，也要明确标注。 - -这需要与“超时停止”一起设计：先让执行停止并在有限时间内完成收尾，再采集和 -汇总。当前轨迹导出依赖 agent 完成收尾；如果直接强制杀死进程，轨迹可能来不及 -保存。因此还需考虑定期保存快照，或从已落盘的 Journal 恢复轨迹等 -方案。具体方案尚未确定，修复目标是让实际执行记录、保存的 trajectory 和 Harbor -汇总指标能够对应起来。 - -#### 完善延迟费用补查 - -“完善延迟费用补查”指的是：任务结束时还没查到的模型调用费用，要能在稍后继续 -查询，并补齐费用统计。 - -模型回复已经返回时，响应里可能没有实际费用，账单查询接口也暂时查不到金额。 -此时 agent 保存该次调用的 generation ID，作为后续关联账单的标识，再用它查询 -这次调用的 `total_cost`。暂时查不到只能标记为待查或未知，不能当成没有费用。 - -当前 agent 已经会在运行收尾时，自动对缺少费用且有 generation ID 的调用逐笔 -补查。每笔默认最多尝试 6 次，重试间隔为 `1、2、4、8、15` 秒。这些间隔 -累计为 30 秒，还要加上查询请求本身的耗时，因此 30 秒并非整个补查过程的耗时 -上限。超过尝试次数仍未查到，就保留费用不完整的状态。上述收尾补查已经是 agent -的正式能力；当前尚未提供正式的独立后续补查入口。 - -本轮 13 道题的原始 trajectory 费用不完整，共有 18 次调用未在收尾补查中取得 -费用。在 2026-09-06 本轮批量运行期间及结束后，通过独立脚本分批查询同一个费用 -接口，最终补齐了这 18 笔费用。 - -**这次独立补查使用的是临时脚本，尚未纳入正式流程。** 脚本为本地 -`jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`，保存在被 Git -忽略的 `jobs/` 目录中，作为本次试跑的一次性工作流记录归档。它需要单独运行， -没有接入正式 CLI、Harbor adapter 或固定的评测后处理步骤。因此，这次通过临时 -脚本查全费用，并不表示 agent 已具备自动完成后续补查的能力。 - -补查前后的费用为： - -| 统计口径 | 费用（USD） | -| --- | ---: | -| 最终 trajectory 中的已知费用小计 | 0.11053603 | -| 独立补查后的完整费用 | 0.119200332 | - -补查后的金额覆盖全部 267 次已记录模型回复。这说明收尾阶段的有限尝试仍会留下 -需要稍后补查的费用，不能把当时已知的小计视为完整总额。 - -拟议方向是把本次临时完成的后续补查做成可重复使用、能接入评测流程的正式能力， -并支持任务结束后独立运行：读取尚未取得费用的 -generation ID，稍后重新查询，保存补查回执，再生成更新后的费用汇总。该流程 -需要保留原始结果，避免同一笔费用被重复计入，并明确区分已知费用小计和完整 -总费用。仍查不到的金额保持未知，不能填成零；补查失败也不应改变题目的评分。 - -具体采用独立命令还是批量评测后的自动步骤，尚未确定。重点是让补查可以跨越任务 -收尾阶段继续进行，避免为了等待账单而长时间拖住 agent 退出。 - -### 更换模型复跑未通过的 6 题 - -**2026-09-25 实跑。** 上一轮流中断恢复复测在 `deepseek/deepseek-v4-flash-0731` -下，20 题里有 6 题 reward 为 0：`kv-store-grpc`、`dna-assembly`、 -`log-summary-date-ranges`、`caffe-cifar-10`、`mteb-leaderboard`、`circuit-fibsqrt`。 -为了判断这些失败是否受模型限制，本轮用同一批题、同一套运行条件，只更换模型把它们 -重跑一遍。这不是受控实验：每题只跑 1 次，路由、采样、缓存和服务端后端都没有固定。 - -**模型选择。** 目标是换一个 DeepSeek Flash 模型。OpenRouter 上并没有 -`deepseek-flash` 这个 slug；`deepseek/deepseek-v4-flash-latest` 等别名会被 -Anthropic Messages 接口判为无效模型。最终使用 **`deepseek/deepseek-v4.1-flash`** -（V4.1 Flash），并先发一次最小 Messages 请求确认它可用且支持工具调用。此前还曾用 -`deepseek/deepseek-v4-flash`（V4 Flash 0423）启动过一轮，但在完成前被中止，产物 -保留在 `jobs/tb21-v4flash-6task-20260925/`，不作为实验结果；本文只记录 V4.1 Flash -这轮。 - -**固定条件。** - -| 项目 | 本轮配置 | -| --- | --- | -| Job | `tb21-v41flash-6task-20260925` | -| Agent 源码 | `main` 提交 `1386df48ef9c84d7b65c33fcf0f903fad7036a79`；wheel `0.8.1.dev30+g1386df48e` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| Dataset | `terminal-bench/terminal-bench-2-1`，`sha256:7d7bdc1c…a0699a` | -| 题目 | 上述 6 题，沿用各自固定 task ref | -| 生成／轮数 | 每次回复最多 65536 tokens；每题最多 50 次完整回复 | -| 时限 | agent 3600 秒、收尾宽限 120 秒、Harbor 外层 3780 秒、安装 3600 秒；verifier 保留题目原始时限 | -| 并发／重试 | 并发 2；每题 1 次尝试，Harbor 自动重试 0 | -| 依赖 | Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0、Pydantic 2.13.5 | -| 镜像 | 沿用基线缓存镜像，运行前核对 RepoDigests | - -**结果。** 6 题全部结束、全部得到官方评分，**通过 4 题、未通过 2 题**。 - -| 指标 | 本轮结果 | -| --- | --- | -| 通过／未通过／未评分 | **4 ／ 2 ／ 0** | -| Journal 模型调用 | **206 次启动，206 次完整返回** | -| `model.failed` / HTTP 响应体中断 | **0 / 0** | -| input／cache／output tokens | 8,122,046 ／ 7,444,992 ／ 219,443 | -| 模型费用 | **$0.278759041**（逐次回执完整） | -| 基础设施异常 | 0 | - -| 题目 | Reward | 终态 | 模型回复 | 费用（USD） | -| --- | ---: | --- | ---: | ---: | -| `kv-store-grpc` | 1 | `completed` | 17 | 0.008440065 | -| `dna-assembly` | 1 | `completed` | 34 | 0.069021966 | -| `log-summary-date-ranges` | 1 | `completed` | 6 | 0.005224672 | -| `circuit-fibsqrt` | 1 | `completed` | 49 | 0.080010864 | -| `caffe-cifar-10` | 0 | `max_turns_exhausted` | 50 | 0.065742942 | -| `mteb-leaderboard` | 0 | `max_turns_exhausted` | 50 | 0.050318532 | - -**两题仍未通过，原因都是 50 轮耗尽，不是崩溃或传输故障。** - -- `caffe-cifar-10`：CPU 训练太慢，50 轮内只走到 `Iteration 200`， - `cifar10_quick_iter_500.caffemodel` 与 `Iteration 500` 都不存在；官方 6 项测试 - 通过 2 项、失败 3 项（另有 1 项通过）。瓶颈是轮数上限加训练耗时。 -- `mteb-leaderboard`：始终没有生成 `/app/result.txt`，官方 2 项测试全部失败； - 模型一直在阅读 MTEB 源码，50 轮耗尽仍未写出结果文件。这题在每一轮历史实验里 - 都是同一种死法。 - -**与历史的对比。** 这 6 题在 2026-09-21 的 0731 运行中全部 reward 0；换到 -V4.1 Flash 后： - -- `kv-store-grpc`、`log-summary-date-ranges` 恢复通过（它们在更早的 8192 与 - 09-14~15 运行中也曾通过，属于回归恢复）。 -- `dna-assembly` **首次通过**，且是干净的 `completed`；此前所有尝试都是 0 或未评分。 -- `circuit-fibsqrt` **首次干净通过**；此前两次 reward 1 都伴随执行上限或轨迹重建异常。 -- `caffe-cifar-10`、`mteb-leaderboard` 仍未通过，且历史上从未通过。 - -**证据边界。** 换模型的同时没有控制路由、采样、缓存和后端，每题也只跑了 1 次， -因此 **0/6 → 4/6 不能直接归因于模型差异**，只能作为一个待验证的信号。 - -**产物位置。** 原始 trial 在 `jobs/tb21-v41flash-6task-20260925/`，实验记录目录为 -`jobs/tb21-v41flash-6task-20260925-record/`（`manifest.json`、`summary.json`、 -`report.md`）；这些位于 Git 忽略的 `jobs/`，未上传。版本管理保存结构化结果 -[`tb21-v41flash-6task-20260925.json`](../../../benchmarks/harbor/results/tb21-v41flash-6task-20260925.json) -与本节汇总。 - -### 实验一：把 max_turns 提到 100 - -**2026-09-26 实跑。** 上一轮（`tb21-v41flash-6task-20260925`）里 -`caffe-cifar-10` 与 `mteb-leaderboard` 都是 50 轮耗尽（`max_turns_exhausted`）。 -Terminal-Bench / Harbor 对 agent 的硬约束是**墙钟时间**（本题原生 -`[agent].timeout_sec = 3600`），并不包含轮数上限；50 轮是 nanoPyCodeAgent 自己的 -成本护栏（`adapter.py:18`、`agent.py:82`）。因此本轮只改一件事：**把 `max_turns` -从 50 提到 100，其余设置保持不变**。为让这次运行合规，**移除了 Harbor 层的 -`override_timeout_sec` 与 `override_setup_timeout_sec`**，改用题目原生时限，agent -在题目 3600 秒内自行限时 3480 秒并预留 120 秒收尾。 - -| 项目 | 本轮配置 | -| --- | --- | -| Job | `tb21-exp1-turn100-20260926` | -| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `41476d451`；wheel `0.8.1.dev32+g41476d451` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| Dataset | `terminal-bench/terminal-bench-2-1`，`sha256:7d7bdc1c…a0699a` | -| 题目 | `caffe-cifar-10`、`mteb-leaderboard`，沿用固定 task ref | -| 生成／轮数 | 每次回复最多 65536 tokens；每题最多 **100** 次完整回复 | -| 时限 | 题目原生 agent 3600 秒；agent 自限 3480 秒 + 120 秒收尾；**未覆盖任何 Harbor timeout** | -| 并发／重试 | 并发 2；每题 1 次尝试；Harbor 自动重试 0 | - -**结果：2 题全部通过，但两题的死法不同。** - -| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | -| --- | ---: | --- | ---: | ---: | ---: | -| `mteb-leaderboard` | 1 | `completed` | 70 | 1666 s | 0.054191917 | -| `caffe-cifar-10` | 1 | `DiagnosticDeadlineExceeded` | 69 | 3600 s | 0.039895559 | - -- `mteb-leaderboard`：**历史上第一次通过**，而且是干净的 `completed`——第 70 轮 - 写完 `/app/result.txt` 并主动结束。这说明上一轮的失败**不是能力问题，而是 50 轮 - 这个自设上限卡住了它**：它在第 39 轮就已经算出正确答案，50 轮时还在复核，100 轮 - 时终于收敛。 -- `caffe-cifar-10`：官方 6 项测试全通过，但 agent 没有自己收尾——它在 - `Iteration 500` 训练完成、并用训练好的模型跑完测试之后，又去构建 pycaffe - (`make py`)、装 `python3-skimage` 等额外工作，直到撞上 3480 秒自限与 3600 秒 - 硬上限被强杀（`deadline_reached=true`、`forced_kill=true`）。**奖励来自超时前 - 已落盘的产物**，不是 agent 主动结束；轨迹因此只能由 supervisor 从 journal 重建 - （`trajectory_source=reconstructed`）。 - -**结论。** 这两题印证了同一判断的两面： - -1. 把轮数上限提到 100 确实让 `mteb-leaderboard` 通过——**自设的轮数上限曾经是 - 绑定约束**。 -2. 但 `caffe-cifar-10` 表明，**光放宽轮数并不能让 agent 学会收尾**：它仍会一路 - 做到被外部强杀。这正是"时间预算 + 预算注入 + 到点停止"（实验二）要解决的问题。 - -**证据边界。** 每题只跑 1 次，路由、采样、缓存、后端都未固定；这只是一个针对性 -信号，不是通过率的受控估计。 - -**产物位置。** 原始 trial 在 `jobs/tb21-exp1-turn100-20260926/`，实验记录在 -`jobs/tb21-exp1-turn100-20260926-record/`；这些位于 Git 忽略的 `jobs/`，未上传。 -版本管理保存结构化结果 -[`tb21-exp1-turn100-20260926.json`](../../../benchmarks/harbor/results/tb21-exp1-turn100-20260926.json) -与本节汇总。 - -### 实验二：时间预算、预算注入与到点停止 - -**2026-09-26 实跑。** 实验一证明"放宽轮数"能让两题通过，但 `caffe-cifar-10` -仍是被外部强杀（`forced_kill=true`、轨迹靠重建）。本轮在实验一的基础上加三样东西， -其余不变： - -- **时间预算**：新增 `--time-budget-seconds`（Harbor 侧 kwarg - `time_budget_seconds`）。本轮取 3420 秒，落在题目原生 3600 秒之内、supervisor - 3480 秒自限之前。 -- **预算注入**：每轮把剩余时间写进 system prompt；进入收尾窗口 - （`max(15% 预算, 180 秒)`）后升级为"立即停止调研、写出产物、然后收尾"的强提示。 -- **到点停止**：预算耗尽时返回新终态 `time_budget_exhausted`，在下一次调用或执行 - 工具之前停下，并写进 Journal 与 ATIF。 - -| 项目 | 本轮配置 | -| --- | --- | -| Job | `tb21-exp2-timebudget-20260926` | -| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `eb77fd578`；wheel `0.8.1.dev34+geb77fd578` | -| Model | `openrouter/deepseek/deepseek-v4.1-flash` | -| 时限 | 题目原生 agent 3600 秒；agent 预算 3420 秒；supervisor 自限 3480 秒 + 120 秒收尾；**未覆盖任何 Harbor timeout** | -| 其余 | 每题最多 100 次回复、每次最多 65536 tokens、并发 2、每题 1 次、重试 0，与实验一一致 | - -**结果：2 题全部通过，而且这次两题都自己干净收尾。** - -| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | -| --- | ---: | --- | ---: | ---: | ---: | -| `mteb-leaderboard` | 1 | `completed` | 50 | 865 s | 0.481624230 | -| `caffe-cifar-10` | 1 | `completed` | 61 | 2657 s | 0.374407898 | - -- `mteb-leaderboard`：50 轮、865 秒完成，比实验一的 70 轮、1666 秒**快了一倍**， - 最终消息明确写出答案 `GritLM/GritLM-7B` 并说明已写入 `/app/result.txt`。 -- `caffe-cifar-10`：61 轮、2657 秒**主动收尾**——supervisor 显示 - `deadline_reached=false`、`forced_kill=false`、`trajectory_source=native`， - 与实验一"训练完成后继续构建 pycaffe、最终被强杀、轨迹靠重建"形成对照。 - 最终消息是"All requirements are met"，训练恰好 500 步、测试精度 55.17%。 - -**两轮实验并列对比（同为 2 题、各 1 次）：** - -| 指标 | 实验一（max_turns=100） | 实验二（+ 3420s 预算） | -| --- | ---: | ---: | -| 通过 | 2 / 2 | 2 / 2 | -| 干净 `completed` | 1 / 2 | **2 / 2** | -| 触发截止／强杀 | 1（caffe） | **0** | -| 轨迹需重建 | 1（caffe） | **0** | -| 总模型调用 | 139 | 111 | -| input tokens | 340,804 | 2,919,588 | -| cache tokens | 4,289,280 | 1,408 | -| 总费用（USD） | 0.094 | **0.856** | - -**发现一：时间预算让 agent 自己收尾。** 两题都在预算耗尽之前主动结束， -`deadline_reached` 与 `forced_kill` 全为 false，轨迹全部是原生的。这正好补上了 -实验一暴露的"能做完但不会停"。 - -**发现二：逐轮改写 system prompt 破坏前缀缓存；总费用上升但被路由混淆。** -表面上，实验二的 input tokens 是实验一的约 8.6 倍（2.92M 对 0.34M），cache -tokens 从 4.29M 掉到 1.4K，总费用从 $0.094 涨到 $0.856。按 provider 拆开后有两个 -原因： - -1. **system prompt 每轮都变，前缀缓存失效。** DeepInfra 是两次运行里唯一稳定上报 - 缓存的 provider。实验一 121 次 DeepInfra 调用平均每次命中 29,116 个缓存 tokens； - 实验二 23 次 DeepInfra 调用平均只命中 61 个。同一 provider 上前缀缓存几乎归零， - 与"每轮改写 system prompt 使最长公共前缀失效"一致。 -2. **路由发生偏移（未受控）。** 实验一 139 次调用里 121 次落在 DeepInfra；实验二 - 111 次里 Together 48、Parasail 29、DeepInfra 23、Relace 10。Together／Parasail／ - Relace 在两次运行里都不上报缓存读取。因此总费用的 8.6 倍**不能单独归因于注入**， - 路由变化是叠加的混淆因素。 - -无论哪种原因，修复方向一致：**保持 system prompt 固定，把预算提示作为对话尾部 -的一条 user/tool 消息追加**（尾部变化不影响前缀缓存），或只在跨阈值时注入。本次先 -如实记录，修复方案留待下一轮。 - -**证据边界。** 每题仍只跑 1 次，路由、采样、缓存与后端都未固定；"更快、更干净" -是针对性信号，不是受控估计，也不能排除单次运行波动。唯一能直接归因的是机制本身： -`deadline_reached=false`、`forced_kill=false`、`trajectory_source=native` 是 -"agent 自己停"的直接证据。 - -**产物位置。** 原始 trial 在 `jobs/tb21-exp2-timebudget-20260926/`，实验记录在 -`jobs/tb21-exp2-timebudget-20260926-record/`；位于 Git 忽略的 `jobs/`，未上传。 -版本管理保存结构化结果 -[`tb21-exp2-timebudget-20260926.json`](../../../benchmarks/harbor/results/tb21-exp2-timebudget-20260926.json) -与本节汇总。 - -### 实验三：把预算提示移到对话尾部（修复缓存） - -**2026-09-27 实跑。** 实验二发现逐轮改写 system prompt 会让 provider 的前缀缓存 -失效。本轮唯一的改动是：**system prompt 保持固定，把预算提示追加到对话尾部** -（第 1 轮跟在任务文本后，之后跟在 tool 结果后），其余与实验二完全一致（max_turns -100、预算 3420 秒、无任何 Harbor timeout 覆盖）。 - -| 项目 | 本轮配置 | -| --- | --- | -| Job | `tb21-exp3-timebudget-tail-20260926` | -| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `83e5c064d`；wheel `0.8.1.dev37+g83e5c064d` | -| 其余 | 与实验二相同：预算 3420 秒、每题最多 100 次回复、无 timeout 覆盖 | - -**结果：2 题全部通过。** - -| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | -| --- | ---: | --- | ---: | ---: | ---: | -| `caffe-cifar-10` | 1 | `completed` | 59 | 3333 s | 0.048236604 | -| `mteb-leaderboard` | 1 | `max_turns_exhausted` | 100 | 1365 s | 0.138716976 | - -**缓存修复已验证。** 两轮对比（同为 2 题）： - -| 指标 | 实验二（system 注入） | 实验三（尾部注入） | -| --- | ---: | ---: | -| input tokens | 2,919,588 | **108,646** | -| cache tokens | 1,408 | **8,410,880** | -| 总费用（USD） | 0.856 | **0.187** | - -更直接的证据来自同一 provider 的前后对比：实验二里 Parasail 的 29 次调用命中 0 个 -缓存读取；实验三里 Parasail 承担了 159 次中的 157 次，平均每次命中约 **53,572** 个 -缓存 tokens。同一 provider 上从 0 恢复到 5 万多，与"system 固定 + 前缀缓存生效" -一致。 - -**但收敛行为变弱了。** 实验二的 `mteb-leaderboard` 在 50 轮主动 `completed`； -实验三它写对了 `/app/result.txt`（reward 仍为 1），却一路做到第 100 轮被 -`max_turns_exhausted` 截断，全程没有输出任何收尾文本。两种可能：尾部提示嵌在 -tool 结果里，显著性不如 system prompt；也可能只是单次运行波动。无论哪种，**这一 -轮仍只跑 1 次，不能区分二者**。 - -**代码 review 与修复。** 借独立的 codex `/review`（按 main 分支对比）复查了本分支 -的实现，它提出 3 个问题并已修复（见下节）： - -- **[P1] 只在批次前检查一次截止时间**：一次回复里有多个工具调用时，第一个工具 - 耗尽预算后其余仍会执行。改为**执行每个工具前重新检查**。 -- **[P2] 注入的提示没有进入轨迹**：预算提示是改变行为的外部输入，却既不在 Event - Journal 也不在 ATIF 里。新增 `input.injected` 事件和对应的 ATIF step。 -- **[P2] 新终态没有版本化**：`time_budget_exhausted` 让新写入仍声称 schema v3， - 而旧 v3 reader 会拒绝它，更新后的 reader 又会错误接受历史 v1/v2 记录。按 - `response_truncated` 的先例**把 Schema 升到 v4**。 - -**证据边界。** 每题仍只跑 1 次，路由未受控（实验三 157/159 次落在 Parasail）。 -缓存差异的方向明确，但"收敛变弱"只能算信号。 - -**产物位置。** 原始 trial 在 `jobs/tb21-exp3-timebudget-tail-20260926/`，实验记录在 -`jobs/tb21-exp3-timebudget-tail-20260926-record/`。版本管理保存结构化结果 -[`tb21-exp3-timebudget-tail-20260926.json`](../../../benchmarks/harbor/results/tb21-exp3-timebudget-tail-20260926.json) -与本节汇总。 diff --git a/docs/dev_notes/zh-CN/0.9.x.md b/docs/dev_notes/zh-CN/0.9.x.md new file mode 100644 index 0000000..2a23ee6 --- /dev/null +++ b/docs/dev_notes/zh-CN/0.9.x.md @@ -0,0 +1,1451 @@ +# 开发笔记 — 0.9.x + +> 本文件为**中文源文件**(source of truth);英文版 [`../en/0.9.x.md`](../en/0.9.x.md) 由其生成。 + +## 0.9.0 - 2026.10.05 + +### 批量运行 Terminal-Bench 并建立 baseline + +单题验收已经证明任务容器、agent、trajectory、费用对账与官方 verifier 的链路可用。 +下一步先扩大到 20 题，观察不同任务上的失败方式和实际消耗，再决定完整 89 题的 +baseline 运行预算。这 20 题用于小规模试跑，不把它的通过率当作完整 Terminal-Bench 2.1 分数。 + +#### 固定实验配置 + +本轮于 2026-09-06 准备，固定如下条件；后续比较 harness 改进时，应复用题目和运行 +限制，并分别记录新的 agent revision。 + +| 项目 | 本轮配置 | +| --- | --- | +| Harbor | 0.21.0，使用 `benchmarks/harbor/uv.lock` | +| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | +| Dataset | `terminal-bench/terminal-bench-2-1`，共 89 题 | +| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | +| API endpoint | `https://openrouter.ai/api`，使用 Anthropic Messages 接口 | +| Provider routing | 请求不额外指定供应商；账户路由设置也属于实验条件 | +| 试跑规模 | 数据集列表前 20 题，每题 1 次 | +| Environment | 本地 Docker，并发 2 | +| Agent limit | 每题最多 50 次模型回复 | +| Per-response limit | `MAX_TOKENS = 8192`；请求未显式设置 reasoning effort 或 temperature | +| Harbor retries | 0；该设置不关闭 Anthropic SDK 内部的请求重试 | +| Timeouts and resources | 保留题目默认配置 | + +Harbor 0.21.0 的 `--n-tasks 20` 在过滤后截取列表前 20 项，不是随机抽样。固定数据集 +hash 可以固定任务版本，但仍应保存实际选中的任务清单，后续可用重复的 +`--include-task-name` 精确选题。模型在 OpenRouter 上的供应商路由和服务端行为仍可能 +变化，固定 model slug 不等于完全确定性的实验。 + +宿主机 Harbor 使用 lockfile；容器内 `uv tool install` 只固定 agent 源码 revision， +没有锁定依赖解析。公开结果保存 Docker 版本及任务镜像的 RepoDigests，用于核对后续 +实验环境；宿主机硬件信息仅保存在本地记录中。 + +#### 运行方法 + +先确认 `docker info` 成功，再从仓库根目录执行。凭据通过环境变量提供，不写入 +命令参数或实验记录。`ANTHROPIC_MODEL` 会覆盖 adapter 从 `--model` 推导的模型名， +因此本轮显式移除它。 + +```bash +export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" +export ANTHROPIC_BASE_URL="https://openrouter.ai/api" +unset ANTHROPIC_MODEL + +BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" +BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" + +uv run --locked --project benchmarks/harbor harbor run \ + --dataset "$BENCH_DATASET" \ + --n-tasks 20 \ + --agent harbor_adapter:NanoPyCodeAgent \ + --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ + --agent-kwarg max_turns=50 \ + --model openrouter/deepseek/deepseek-v4-flash-0731 \ + --env docker \ + --n-concurrent 2 \ + --n-attempts 1 \ + --max-retries 0 \ + --job-name tb21-flash0731-pilot20-20260906 +``` + +如果凭据已经保存在本机 `~/.nanoPyCodeAgent/settings.json`，可在启动 Harbor 的 Python +进程中调用 `nanopycodeagent.settings.load_settings_env()`，随后移除 +`ANTHROPIC_MODEL`，再启动同一条 Harbor 命令。仅由 task 容器内的 CLI 读取配置不够： +宿主机配置文件不会自动挂载到容器，必须先让宿主机 Harbor 获得连接环境变量。本次 +实跑采用这一方式，通过 Herdr 在当前 tab 新建右侧 split 执行，保持原 pane 的焦点。 + +复跑时使用新的 `--job-name`，避免与既有实验目录混淆。完整 89 题运行时删除 +`--n-tasks 20`，并另取 baseline job 名，其余条件保持一致；这会重新跑完整集合， +小规模试跑费用另计。暂不执行完整集合或增加每题 attempts，先分析本轮结果。 + +#### 预算与记录计划 + +之前的证书任务实际消耗为 0.00222441 USD，但简单任务不能代表整套集合的平均费用。 +按 2026-09-06 查到的 [OpenRouter 模型价格](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) +和[供应商报价](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints)， +Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 tokens +0.04998／0.009996／0.09996 USD，DeepSeek 当时基础价为 +0.22／0.007／0.66 USD；路由、优惠和时段会影响实际报价。 + +如果每题累计输入 100 万、输出 3 万 tokens，输入缓存命中率 80%，20 题按上述两档 +价格估算约 0.4–1.4 USD；如果增至每题累计输入 300 万、输出 15 万 tokens，则约 +1.4–5 USD。它们是预算场景，不是实测均值或费用上限。50 轮限制也不是美元硬上限。 +这里只估算模型 API 费用，不含云容器、网络或本机运行成本。 + +本轮按以下顺序记录与复核： + +1. 保存 `jobs//config.json`、`lock.json`、任务 content hash 和 20 题清单。 +2. 保存每题 `result.json`、`agent/trajectory.json`、agent 文本日志和 verifier 日志。 +3. 分别统计 reward 为 1 的题数、reward 为 0 的题数、未产生评分的题数和基础设施 + 异常；报告通过题数／计划题数，不能静默缩小分母。 +4. 检查所有 trajectory 的 ATIF 校验、usage 与 cost 完整性，以及 Harbor 指标回填。 + 缺失或部分费用保持未知，已知小计不能当成完整总额。 +5. 记录总 input／cache／output tokens、实际 USD 费用、job 墙钟耗时和逐题耗时。 + Input 已包含 cache tokens，不再把两者相加。 +6. 对失败任务区分解答错误、轮数用尽、执行超时、API 错误、安装／镜像／verifier + 环境故障；保留首次结果，任何后续重跑另建记录。 +7. 用小规模试跑的实测消耗调整完整 89 题预算，再建立单轮 baseline。未来如要评估波动， + 另做多次重复实验，并保留每次结果，不按重跑后的最好成绩替换首次结果。 + +`/jobs/` 已被 Git 忽略。原始结果应另行归档，开发笔记保存可审查的汇总、逐题结果及 +产物位置；不能只留下本机目录名就认为 baseline 已进入版本管理。 + +#### 20 题小规模试跑的实际结果 + +本轮 job 为 `tb21-flash0731-pilot20-20260906`，20 题均已结束；完整数据见 +[机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用完整性；逐次费用回执保存在本地。 + +| 指标 | 实测结果 | +| --- | --- | +| 运行日期 | 2026-09-06 | +| Job 墙钟耗时 | 111 分 10 秒 | +| 通过／未通过／未评分 | 8 ／ 12 ／ 0 | +| 通过率（计划 20 题为分母） | 8 / 20 = 40% | +| 无 Harbor 异常通过 | 7 / 20 = 35% | +| Harbor 异常／自动重试 | 1 ／ 0 | +| 模型回复／工具调用 | 267 ／ 329 | +| Harbor 原始 input／cache／output tokens | 5,853,395 ／ 5,050,368 ／ 311,514 | +| 从最终 ATIF 重新汇总的 input／cache／output tokens | 6,207,708 ／ 5,362,944 ／ 331,041 | +| ATIF 文件校验 | 20 / 20 | +| Harbor 原始指标一致 | 19 / 20 | +| 补查前费用不完整的 trajectory | 13 / 20 | +| Harbor 原始 job 费用汇总 | 0.045695378 USD | +| 所有 trajectory 已知费用小计 | 0.11053603 USD | +| 独立补查后费用 | 0.119200332 USD（完整） | + +下表使用最终 ATIF 中的模型计量信息，费用采用独立补查后的值；原始 +`result.json` 和 trajectory 没有被改写。 + +| 任务 | Reward | 模型回复数 | 末次 stop reason | 费用（USD） | +| --- | ---: | ---: | --- | ---: | +| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | +| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | +| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | +| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | +| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | +| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | +| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | +| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | +| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | +| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | +| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | +| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | +| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | +| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | +| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | +| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | +| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | +| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | +| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | +| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | + +`pytorch-model-recovery` 的 reward=1 与 `AgentTimeoutError` 同时存在。因此 Harbor +原始通过率保留为 **8/20（40%）**，另报**无 Harbor 异常通过 7/20（35%）**，两者都 +以计划的 20 题为分母。这题不能作为正常完成的成功样本。 + +#### 本轮暴露的问题 + +1. **11 道未通过题的末次回复均为 `max_tokens`。** 当前每次回复最多 8192 tokens， + 部分回复的预算主要消耗在 thinking，尚未交付完整解答就结束。 + 当时 agent 将“不再请求工具”视为模型完成处理，未区分因预算耗尽而中断的回复， + 所以这些轨迹的终态仍为 `completed`。这说明需要显式 + 处理截断，并评估 reasoning／output 预算；增加上限后的通过率和费用需要另行实测。 +2. **`mteb-leaderboard` 用尽 50 次模型回复。** 终态 outcome 为 + `max_turns_exhausted`，末次回复仍要求调用工具，verifier 所需的 `/app/result.txt` + 尚未生成。这与单次回复截断是两种不同的预算限制。 +3. **超时后 agent 仍在运行，且发生工具参数异常。** 以 Harbor 开始执行 agent 为 + 起点，`pytorch-model-recovery` 在约第 900 秒达到限制，verifier 约第 901 秒开始； + 内部 journal 记录 agent 在约第 929 秒和第 939 秒又启动两次模型调用。最后一次 + `edit` 只有 `old_text`、`new_text`，缺少 `path`，在显示工具调用信息时触发 + `KeyError`；运行失败记录约在第 1063 秒落盘。 + 因而该异常发生在超时之后，不能用它解释之前的超时。运行与验证发生重叠，该题的 + reward 需要带着这一限制解读。 +4. **Harbor 原始汇总漏掉超时题的计量，费用也有延迟。** 超时采集时 trajectory + 被标记为 missing，最终目录中却已有有效 ATIF，且与独立备份一致；Harbor 的 + tokens 仍为 null，所以原始指标只有 19/20 与最终 ATIF 一致。13 题原始费用为 + partial，共 18 个 generation 在原生对账中未取得费用。独立补查同一 OpenRouter + generation 接口后，18 个回执均成功，**267 次已记录模型回复全部获得费用**。 + 原始 job 的 0.045695378 USD 不能作为总费用；完整实测为 **0.119200332 USD**。 + 两次超时后的调用也计入实际消耗。 + +完整 input 为 6,207,708，其中 cache 为 5,362,944；output 为 331,041。费用依据 +generation 的 `total_cost`，没有按展示价格反推，也不是整个 OpenRouter 账户余额的 +差额。20 题均有评分且 ATIF 校验通过；除上述超时外，Harbor 没有记录其他 trial 异常。 + +原始 job 位于 `jobs/tb21-flash0731-pilot20-20260906/`。本地归档为 +`jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`，包含任务清单、日志、轨迹、 +generation 费用明细、补查回执、Docker 镜像标识和一次性运行／汇总脚本。归档未上传， +其 SHA-256 见机器可读结果。`torch-pipeline-parallelism` 的两份原始日志含运行时 +API 凭据，原件权限已设为 0600，归档副本已脱敏；原件与脱敏副本的校验值保存在 +本地记录中。 + +版本管理仅保存精简的实验配置、逐题评分、token、费用、耗时和异常摘要。请求 ID、 +内部运行 ID、逐次账单回执、精确调用时间、宿主机硬件信息及完整对话日志留在本地。 +这一精简修改作用于最新版；之前已推送的提交仍包含对账元数据，未改写 Git 历史。 + +#### 下一轮计划与费用判断 + +先修复截断处理、缺失工具参数导致的崩溃、超时停止及 trajectory 采集，再完善延迟 +费用补查；这些修复在新 revision 中进行。本轮固定源码版本且没有中途修改 agent。 +随后使用相同 20 个任务 hash 和新的 job 名复跑，对比无异常通过率、终止原因、token +和费用；保留本轮作为修复前的小规模试跑基准。确认运行与计量可靠后，再运行完整 +89 题，并把结果单独记录为完整集合的 baseline。 + +本轮约 0.12 USD，机械地乘以 89/20 约为 0.53 USD，但 11 题提前截断，且题目取自 +列表前 20 项，这个外推不能作为修复后完整集合的预算。按前述每题 300 万 input、 +15 万 output、80% 输入缓存命中的场景，89 题约为 6–22 USD；仍需按当时供应商报价 +和修复后小规模试跑的消耗调整。本轮只执行了 20 题，完整 89 题尚未运行。 + +### 修复批量运行 Terminal-Bench 时遇到的问题 + +以下记录本轮问题的含义、实现与修复方向。“截断处理”“增加模型生成预算”和 +“缺失工具参数导致的崩溃”已实现修复；其余小节仍描述问题与拟议方向，不表示 +对应代码已修复。 + +#### 截断处理 + +原始试跑中，每次模型回复最多生成 8192 tokens。模型尚未输出完就用尽这次预算时，接口 +返回 `stop_reason="max_tokens"`，表示达到生成长度上限，被迫停止。例如，模型 +可能仍在分析如何修改代码，后面的工具调用或完整解答还没有生成，预算就耗尽了。 +本轮部分回复的预算主要消耗在 thinking；11 道未通过题的末次回复均为 `max_tokens`。 + +过去 agent 将“不再请求工具”视为模型完成处理，但生成预算耗尽也会让回复停在 +没有工具调用的位置。结果是任务尚未做完，轨迹却将它记为 `completed`。判断运行 +结果时，需要区分模型正常结束、任务轮数用尽和单次回复被截断；它们说明的是不同 +的停止原因。即使模型正常结束，解答是否正确仍要由 verifier 判断。 + +本次先让运行结果可信：识别截断、保留证据，并明确告知任务尚未完成。遇到截断后 +停止本次运行；增加生成预算和自动恢复作为后续能力，分别评估效果与额外消耗。 + +**2026-09-07 实现：识别截断并停止。** + +截断后的停止原因记录为 `response_truncated`。即使它恰好发生在最后一轮，也保留 +这个原因，让使用者知道最后一次回复本身没有生成完。agent 不再发起新的模型调用， +并跳过该回复中的全部工具调用，包括参数看似完整的调用，避免根据未完成的回复 +执行操作。 + +已经生成的文本、思考内容、工具参数和用量仍保存在执行记录中，费用继续补查， +便于分析预算花在哪里、任务停在哪一步。未执行的工具不会被记成已执行，也不会 +产生虚构的结果。分析结果时，应把“运行是否正常结束”和“任务是否完成”分开判断。 + +使用者会看到明确的截断提示，已输出文字保持可见。Headless 仍以 `0` 退出，由 +verifier 判断任务是否通过。交互模式回到输入提示，用户可以继续发消息；后续对话 +保留已生成文字和截断说明，移除未执行的工具请求与不完整的思考内容，避免下次 +请求带入无法衔接的历史。原始回复仍保留在执行记录中供追溯。 + +新增停止原因超出了旧记录协议允许的范围，因此新记录升级为 Journal v2；已有 v1 +记录仍可读取和转换，公开轨迹继续使用 ATIF-v1.7。模型生成到达上限与日志为控制 +体积而缩短内容是两件独立的事。具体字段、事件顺序和兼容规则见 +[Event Journal v2](../../dev_docs/zh-CN/event-journal-protocol-v2.md)。 + +回归测试覆盖文本、仅 thinking、空回复、最后一轮截断、工具跳过、交互下一轮、 +usage 与费用保留，并通过真实 Anthropic SDK 的本地模拟 SSE 验证半截工具 JSON。 +旧 v1 与新 v2 截断轨迹均通过 Harbor 固定版本的 ATIF validator。测试没有调用付费 +模型；2026-09-07 实现时尚未重跑 benchmark，也没有将这些自动化测试解释为通过率 +提升。2026-09-09 的真实模型复跑记录如下。 + +从 `tb21-flash0731-pilot20-20260906` 中选择 `regex-log` 和 `write-compressor`。 +两题上次 reward 都为 0，末次回复都是 `max_tokens`，但旧轨迹将 outcome 记录为 +`completed`。本次同时检查官方 verifier 的评分，以及当前分支是否准确记录截断。 + +**测试方法与配置。** 在当前 Herdr tab 中向右新建 split，保留原 pane 的焦点， +从仓库根目录运行。两题并发执行，每题只尝试一次，原始 job 保持不变。 + +| 项目 | 本次配置 | +| --- | --- | +| Agent 分支／提交 | `fix/handle-truncated-model-responses` ／ `f7788616e11f3347300ac2fdef6c6400d4eb5611` | +| 容器中的包版本 | `0.8.1.dev11+gf7788616e` | +| Harbor | 0.21.0，通过 `uv run --locked --project benchmarks/harbor` 运行 | +| Dataset | `terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model／API endpoint | `openrouter/deepseek/deepseek-v4-flash-0731` ／ `https://openrouter.ai/api` | +| 生成限制 | 每次回复 8192 tokens；每题最多 50 次模型回复；未显式设置 reasoning effort 或 temperature | +| 环境与重试 | 本地 Docker，并发 2，每题 1 次尝试，Harbor `max_retries=0`；保留任务默认资源与超时 | +| Job | `tb21-truncation-rerun2-20260909-f778861` | + +两题沿用原始任务版本，并检查新旧 trial config 中的任务标识完全一致： + +| 任务 | Task hash | +| --- | --- | +| `terminal-bench/regex-log` | `sha256:802c16cfd132e6c457529cb864be5a757c1b23b6cadc57f2d01983cb0110292a` | +| `terminal-bench/write-compressor` | `sha256:d9ddd9a8e925e2c566b37b2492cbf995afecefe58874e4043ef78d7f3c892c7e` | + +当时分支尚未推送，容器无法按远端 Git revision 安装，因此先对当前提交执行 +`uv build --wheel`，逐文件核对 wheel 中的 Python 源码与该提交一致，再上传到各自 +容器的 `/tmp/`。测试通过临时适配器将安装来源改为这个本地 wheel；模型调用、 +工具执行和 trajectory 导出仍使用当前分支。 +wheel 的 SHA-256 为 +`3c4ebd9268680ba0798ccbc44e6e56ee91424c5773399171050b7ac24734e9e4`。 + +启动脚本先读取宿主机连接配置，移除 `ANTHROPIC_MODEL` +覆盖值并确认 endpoint 为 OpenRouter；凭据通过环境传递，不写入命令参数。 +Harbor 用以下过滤参数选择题目，名称必须包含 `terminal-bench/` 前缀： + +```bash +--include-task-name terminal-bench/regex-log \ +--include-task-name terminal-bench/write-compressor +``` + +本机实际运行入口如下。脚本内部准备 wheel、记录 manifest，再调用 Harbor；脚本与 +临时适配器都属于这次实验的本地产物，并非已提交的通用 benchmark 命令。 + +```bash +uv run --locked --project benchmarks/harbor python \ + jobs/tb21-truncation-rerun2-20260909-f778861-record/workflow/run.py +``` + +同日还运行了相关自动化回归测试，核心 54 项、Harbor 适配器 16 项全部通过： + +```bash +.venv/bin/python -m pytest -q \ + tests/test_truncation.py tests/test_event_journal.py tests/test_atif.py +benchmarks/harbor/.venv/bin/python -m pytest -q \ + -c benchmarks/harbor/pyproject.toml benchmarks/harbor/tests +``` + +**测试结果。** 两题均完成判题，无 Harbor 异常，通过率为 **0/2**；Harbor 报告的 +job 墙钟耗时约 **9 分 42 秒**。两题的截断状态都正确记录，但仍未完成任务。 + +| 任务 | 上次／本次 reward | 本次模型回复数 | 末次回复 tokens | 末次 stop reason | 本次 terminal outcome | Verifier 失败原因 | +| --- | --- | ---: | ---: | --- | --- | --- | +| `regex-log` | 0.0 ／ 0.0 | 1 | 8192 | `max_tokens` | `response_truncated` | 未生成 `/app/regex.txt`，1 项测试失败 | +| `write-compressor` | 0.0 ／ 0.0 | 2 | 8192 | `max_tokens` | `response_truncated` | 未生成 `/app/data.comp`，3 项测试失败 | + +| 任务 | Input tokens | Output tokens | 费用（USD） | +| --- | ---: | ---: | ---: | +| `regex-log` | 1678 | 8192 | 0.00158363 | +| `write-compressor` | 3927 | 8303 | 0.001742255 | +| 合计 | 5605 | 16495 | 0.003325885 | + +费用来自最终 trajectory 中的实际计量，两题费用均完整。两份 ATIF 都通过 Harbor +validator，启动版本包含 `f778861`，日志都有明确的截断提示，末次回复后没有继续 +调用模型。两题末次回复均无工具调用；半截工具参数的跳过与后续历史处理由上述 +自动化测试覆盖，未在这两次实测中触发。Harbor 正常结束且退出码为 0，不代表题目 +通过，评分仍以 verifier 的 reward 为准。 + +这次复跑验证了“识别截断并停止”的行为；它没有提高生成上限，也没有自动续写， +两题仍在交付所需文件前耗尽单次预算。这两次结果不能说明增加预算或自动续写后的 +通过率，后续需要分别实验。 + +原始 trial 日志、trajectory 与 verifier 输出保存在 +`jobs/tb21-truncation-rerun2-20260909-f778861/`。对应的 +`jobs/tb21-truncation-rerun2-20260909-f778861-record/` 保存 `manifest.json`、 +`summary.json`、`results.en.md`、wheel 和 `workflow/` 下的运行、临时安装与汇总脚本。 +这些目录被 Git 忽略，未上传；本文保存版本、方法与结果汇总，不将本地产物视为 +已经进入版本管理的文件。以后重跑须使用新的 job 名，保留这次 0/2 的原始结果。 + +#### 增加模型生成预算 + +**已实现单次生成预算配置，并将默认值从 8192 提高到 32768 进行试用。** +这里的预算指**单次模型回复的生成 token 上限**，不是整题累计 token 或上下文窗口。 +**完整轨迹实验中，`regex-log` 在 32768 下两次通过；`write-compressor` 在 +32768 下两次截断，09-12 显式改用 65536 重跑后通过，最终文件 2263 字节。** +但这次成功运行的最大单轮输出只有 **32073**，未实际用到 32768 以上的输出空间， +不能单凭它证明预算翻倍导致通过。解题耗时 **1976.4 秒**，900 秒检查点仍无目标 +文件，因此这次执行确实需要延长时限。此前的安装失败、服务中断及超时均在下文保留。 + +**其余 18 题在 65536 下首轮通过 5/18（同组 8192 基线为 8/18）；针对 4 题追加 +5 次补跑，没有新增通过。** 这批实验未出现 `max_tokens` 或 3600 秒解题时限终止， +还暴露了 50 轮限制、缺失或不完整工具参数、环境故障；不能把失败统一归因于 thinking +预算，也不能据此认定提高预算改善或降低了通过率。 + +**调研结论：32768、65536 适合作为下一轮实验的候选档位，但公开资料没有给出一个 +可以直接照搬的统一默认值。** TB 2.1 已有明确采用这两个值的评测；code agent 则 +既有约 32k／64k 的默认设置，也有按模型决定或不显式设置的实现。下面分别记录 +评测参数和产品默认行为，避免把模型能力上限当成实际请求参数。 + +**其他 agent／模型跑 Terminal-Bench 时设置多少。** 以下为 2026-09-09 查到的 +第一方评测说明，主看本项目使用的 **TB 2.1**；TB 2.0 仅作补充。数字是发布方 +披露的单次生成上限，不是整道题累计消耗，也不是对其线上请求的独立抓包验证。 + +| Benchmark | 模型与 agent | 披露的单次生成上限 | 影响比较的条件与来源 | +| --- | --- | --- | --- | +| **TB 2.1** | GLM-5.3、GLM-5.3-Flash／Claude Code **2.1.207** | **65536**（`max_new_tokens`） | Z.ai 披露 `temperature=1.0`、`top_p=1`、6 小时 timeout。[GLM-5.3 说明](https://huggingface.co/zai-org/GLM-5.3/blob/main/README.md#footnotes)、[Flash 说明](https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/README.md#footnotes) | +| **TB 2.1** | GLM-5.3-Flash-NVFP4／Terminus-2 | **32768**（`max_tokens`） | RadixArk 的量化版本评测，effort 为 `max`；**关闭了任务 deadline**，存在无法运行及未完成的 trial，不能视为本项目同条件的通过率对照。[评测说明](https://huggingface.co/RadixArk/GLM-5.3-Flash-NVFP4#evaluation) | +| TB 2.0（补充） | Qwen3.6-27B／Harbor + Terminus-2 | **80K**（`max_tokens`，保留原文单位） | Qwen 披露 256K context、3 小时 timeout、32 CPU／48 GB RAM，取 5 次运行平均。[模型卡脚注](https://huggingface.co/Qwen/Qwen3.6-27B#evaluation) | +| TB 2.0（补充） | Kimi-K2.6／Tinker Cookbook 的简化 agent | **8192**（`max_tokens`） | Thinking Machines 的实验使用 32K context、200 轮、无上下文压缩；说明中记录了大量上下文超限。它证明 8192 也被实际采用过，不代表这一预算适合本项目。[实验配置与结果](https://github.com/thinking-machines-lab/tinker-cookbook/blob/main/tinker_cookbook/recipes/harbor_rl/README.md#evaluation) | + +与本项目更接近的 DeepSeek-V4，技术报告披露了代码 agent 使用内部 bash + file-edit +harness、最多 500 步和 512K **上下文**，但该段没有披露单次 `max_tokens`，评测 +还是 TB 2.0。因此，不能把 512K 当生成预算，也不能用它确认本项目 +`deepseek-v4-flash-0731` 在 TB 2.1 上应设置多少。[报告 §5.3.1](https://arxiv.org/html/2606.19348v1#S5.SS3.SSS1) + +**其他 code agent 的默认值。** 默认值必须同时注明版本、模型和配置路径。 +下面的源码链接固定到本次检查的 commit；官方文档按 2026-09-09 查询结果记录。 + +| Agent／检查范围 | 未主动覆盖时的行为 | 依据与边界 | +| --- | --- | --- | +| Claude Code／Opus 4.6 | **64k** | **v2.1.77 发布说明**明确将该模型的默认值提高到 64k；同条中的 128k 是可提高到的上界，不能当成默认值，也不能将该版本结论推广到所有模型。[发布说明](https://github.com/anthropics/claude-code/releases/tag/v2.1.77) | +| Claude Code／无法识别的模型 ID，例如网关自定义名称 | **32000** | 当前官方文档明确列出这个 fallback；可用 `CLAUDE_CODE_MAX_OUTPUT_TOKENS` 覆盖，已识别模型的默认值和上界因模型而异。[环境变量说明](https://code.claude.com/docs/en/env-vars#variables) | +| OpenCode／`830d5eb` | **`min(模型输出上限, 32000)`**；模型输出上限缺失或为 0 时回退到 32000 | 默认请求准备路径使用这一计算结果；环境变量和插件可覆盖。[计算函数](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/provider/transform.ts#L1468)、[请求构造](https://github.com/anomalyco/opencode/blob/830d5eb5354874105cc31599635a80c1662609e8/packages/opencode/src/session/llm/request.ts#L117) | +| pi／`acaa253` 的普通模型请求路径 | **按模型的 `maxTokens` 决定**，再按剩余上下文收紧 | 当前 `buildBaseOptions` 采用 `options.maxTokens ?? model.maxTokens`，并为上下文估算留出 4096 tokens；不是所有模型共用一个固定数值。thinking 的预算还会经过对应 provider 的处理。[源码](https://github.com/earendil-works/pi/blob/acaa253cc8e3f159e6100b6f3874861b1f0bfc99/packages/ai/src/api/simple-options.ts#L11) | +| mini-swe-agent／`04d809c` 的默认配置与 LiteLLM 路径 | **不显式设置统一的 `max_tokens`** | 默认 YAML 未指定，模型层透传 `model_kwargs`；实际值由选用配置、LiteLLM 和服务端共同决定，省略参数不等于无限生成。[默认配置](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/config/default.yaml)、[模型调用](https://github.com/SWE-agent/mini-swe-agent/blob/04d809ceab9df28f9adaed044884180159172930/src/minisweagent/models/litellm_model.py#L64) | +| Codex CLI／官方配置参考 | **本次未确认统一的默认数字** | 所查官方配置参考未给出单次生成上限的默认值；不能拿 API 模型页上的最大输出能力代替 CLI 的实际默认设置。[配置参考](https://learn.chatgpt.com/docs/config-file/config-reference) | + +这些数字也提醒我们：**32000 与 32768 要分别记录**，不能都简写成“32k”后当成同一 +配置；只披露 `64k`、`80K` 的来源则保留原文单位。thinking 上限、`high`／`max` +这类 effort 档位也要与总生成上限分开记录；本次尚未确定独立 thinking 参数的设计。 + +**实现与自动化验收。** 实现提交为 `7a610cbf8`。配置入口采用 `--max-tokens`、 +`ANTHROPIC_MAX_TOKENS` 和现有 `settings.json` 的 `env`，优先级为 +**CLI > 环境变量 > 配置文件 > 32768**；交互和 headless 都生效，启动时解析一次， +每轮请求使用同一值。最终值必须是正整数，非法值在请求前以退出码 `2` 报错。 +配置文件沿用现有 `env` 字符串规则，例如 `"ANTHROPIC_MAX_TOKENS": "32768"`。 + +Harbor 支持 `--agent-kwarg max_tokens=N` 和 `ANTHROPIC_MAX_TOKENS` 透传；省略 +kwarg 时不强制插入 CLI 默认值，以便环境配置及所安装版本的默认值生效。 +启动提示、Journal 的 `run.started.max_tokens` 和 ATIF 的 `agent.extra.max_tokens` +都记录生效上限，实际 usage 另行记录。旧 Journal 缺少此字段时仍可转换，ATIF 不会 +为历史记录补写一个推测的预算。 + +```bash +nanoPyCodeAgent --max-tokens 65536 -p "fix the failing tests" +# Harbor: add --agent-kwarg max_tokens=65536 to the run command +``` + +核心 **242 项测试**、Harbor adapter **22 项测试**通过，覆盖配置优先级、交互与 +headless 每轮请求的预算、非法值提前失败、记录与实际 usage 分离，以及真实 SDK +请求序列化。**65536 的真实模型结果见下文 09-12 实验；参数透传通过不等于效果归因成立。** + +**即使增大上限，也必须保留 `stop_reason="max_tokens"` 的处理。** 任何有限预算 +都有可能耗尽。提高上限可以给模型更多生成空间；现有截断处理负责准确识别停止原因。 +上次两题均用尽 8192 tokens，为评估更高预算提供了对照，但不能保证提高后一定通过。 + +增大预算后的截断回归也已通过：遇到 `max_tokens` 仍记录 `response_truncated`， +保留 usage 与费用，跳过该回复中的工具调用。本次没有新增独立 thinking 参数或 +自动续写。 + +**32768 的真实模型实验。** 使用新 job +`tb21-budget32768-20260909-7a610cb`，以 +`tb21-truncation-rerun2-20260909-f778861` 的 8192 结果作为对照。保留同一 +TB 2.1 dataset、两题 hash、OpenRouter 的 `deepseek/deepseek-v4-flash-0731`、 +`max_turns=50`、每题 900 秒原生 timeout、并发 2、每题 1 次且不重试。 +从实现提交构建本地 wheel，并逐个核对包内 Python 源文件与该提交一致。 +实验清除 `ANTHROPIC_MAX_TOKENS`，也不传 `--max-tokens`，验证内置默认值。 +`regex-log` 首次在安装阶段超过默认 360 秒，尚未进入模型执行，也没有 verifier +评分；该记录保留在原 job。另建 +`tb21-budget32768-regex-setup-retry-20260909-7a610cb` 仅补跑这题，将安装 timeout +放大为 1080 秒（`--agent-setup-timeout-multiplier 3`），解题 timeout 仍为 900 秒。 +`write-compressor` 首次进入模型执行，第一轮正常完成工具调用；整次执行约 421 秒 +时，第二轮收到服务端 `stream closed before completion`，Harbor 记录 `UnknownApiError`， +verifier reward 为 0。它没有返回 `max_tokens`，不能作为预算耗尽的证据。 +已完成首轮的 usage 为 input 1528／output 103，费用为 $0.00011786；中断调用的 +完整 usage 与费用未知，不能把这一已知小计当成整题总费用。 + +为排除一次性服务故障，再以相同模型、预算和解题时限补跑一次，保存为 +`tb21-budget32768-compressor-api-retry-20260909-7a610cb`；安装窗口同样为 1080 秒。 +两次模型运行均保留，不以补跑覆盖首次失败。 + +**最终结果（2026-09-09 执行，09-10 核对归档）。** 下表的时间是 Harbor 记录的 +agent 执行时间，不含安装与 verifier；首次安装失败没有模型执行时间或评分。 + +| 题目 | 8192 基线 | 32768 首次运行 | 32768 补跑 | +| --- | --- | --- | --- | +| `regex-log` | reward **0**；`response_truncated`；187.3 秒 | `AgentSetupTimeoutError`；未调用模型，未评分 | reward **0**；`AgentTimeoutError`；900.9 秒 | +| `write-compressor` | reward **0**；`response_truncated`；304.4 秒 | reward **0**；`UnknownApiError`；421.4 秒 | reward **0**；`AgentTimeoutError`；900.9 秒 | + +两次补跑的启动日志均确认 `max tokens 32768`，agent 版本和题目 hash 与实验配置 +相符。verifier 分别报告 `/app/regex.txt` 和 `/app/data.comp` 不存在。 +包括首次服务端中断在内，共有 **3 次进入模型执行且获评分的运行，全部为 0**；另有 +1 次安装失败。没有挑选一次补跑结果来替换原始失败。 + +**完整用量与成本比较未能完成。** 两次超时均未导出最终 ATIF，Harbor 将 trajectory +记为 `missing`，input／output tokens 和 cost 都为 `null`。模型调用次数、末次 +`stop_reason` 和实际截断次数也无法从现存记录完整恢复，不能将缺失值写成 0。 +8192 基线的完整费用分别为 $0.00158363、$0.001742255；32768 仅有前述首次 +`write-compressor` 已完成首轮的 $0.00011786 小计，无法计算可靠的总费用增幅。 + +因此，**保留 32768 作为本轮试用默认值，但不将 09-09 的实验写成提高预算后的成功验收**。 +参数配置、请求透传和截断回归已验证；真实模型实验仍受服务端中断、任务 timeout +和超时后 trajectory 缺失影响。没有完整的末次回复，不能断言更高预算消除了 +`max_tokens` 截断，也不能由这两题推导整套 benchmark 的收益。 + +三个 job 的 wheel 完全相同，SHA-256 为 +`91996b1d4c70af8062318a38b3af9235c85d93c9685e10ac1323f3bef3625425`。 +原始日志、verifier 与异常保存在上述各 job；对应的 `-record/` 保存 manifest、 +wheel 和运行脚本。统一汇总位于 +`jobs/tb21-budget32768-20260909-7a610cb-record/summary.json`、`results.en.md` +及 `workflow/summarize.py`。这些 `jobs/` 产物被 Git 忽略，未上传；相关容器已清理。 + +**延长时限并保留完整 trajectory（2026-09-10）。** 新 job +`tb21-regex-3600s-trace-20260910-7a610cb` 的 `regex-log__2A9PJgL` 得到 +**reward 1.0**，官方 verifier 的 `test_regex_matches_dates` 通过。 +Harbor 记录 agent 执行 **580.4 秒**，不含安装和验证；因此，这次成功并未用到 +原 900 秒以外的时间，不能据此认定延长时限解决了此前超时。 + +继续使用上述同一 wheel、题目 hash、模型、内置 `max_tokens=32768` 和 50 轮上限， +单题、单次、不重试。将解题执行窗口改为 **3600 秒**，另留 120 秒用于中断和导出； +Harbor 外层窗口设为 3780 秒（`--agent-timeout-multiplier 4.2`），避免提前杀死 +清理过程。安装仍为 1080 秒，verifier 时限不变。临时诊断包装器将未截短的 Journal、 +HTTP 响应体、逐块到达时间和每 120 秒的线程栈直接写入持久日志目录；agent 源码、 +提示词和工具保持本次对照配置。本次自然完成，未触发中断或轨迹重建。 + +| 观测项 | 结果 | +| --- | --- | +| 模型回复／工具调用 | **5 轮／6 次**；前 4 轮为 `tool_use`，末轮为 `end_turn`，无 `max_tokens` 截断 | +| 模型累计耗时 | **567.3 秒**，占原生 run 时间约 **97.9%** | +| 首轮／后续各轮耗时 | **502.4 秒**／35.1、2.8、4.2、22.8 秒 | +| 本地工具累计耗时 | **0.023 秒** | +| 完整 usage | prompt **74650**（含 cache **53504**），completion **17264** | +| 完整费用 | **$0.005338074**，5 次调用均已完成费用核对 | + +**证据指向本次首轮持续生成 thinking，而非本地工具耗时。** 首轮在请求后约 +2.5 秒开始返回 thinking，持续到第 500.8 秒，第 501.7 秒才开始输出工具参数； +非空生成片段之间最长间隔仅 **3.4 秒**，没有观察到长时间断流。该轮保留了 +47007 个 thinking 字符，服务端报告 output **15123 tokens**，其中 +`output_tokens_details.thinking_tokens` 为 **12026**。字符数与服务端 token +口径不同，不能把两项 token 数的差额直接当成可见文本量。 + +完整性检查通过：**147 条 Journal 记录均未截短**，原生 ATIF 通过 Harbor schema +校验，并与 Journal 重建结果完全一致；5 个响应流都有 `message_stop`，5 次请求均 +实际发送 32768。记录使用单调时钟分析流式耗时，避免系统时钟微调影响。诊断包装器的 +正常结束、模拟卡住后中断、强制终止后恢复三个离线场景也均通过。 + +**仍未确定旧超时的真正原因。** 旧运行缺少完整回复与流式时间线，无法区分当时的 +长时间生成、服务端／网络停滞或不同解题路径。这次记录了实际容器依赖 +Anthropic SDK **1.4.0**、httpx **0.28.1**、httpx2 **2.12.0**；旧容器未保存对应 +版本，因此不能认为同一 wheel 就等于全部运行条件相同。该结果证明这一题在 32768 +下能够完成，但单次成功不能证明延长时限的因果收益，也不能推广到另一题或整套评测。 + +完整 ATIF 位于上述 job 的 `regex-log__2A9PJgL/agent/trajectory.json`；同级 +`journals/`、`http/`、`http-events.jsonl` 和 `python-stacks.log` 保留原始诊断证据。 +对应 `-record/` 下的 `results.en.md`、`summary.json`、`validation.json` 和 +`workflow/` 保存诊断报告、完整性核对和复现脚本。产物保留在本地 `jobs/`，被 Git +忽略且未上传；容器已清理，此前失败记录保留。 + +**同法补跑 `write-compressor`（2026-09-11）。** 新 job +`tb21-compressor-3600s-trace-20260911-7a610cb` 的 `write-compressor__GTmsjFV` +得到 **reward 0**。官方 verifier 的 3 项测试均因 `/app/data.comp` 不存在失败。 +Harbor 记录 agent 执行 **1449.1 秒**（约 24 分钟），无基础设施异常，原生 trajectory +完整导出；最终 outcome 为 **`response_truncated`**，没有触发 3600 秒超时。 + +方法沿用上次诊断：同一模型和 wheel、内置 `max_tokens=32768`、50 轮上限、单题 +单次且不重试；执行／清理／Harbor 外层窗口分别为 3600／120／3780 秒，安装仍为 +1080 秒。诊断包装器与上次逐字节一致，继续保存未截短的 Journal、原始响应流、 +到达时间和线程栈。题目 hash 与此前 `write-compressor` 基线一致。 + +| 模型回复 | 耗时 | output tokens | 服务端报告的 thinking tokens | 停止原因 | +| --- | --- | --- | --- | --- | +| 第 1 轮 | **5.8 秒** | 118 | 16 | `tool_use` | +| 第 2 轮 | **1414.2 秒** | **32768** | **32767** | **`max_tokens`** | + +**这次明确观察到“持续长思考 → 超过原时限 → 用尽生成预算 → 未交付文件”。** +第 1 轮执行了 `read` 和 `bash`，合计仅 **0.004 秒**。第 2 轮约 0.9 秒开始返回 +thinking，持续到第 1414.1 秒；非空生成片段最长间隔 **2.2 秒**，没有长时间断流 +迹象。该轮完整保留 **116281 个 thinking 字符**，没有可见文本或工具参数输出。 +当前 agent 在 `max_tokens` 后结束 run，未自动续写，因此没有后续机会生成文件。 + +在原时限附近的 **第 900.4 秒**，独立只读检查确认 `data.comp` 不存在，仅完成 +首轮回复和两次工具调用，第二轮仍在输出 thinking。这证明本次执行无法在原窗口内 +交付；延长窗口让它继续到生成预算耗尽，**单独延长时限没有解决本次失败**。 +这条时间线解释了本次过程，不能反推旧运行中缺失的轨迹，也不能将此前服务端中断 +与本次截断混为同一个原因。 + +完整 usage 为 prompt **3926**、cache **0**、completion **32886**，费用 +**$0.00615504**。累计输出大于 32768，是因为预算按单次回复计算。两轮费用均已 +核对；第二轮费用查询先返回 4 次 HTTP 404，第 5 次成功，该等待发生在回复截断后。 +Harbor 的安装和 verifier 分别耗时 143.9、226.2 秒，均不计入上述 agent 执行时间。 + +**完整轨迹与模型回复被截断是两回事。** 本次 **13 条 Journal 均未截短**，包括 +超过默认 10 万字符日志阈值的 thinking；原生 ATIF 通过 Harbor schema 校验，并与 +Journal 重建结果一致。两个响应流均完整接收、有 `message_stop`，实际请求均为 +32768。实际容器的 Anthropic SDK 为 **1.5.0**，上次 regex 诊断为 1.4.0；同一 +wheel 的依赖范围允许安装到新版本，不能把相同方法等同于全部依赖和后端条件固定。 + +完整 ATIF 位于该 job 的 `write-compressor__GTmsjFV/agent/trajectory.json`，同级 +目录保留 Journal、HTTP 原始响应与时间线、线程栈和运行信息。对应 `-record/` 下 +保存 `results.en.md`、`summary.json`、`validation.json`、`checkpoints/` 和复现 +脚本，其中包含原 900 秒截止点的文件检查。产物仍在本地、被 Git 忽略且未上传， +容器已清理；此前所有失败及 regex 通过记录保留。 + +**再次各跑一题（2026-09-11）。** 本轮串行运行 `regex-log`、`write-compressor`， +各一次、不自动重试；第一题验证及容器清理完成后才启动第二题。模型、wheel、题目 +hash、32768 内置预算、50 轮上限及 3600／120／3780 秒窗口均沿用前次诊断。 + +| 题目 | 前次完整诊断 | 本次重复实验 | +| --- | --- | --- | +| `regex-log` | reward **1**；580.4 秒；5 轮 | reward **1**；**631.2 秒**；9 轮，末轮 `end_turn` | +| `write-compressor` | reward **0**；1449.1 秒；第 2 轮 `max_tokens` | reward **0**；**582.1 秒**；第 3 轮 `max_tokens` | + +时间均为 Harbor 的 agent 执行时间，不含安装与 verifier。**本次两题都未用到 +原 900 秒以外的解题时间**，没有 deadline、强制终止或轨迹重建。 + +regex 首轮仍耗时约 **517.4 秒**，使用 16445 output tokens，其中服务端报告 +16346 为 thinking tokens；之后完成 8 次工具调用，累计仅 0.039 秒。9 轮回复 +均未截断，官方 `test_regex_matches_dates` 再次通过。 + +compressor 本次第 2 轮用 **172.5 秒、5463 output tokens** 正常完成工具调用， +走到了第 3 轮；第 3 轮再用 **394.8 秒**耗尽 **32768** 总生成预算，服务端报告 +其中 **28961** 为 thinking tokens。捕获流包含 **112785 个 thinking 字符**， +没有可见文本或工具参数；不能把 token 字段的差额推算为可见文本。该轮非空生成 +片段最长间隔 1.5 秒。全部 3 次工具调用合计约 0.004 秒，最终仍没有 `data.comp`， +官方 3 项测试因此失败，outcome 为 `response_truncated`。 + +| 题目 | 完整 prompt tokens（含 cache） | Cache tokens | Completion tokens | 完整费用 | +| --- | --- | --- | --- | --- | +| `regex-log` | 161870 | 137216 | 20400 | **$0.00720948** | +| `write-compressor` | 12704 | 1280 | 38335 | **$0.00764908** | + +两题的 **256／18 条 Journal 均未截短**；原生 ATIF 均通过 Harbor schema 校验， +并与 Journal 重建结果一致。全部 12 个模型响应流均完整接收、有 `message_stop`， +请求实际发送 32768，所有调用费用均已核对。安装分别耗时 833.6／448.1 秒， +verifier 分别耗时 114.9／264.6 秒，均单列而不混入解题时间。 + +**对照中的两次 regex 均通过，两次 compressor 均耗尽生成预算，但耗时和停止轮次 +明显不同。** 本次 compressor 在 900 秒以内就失败，进一步确认这一次的停止条件是 +单次生成上限。两次观察不足以断言以后必过／必败，也没有验证更高预算必定有效。 +本轮两容器均记录 Anthropic SDK 1.5.0，其余已记录包版本一致；前次 regex 为 1.4.0。 +采样、缓存和后端条件没有固定，不能将时间差异直接归因于某一个因素。 + +本轮 job 分别为 `tb21-regex-3600s-trace-repeat2-20260911-7a610cb` 和 +`tb21-compressor-3600s-trace-repeat2-20260911-7a610cb`，trial 分别为 +`regex-log__NmpJwcQ`、`write-compressor__NqmJQWm`；各自 `agent/trajectory.json` +及同级 Journal／HTTP／线程栈保留完整证据。各 `-record/` 保存报告和复现脚本， +统一对照在 `jobs/tb21-3600s-trace-repeat2-20260911-7a610cb-record/` 的 +`results.en.md` 与 `comparison.json`。原始产物仍在本地、被 Git 忽略且未上传， +两容器已清理，此前记录全部保留。 + +**65536 重跑 compressor（2026-09-12）。** 本次显式传入 +`--agent-kwarg max_tokens=65536`，其余沿用前次 compressor 的模型、wheel、题目 +hash、50 轮及 3600／120／3780 秒窗口；单题一次、不自动重试，项目默认值仍为 +32768。最终 **reward 1，官方 3 项测试全部通过**：`data.comp` 为 **2263 字节**， +解压内容与原文件一致。 + +| 项目 | 本次结果 | +| --- | --- | +| 解题时间 | **1976.4 秒**；安装 278.2 秒、verifier 169.1 秒另计 | +| 模型回复／工具调用 | **32 轮／32 次**；末轮 `end_turn`，outcome `completed` | +| 最大单轮输出 | **32073 tokens**；所有请求实际发送 65536，无截断 | +| 完整 prompt／cache／completion tokens | **1528746／1471744／59563**；prompt 含 cache | +| 完整费用 | **$0.018819072**，32 次调用费用全部核对完成 | + +主要耗时仍是第 2 轮：**1179.7 秒**、32073 output tokens，其中服务端报告 +27900 thinking tokens；随后正常返回 `tool_use`。完整流含 108811 个 thinking +字符，非空生成片段最长间隔 2.2 秒。全部工具累计 27.3 秒。900.4 秒检查点尚无 +目标文件，1299.7 秒时为 2727 字节、1449.9 秒时为 2388 字节，最终再缩至 2263。 + +**本次证明“65536 配置下重跑通过”，但没有证明“32768 一定不够”。** 所有单轮 +输出都低于 32768，采样、缓存和后端未固定，不能把通过直接归因于翻倍预算。 +相比之下，本次轨迹确实用到了原 900 秒以外的时间。运行环境记录的 SDK 1.5.0 +及其他依赖版本与前次 compressor 一致；没有 deadline、强杀或轨迹重建。 + +498 条 Journal 均未截短，原生 ATIF 通过 schema 校验并与 Journal 投影一致。 +32 个模型响应流均完整接收并含 `message_stop`；一次费用查询 GET 在响应头前 +`ConnectError`，重试后成功，未中断模型回复。原分析器对“所有 HTTP 请求均收到 +完整响应”的检查因此为 false，`validation-notes.json` 保留并解释该区别。 + +job 为 `tb21-compressor-65536-3600s-trace-20260912-7a610cb`，trial 为 +`write-compressor__hzfqDFs`；完整 `agent/trajectory.json`、Journal、HTTP 流与 +时间线均保留。对应 `-record/` 保存报告、校验、检查点和复现脚本，`task-output/` +另存最终压缩文件及输入／解压器源码和 hash。产物仍在本地、被 Git 忽略且未上传， +容器已清理，先前结果全部保留。 + +**65536 扩展到其余 18 题（2026-09-12～13）。** 首轮耗时约 2 小时 57 分， +结果为 **5 题通过、8 题 reward 0、5 题未评分**，即 5/18（27.8%）；同组 +8192 基线为 8/18。通过的是 `pypi-server`、`openssl-selfsigned-cert`、 +`log-summary-date-ranges`、`pytorch-model-recovery` 和 `merge-diff-arc-agi-task`。 +随后 4 题共补跑 5 次，均未新增通过。首轮与补跑分别记录，保留所有失败。 + +继续使用 `7a610cb` 的同一已校验 wheel、DeepSeek V4 Flash 0731／OpenRouter、 +相同题目 hash；18 题的缓存镜像 digest 也与基线一致。显式设 `max_tokens=65536`、 +最多 50 轮、并发 2；解题 3600 秒、收尾 120 秒、Harbor 外层 3780 秒、安装 +1080 秒，verifier 保留题目原时限。每题每个 job 一次，不自动重试整题。 +**项目默认值仍为 32768，65536 是本次实验覆盖值。** + +表中 `—` 表示未得到官方评分，不计作通过；reward 0 也可能来自基础设施或记录器 +故障，需结合原因判断。 + +| 题目 | 8192 基线 | 65536 首轮 | 主要结果 | +| --- | ---: | ---: | --- | +| `torch-tensor-parallelism` | 0 | — | 首个响应流中断；verifier 下载 Torch／CUDA 依赖超时 | +| `schemelike-metacircular-eval` | 0 | 0 | 第 19 轮响应流中断；解释器仅通过 4/63 子例 | +| `kv-store-grpc` | 1 | 0 | `SetValRequest` 缺少要求的 `value` 字段；通过 5/7 测试，服务存活检查通过 | +| `pypi-server` | 1 | 1 | 本地包安装及 API 测试通过 | +| `dna-assembly` | 0 | — | 安装 uv 时 TLS 下载失败，未调用模型 | +| `torch-pipeline-parallelism` | 0 | — | 耗尽 50 轮；verifier 下载依赖超时，无功能测试结果 | +| `qemu-alpine-ssh` | 1 | — | 安装依赖时 APT 包返回 404，未调用模型 | +| `openssl-selfsigned-cert` | 1 | 1 | 6/6 测试通过 | +| `regex-chess` | 0 | 0 | 耗尽 50 轮；初始局面生成非法后继，仅通过长度检查（1/4） | +| `log-summary-date-ranges` | 1 | 1 | 2/2 测试通过 | +| `model-extraction-relu-logits` | 0 | — | 获取 uv 安装脚本时 TLS 失败，未调用模型 | +| `path-tracing` | 0 | 0 | 耗尽 50 轮，缺少 `image.c`；0/5 测试通过 | +| `caffe-cifar-10` | 0 | 0 | 耗尽 50 轮；末次构建缺少 `g++`，二进制及模型未生成；仅通过 prototxt 检查（1/6） | +| `mteb-leaderboard` | 0 | 0 | 耗尽 50 轮，缺少 `result.txt`；0/2 测试通过 | +| `llm-inference-batching-scheduler` | 1 | 0 | 记录器在约 120 秒定时转储栈时 SIGSEGV，计划文件缺失 | +| `pytorch-model-recovery` | 1 | 1 | 5/5 测试通过 | +| `circuit-fibsqrt` | 0 | 0 | 电路计算错误；空 `bash` 参数触发原生 `KeyError('command')`，中止修复；通过 2/3 测试 | +| `merge-diff-arc-agi-task` | 1 | 1 | 5/5 测试通过 | + +**补跑只用于排除已确认的环境／记录器问题，结果不覆盖首轮。** 3 个安装失败任务 +先用缓存 uv 和 wheel 绕过下载／APT 故障；scheduler 单独移除定时栈转储后再跑。 +缓存安装方案遗漏了 DNA 镜像需要的系统 CA 证书，因此 DNA 又修正安装步骤重跑一次。 + +| 题目 | 追加次数 | 补跑结果 | +| --- | ---: | --- | +| `dna-assembly` | 2 | 第一次因上述 CA 遗漏，在收到响应头前连接失败，不能评价模型；恢复原有全部系统依赖后，完成 3 轮，但不完整 `bash` 参数触发 `KeyError('command')`，缺少 `primers.fasta`，0/1 测试通过 | +| `qemu-alpine-ssh` | 1 | 完成 3 轮后，不完整 `bash` 参数触发 `KeyError('command')`；verifier 又遇 APT 404，pytest 未运行，脚本虽记 reward 0，不能视为功能测试失败 | +| `model-extraction-relu-logits` | 1 | 正常完成 14 轮，但恢复矩阵的第 11 行不匹配，官方测试失败 | +| `llm-inference-batching-scheduler` | 1 | 无 SIGSEGV；完成 15 轮后，不完整 `write` 参数触发 `KeyError('content')`，计划文件缺失，仅通过输入完整性检查（1/6） | + +记录器和安装方法的差异也纳入证据。18 题开跑前已修正 supervisor 的正常退出清理： +保留 agent 启动的后台服务供 verifier 使用，仅在 deadline 时清理进程组；离线服务 +测试通过，`kv-store-grpc` 的服务存活测试也通过。首轮 scheduler 的系统 core 元数据、 +同镜像 ELF 与反汇编定位到定时栈转储中的 `PyCode_Addr2Line+11`；该调用路径与 +[CPython 定时转储实现](https://github.com/python/cpython/blob/v3.13.7/Modules/faulthandler.c#L537) +及[栈帧转储实现](https://github.com/python/cpython/blob/v3.13.7/Python/traceback.c#L842) +一致，但未确定底层内存错误机制。补跑保留原安装方法，只去掉定时转储，未再崩溃。 +DNA 则用同镜像、不带密钥的公开 HTTPS 请求验证：缺 CA 时证书验证失败，补装后 +返回 HTTP 200；修正后的正式补跑恢复 `curl`、`bash`、`git`、`python3` 和 CA +证书，再安装缓存 uv／wheel。这两类诊断错误不能算作模型预算不足。 + +**对生成预算的直接结论：继续加大上限不能统一解决当前失败。** + +- 所有本次 65536 运行均未报告 `max_tokens`，也没有触及 3600 秒解题时限。 + 实际单轮超过 32768 的只有 Scheme（38496）、regex-chess（60483）和修正后 + 的 DNA（35730），三题仍未通过。通过的 compressor 峰值为 32073，其余通过题 + 均不超过 6015。 +- pipeline、regex-chess、path-tracing、Caffe、MTEB 首轮耗尽 **50 轮**；这是 + 总调用轮数限制，与单次生成预算不同。 +- circuit、QEMU、scheduler 补跑和 DNA 修正补跑暴露了另一项 agent 健壮性问题： + 响应流完整结束于 `tool_use`，却给出缺失或不完整的工具参数，SDK 得到空字典； + 原生渲染器直接读取必需字段而抛出 `KeyError`。其中 circuit 的原始 JSON 是 + `{}`，QEMU／scheduler 是 `{`，DNA 是未闭合字符串，不能全部称为合法空 JSON。 + 下一步应单独处理参数校验和错误反馈，使 agent 有机会修复；本次没有修改这部分实现。 + +这些是诊断观察，**不是只改变预算的受控实验**：8192 基线源码为 `2b83097`， +使用原题目时限，依赖、采样、缓存和后端也未固定。因此既不能从 8/18 → 5/18 +认定增大预算有害，也不能把 compressor 的一次通过归功于预算翻倍。`regex-log` +此前两次通过使用的是 32768，本次未在 65536 下重跑；不把这些不同条件拼成统一的 +20 题通过率。 + +其余 18 题及 5 次补跑的已核对费用下界为 **$0.261769566**；加上 compressor， +本次 24 次尝试合计至少 **$0.280588638**。tensor-parallelism 的费用查询三次 +返回 404，完整总费用和 token 用量仍未知；中断的 Scheme／scheduler 调用已单独 +补查账单，不能用账单修补缺失的原生 usage，也不把未知值当作 0。 + +[结构化实验结果](../../../benchmarks/harbor/results/tb21-generation-budget-65536-20260912.json) +收录配置、源码／wheel hash、逐题结果、费用边界及各 job／trajectory 的本地路径。 +24 次尝试覆盖 19 题：**20 份原生 ATIF、1 份明确标记的崩溃后重建 ATIF、3 次 +安装未完成而无轨迹**；所有现存 ATIF 均通过 schema 校验，原生轨迹与完整 Journal +投影一致，无 Journal 字符串截短。实际收到的 HTTP 流（含中断流）、时间线、运行时 +版本、校验报告及诊断脚本均保留在 `jobs/`；主记录目录为 +`tb21-remaining18-65536-3600s-trace-20260912-7a610cb-record`，其中 +`comparison.json` 索引全部分组，`results.en.md` 记录首轮 18 题。 +原始轨迹被 Git 忽略且未上传，所有实验及诊断容器已清理。 + +#### 截断后自动续写 + +当前收到 `max_tokens` 就结束本次 run，即使还有剩余模型轮数也 +不会继续。新需求是在配置允许且仍有剩余预算时，由 agent 自动发起后续模型调用， +继续完成原任务，使 headless 运行不必依赖用户手动发送“继续”。 + +预期行为与约束： + +- 提供自动续写的配置开关及有限的续写次数上限。每次后续模型回复计入同一次 run + 的 `max_turns`，不能通过恢复流程重置轮数；默认开关、次数与其他预算限制待设计。 +- 保留已经成功执行的工具结果和任务上下文，为后续请求构造有效历史。截断回复中的 + 工具调用仍不执行，不把半截参数或无匹配结果的工具调用直接重放；只有 thinking、 + 没有可见文本的截断也需要有明确恢复方式。具体续写提示和请求构造方式待设计。 +- 在 Journal／ATIF 中保留每一次 `max_tokens` 的事实，记录后续尝试及全部 usage、 + 费用。继续生成期间不提前发出 run 终态；最终正常结束、达到续写限制、轮数耗尽或 + 请求失败时，准确记录原因。恢复后的终态规则与协议表达需要一并设计。 +- 达到配置上限、收到取消或发生无法恢复的错误时停止，并给出明确诊断，避免无限 + 续写。自动续写能继续处理任务，是否做对仍由 verifier 判断。 + +自动化验收至少覆盖：截断后继续并完成、连续截断后达到限制、剩余轮数耗尽、只有 +thinking 的回复，以及截断工具参数不被执行或错误重放。真实模型验收使用上述两题 +和新的 job 名，在相同单次预算下比较关闭／开启续写的结果，再评估“增加预算＋ +自动续写”的组合；分别报告通过率、追加模型调用数、总 tokens、费用和耗时。 +自动续写尚未实现；前述 8192 下的 0/2 结果及提高预算后的实验均不属于自动续写验收。 + +#### 缺失工具参数导致的崩溃 + +模型调用工具时漏传了必填参数，agent 没有把它作为可反馈给模型的调用错误处理， +而是抛出 Python 异常，导致整次运行失败。本轮具体发生在 `pytorch-model-recovery` +任务的最后一次 `edit` 调用中。 + +[`edit` 工具定义](../../../src/nanopycodeagent/edit_tool.py) 要求三个必填参数： + +- `path`：修改哪个文件。 +- `old_text`：要替换的原文。 +- `new_text`：替换后的内容。 + +本次调用只有 `old_text` 和 `new_text`，缺少 `path`，相当于模型说明了如何替换 +文字，却没有指定文件。工具 schema 已经把 `path` 声明为必填，但本次收到的实际 +参数仍然缺失，因此程序需要在使用参数前检查。 + +这次错误发生在显示工具调用信息时，尚未执行文件修改。因为缺少目标路径，连展示 +这次调用的意图都触发了 `KeyError: 'path'`，导致整个任务退出。 +异常发生在 Harbor 超时之后，不能用它解释此前的超时。 + +需要同时覆盖预览和执行两处：打印预览时能容忍缺失参数；执行工具前校验参数， +缺少必填项时不执行该工具，而是返回明确的 `tool_result`，标记 `is_error=True`， +例如： + +```text +Missing required argument: path. Provide the file path and retry. +``` + +这样模型可以在后续轮次看到错误，补齐参数重新调用，agent 在剩余预算内继续工作。 +展示和执行两个环节都需要容忍不完整输入，否则只是把崩溃推迟到下一步。处理原则 +是把模型可纠正的参数错误反馈为工具结果，让任务继续，而不是直接结束整个任务； +其他工具也应遵循这一原则。 + +**2026-09-13 实现：工具参数校验与错误反馈。** 实现提交为 `27a9503`。 +`read`、`write`、`edit`、`bash` 在预览和执行前共用参数校验，检查必填字段、 +schema 中声明的字符串／整数／布尔类型，以及路径和命令中的 NUL；未知工具名 +也返回错误。参数不做隐式类型转换，例如 `replace_all="false"` 不会被当作真值， +布尔值也不能充当读取行号。schema 允许的额外字段、空文件内容和空替换文本仍可使用。 + +无效调用不进入工具实现，而是返回匹配原调用 ID 的 `tool_result`，设置 +`is_error=True` 并说明如何纠正。预览显示该调用未执行；同轮其他有效调用仍按顺序 +处理，已经成功的工具不会因为恢复而重新执行。下一次模型回复沿用原任务、历史 +和剩余轮数，不新增独立重试预算。工具内部意外抛出的编程错误仍向外传播，避免 +把所有 `KeyError` 都误报为模型参数错误。 + +同时检查响应流中实际收到的参数 JSON。SDK 会在流式接收期间解析部分 JSON；即使 +缺少结束括号时已经得到包含必填字段的字典，也不能据此执行命令。流完整结束于 +`tool_use`、但参数 JSON 不完整时，本次调用被拒绝并要求重发完整对象。Journal／ +ATIF 保留错误、原始参数 JSON，以及 SDK 已解析的输入；非对象输入在兼容现有 +object 字段约束的同时，另存为 `raw_input`。后续请求使用可发送的对象形式， +不会把半截 JSON 重放给接口。`max_tokens` 的原有处理保持不变：整条截断回复中的 +工具仍全部跳过，运行记录为 `response_truncated`，本次没有实现自动续写。 + +**自动化与独立审查。** 锁定依赖下的 276 项核心测试全部通过；临时环境固定为 +上一轮实跑报告的 Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0 和 +Pydantic 2.13.5，再跑同一核心套件也为 276 项通过。Harbor adapter 的 22 项 +测试通过。新测试覆盖错误参数不产生副作用、真实 SDK 接收不完整 JSON、错误 +反馈后纠正调用、同轮有效调用、交互／headless 模式、轮数上限与轨迹证据。 +通过 Herdr 新建 split，独立 reviewer 按 `review-agent` 技能审查完整差异， +结论为 **No findings**，并额外验证了真实 SDK 中混合调用的恢复和 Harbor 兼容性。 + +此外，将上一轮 circuit、QEMU、scheduler、DNA 的四份实际失败响应流原样交给 +SDK 1.5.0 离线重放，四次都把坏调用转为错误反馈，并在脚本提供的后续回复中 +完成一次有效调用、正常结束。该验证的工具执行被替身替代，没有付费模型调用， +证明同一失败输入的处理行为已经改变，不能当作四题真实解答通过。 + +**已知边界。** 本次恢复依赖 SDK 成功交付响应。非法转义和尾随逗号等部分 JSON +语法错误会先在 SDK 内部抛出异常，尚不能进入恢复流程；独立审查已确认主分支也 +存在这一限制。流中断、任务算法错误、轮数耗尽和评测环境故障也需要分别处理。 + +**实题复测设置。** 复测使用同一份已审查的 `27a9503` wheel，模型仍为 OpenRouter +上的 `deepseek/deepseek-v4-flash-0731`；原始 20 题的 task ref 和缓存镜像摘要 +均与 09-06 基线一致。每次回复显式设置 `max_tokens=65536`，每题最多 50 轮， +同时运行最多 2 题；项目默认值仍为 32768。agent 执行限时 3600 秒，收尾宽限 +120 秒，Harbor 外层限时 3780 秒，验证器保留各题原始时限。每题在每个 job 中 +只尝试一次，Harbor 自动重试为 0；额外补测使用独立 job，保留原失败记录。 + +09-13 首组 targeted5 中，QEMU 安装依赖遇到 APT 404，DNA 安装超过 1080 秒， +均未调用模型；PyTorch 在安装期间被用户暂停，circuit 尚未启动。后续仅将 +**安装时限**增加到 3600 秒，并对 HTTP 404 最多重试三次系统依赖安装。 +同组 scheduler 的 agent 已在暂停前正常结束，恢复任务后使用保留容器中的原始 +产物补跑官方验证：6/6 通过、reward 为 1，**没有新增模型调用**。这是暂停后 +补验证的结果，单独记录，不当作一次完整、不中断的 Harbor 运行。 + +09-14 targeted4 的 QEMU 安装仍遇到间歇性 404，因此另建补测：从原 APT 索引 +选中的官方地址下载同版本 20 个包，核对 SHA256 和大小，在 `apt-get update` +之后放入缓存，再执行原依赖安装。提前放入会被镜像的 APT 清理钩子删除；修正 +顺序后，原始镜像中的安装验证成功。包版本和软件源保持一致。 + +同一 targeted4 中，PyTorch 的 agent 正常结束，但官方验证器下载 Torch／CUDA +依赖超过原始 900 秒，未得到评分。为独立补测和后续完整 20 题准备了 uv 0.9.5 +缓存，依赖仍为官方脚本指定的 `pytest==8.4.1`、`torch==2.7.1` 和 +`pytest-json-ctrf==0.3.5`。缓存迁移到原始镜像后，31 个包离线安装成功，Torch +可正常导入；准备阶段没有运行题目测试。后续仅为 `pytorch-model-recovery` +预置这份缓存，官方验证脚本和 900 秒时限不变。这改变了缓存条件，不能与工具 +参数修复的效果混为一谈。 + +原始 8192 基线还使用不同源码和 agent 时限；上一轮 65536 的首批 18 题为 +5 题通过，之后的补测及 compressor 是独立尝试，`regex-log` 则仅在 32768 下 +复测过。因此分别比较原始 20 题和相同 18 题子集，不拼接不同设置生成通过率。 +采样、路由、缓存和服务端状态未受控；相同失败响应的离线重放、实跑中的错误 +恢复事件、最终题目评分，应作为三类不同证据解读。 + +**针对性复测结果（2026-09-14）。** 下表保留每题的历史报错来源和实际复测结果； +安装失败、验证超时及补测不相互覆盖，也不将这些不同条件合成一次正式的 5 题评分。 + +| 题目 | 历史参数崩溃 | 本次结果 | 本次观察到的参数反馈 | +| --- | --- | --- | --- | +| `dna-assembly` | `bash` 缺 `command`，原始 JSON 为未闭合字符串 | reward **0**，官方测试 **0/1**；50 次模型回复后 `max_turns_exhausted`，agent 执行 3533.7 秒 | 0 次 | +| `qemu-alpine-ssh` | `bash` 缺 `command`，原始 JSON 为 `{` | 安装失败单独保留；缓存补测 reward **1**、**1/1** 通过，48 次回复后正常结束 | 0 次 | +| `llm-inference-batching-scheduler` | `write` 缺 `content`，原始 JSON 为 `{` | 暂停前 47 次回复后正常结束；恢复后官方验证 reward **1**、**6/6** 通过，无新增模型调用 | **1 次**不完整 `bash` JSON 被拒绝，随后继续调用模型，并出现成功的 `bash` 调用 | +| `pytorch-model-recovery` | 8192 基线在超时后发生 `edit` 缺 `path` | 首次 agent 正常结束，验证下载超时未评分；缓存补测 reward **1**、**5/5** 通过，8 次回复后正常结束 | 两次均为 0 次 | +| `circuit-fibsqrt` | `bash` 缺 `command`，原始 JSON 为 `{}` | 49 次回复后达到 3600 秒执行时限，停止后产物通过 **3/3** 测试、reward **1**；**不计为正常结束的通过** | 0 次 | + +circuit 的中断由实验 supervisor 发出，最终为 `KeyboardInterrupt`／退出码 130， +Harbor 记录 `NonZeroAgentExitCodeError`。收尾完成且保留原生轨迹，没有强制杀进程； +验证在 agent 停止之后才开始。因此应同时保留“产物通过”和“执行预算耗尽”， +不能仅凭 reward 1 就把该运行记为正常完成。 + +本组实跑中，直接观察到参数错误反馈后继续并通过验证的只有 scheduler，且本次 +错误是 `bash` JSON，不是历史上的 `write.content`。其他题目的新采样没有产生 +参数校验错误；它们的通过不能单独归因于本功能。四份历史坏输入能否恢复，由 +前述原始响应离线重放提供同输入证据。 + +含 scheduler、首次 PyTorch 验证超时和两次缓存补测在内，共保留 **6 份原生 ATIF**， +全部通过 Harbor 校验、与完整 Journal 投影一致，且没有 Journal 字符串截断。 +这 6 次实跑共 222 次模型回复，输入 12,183,309 tokens（含缓存命中 10,021,376）， +输出 427,835 tokens，模型费用 **$0.243005564**，用量与费用均完整；纯安装失败、 +暂停时尚未启动的模型及离线重放没有模型费用。PyTorch 缓存补测的安装、agent、 +验证分别耗时 432.8、140.0、23.2 秒；这反映了缓存准备条件，不能解释为 agent +代码使原先 900 秒的验证下载变快。 + +**完整 20 题复测（2026-09-14～15）。** 独立完整组最终为 **10/20 通过（50%）、 +9 题零分、1 题未评分**；其中 **9 题正常完成并通过（45%）**。原始 8192 基线 +为 8/20 通过（40%），其中 7 次没有 Harbor 异常。本次 circuit 的产物通过了 +验证，但费用对账阶段达到执行上限，不能计为正常结束的通过。“无 Harbor 异常” +与本次要求原生 `completed`、无异常、未触及执行上限的定义也不完全相同。 +[结构化结果](../../../benchmarks/harbor/results/tb21-tool-input-recovery-20260914.json) +保留各次尝试、配置、计费完整性与验证证据。 + +| 实验组 | 题数 | reward 1 | reward 0 | 未评分 | +| --- | --- | --- | --- | --- | +| 原始 8192 基线 | 20 | 8 | 12 | 0 | +| 上一轮 65536 首批 18 题 | 18 | 5 | 8 | 5 | +| 本次相同 18 题子集 | 18 | 8 | 9 | 1 | +| 本次独立完整组 | 20 | 10 | 9 | 1 | + +相同 18 题子集的 8 次通过中，7 次正常结束。下表的数字为 reward；“不在此组” +表示上一轮首批 18 题没有包含该题，并不表示未评分。compressor 上一轮在独立 +65536 补测中已经通过，regex-log 则在 32768 补测中通过，均不能混入旧首批 18 题。 + +| 题目 | 原始 8192 | 上轮 65536 首批 18 | 本次 65536 | 本次结果说明 | +| --- | --- | --- | --- | --- | +| `write-compressor` | 0 | 不在此组 | 1 | 正常结束；3/3 | +| `torch-tensor-parallelism` | 0 | 未评分 | 0 | 正常结束；1/3 | +| `schemelike-metacircular-eval` | 0 | 0 | 0 | 第 18 次请求响应流中断 | +| `kv-store-grpc` | 1 | 0 | 1 | 正常结束；7/7 | +| `pypi-server` | 1 | 1 | 1 | 正常结束；1/1 | +| `dna-assembly` | 0 | 未评分 | 0 | 第 2 次请求服务商不可用 | +| `torch-pipeline-parallelism` | 0 | 未评分 | 0 | 50 轮耗尽；2/3 | +| `qemu-alpine-ssh` | 1 | 未评分 | 1 | 正常结束；1/1 | +| `openssl-selfsigned-cert` | 1 | 1 | 1 | 正常结束；6/6 | +| `regex-chess` | 0 | 0 | 0 | 50 轮耗尽；1/4 | +| `log-summary-date-ranges` | 1 | 1 | 1 | 正常结束；2/2 | +| `model-extraction-relu-logits` | 0 | 未评分 | 未评分 | agent 正常结束；验证 900 秒超时 | +| `path-tracing` | 0 | 0 | 0 | 第 32 次请求服务商不可用 | +| `regex-log` | 0 | 不在此组 | 1 | 正常结束；1/1 | +| `caffe-cifar-10` | 0 | 0 | 0 | 正常结束；2/6 | +| `mteb-leaderboard` | 0 | 0 | 0 | 50 轮耗尽；0/2 | +| `llm-inference-batching-scheduler` | 1 | 0 | 0 | 第 3 次请求响应流中断 | +| `pytorch-model-recovery` | 1 | 1 | 1 | 正常结束；5/5 | +| `circuit-fibsqrt` | 0 | 0 | 1 | 3/3；费用对账中断，轨迹重建 | +| `merge-diff-arc-agi-task` | 1 | 1 | 1 | 正常结束；5/5 | + +与原始基线相比，新增通过 compressor、regex-log、circuit，scheduler 从通过 +变为零分；净增加 2 题。与上一轮 65536 首批 18 题相比，KV-store 从零分变为通过， +QEMU 从未评分变为通过，circuit 从零分变为产物通过但收尾异常。前两题在原始 +8192 基线中本就通过，compressor 和 regex-log 在此前各自的预算补测中也已通过， +因此不能把这些分数变化当作参数校验的独立收益。 + +本次完整组 **没有触发 `ToolInputError`**。同一历史坏输入能否恢复，仍由四份 +原始响应的离线重放证明；实跑中观察到错误反馈后继续并通过的证据，来自前述 +定向 scheduler。本次完整组的 scheduler 在第 3 次请求发生 `RemoteProtocolError`， +只完成 2 次回复、得到零分；不能用定向组的成功覆盖这次独立失败。 + +完整组共有 4 次请求未完整返回：Scheme 和 scheduler 的响应流中断，DNA 和 +path-tracing 则收到 `provider_unavailable`，提示上游收到 SIGTERM、停止服务超时。 +这些属于传输或服务可用性问题。pipeline、regex-chess、MTEB 耗尽 50 轮；tensor +和 Caffe 正常结束但功能测试未全通过。模型提取完成 27 次回复，依赖已安装， +pytest 已收集并开始执行单个测试，但超过原始 900 秒验证时限，因此保留未评分； +这次不能归因于依赖下载，也没有延长时限来替换结果。 + +**circuit 的收尾问题。** 第 50 次回复在 UTC 18:46:26.965 以 `end_turn` 结束， +之后继续查询费用。supervisor 在 18:47:05.171 触发 3600 秒执行上限，打断费用 +对账；此时尚未写入 `run.completed` 或 `run.failed`，原生 ATIF 导出抛出 +`AtifProjectionError: Event Journal has no terminal event`。实验包装器保留原始 +Journal，另加明确标注的合成终止事件后重建轨迹；没有把重建文件冒充原生导出。 +agent 停止后官方测试 3/3 通过。这个费用收尾控制分支与实现提交的父提交相同， +本次没有修改它；后续需要处理“对账中断仍能记录终止状态并导出轨迹”的问题。 +工具输出中另有模型生成程序的 `Gate` 对象 `KeyError`，它作为工具结果返回， +不是 agent 因缺失工具参数而崩溃。 + +circuit 后段的三次详细监控探针各自超过 25 秒。只读检查时，容器仍在运行， +内存约 1.983/2 GiB，Docker 未报告 OOM；随后停用容器内详细探针，改读宿主机 +结果文件和容器状态，没有修改 agent、验证器或资源上限，也没有发现遗留探针。 +先前探针对内存压力的影响无法量化，因此把这次监控变化列为实验限制。 + +**轨迹与费用。** 20 份 ATIF 均通过 Harbor 校验，其中 19 份是原生导出且与完整 +Journal 投影一致，1 份是上述明确标注的重建文件；全部 Journal 均无字符串截断。 +共启动 491 次模型调用，487 次完整返回。已完整记录的回复合计输入 +**21,991,162 tokens**（含缓存命中 **18,119,936**）、输出 **724,013 tokens**； +4 次中断响应的原生用量仍不完整，不能把缺失值当零。逐回复输出最大值也只统计 +完整回复，不能用它代表中断响应的实际输出。QEMU 另有 1 次建连阶段 +`ConnectTimeout`，SDK 重试后正常通过：41 次模型回复、42 次 HTTP 尝试。 + +所有模型费用已由原生记录或独立账单回执补齐。补查区分已完整返回的调用与中断 +请求，并扣除 Journal 中已经记录的费用，避免重建轨迹缺少终止事件时重复计费。 +完整 20 题费用为 **$0.462175819**；连同定向组、缓存补测和恢复验证的 scheduler， +本次实验模型费用合计 **$0.705181383**。费用完整不等于原生响应或用量完整。 + +#### 流中断恢复的完整 20 题复测 + +**2026-09-21～22 实跑。** 在 `fix/stream-recovery` 分支提交 +`577a1ab7f96fc3d220ca89c068988a479ac7b1aa` 上重新运行原始 20 题，检查真实模型 +响应是否中断，以及中断后能否在同一次 run 内恢复。北京时间 09-21 22:06 开始， +09-22 01:52 结束，总耗时约 **3 小时 46 分钟**；没有人为注入传输故障。 + +**配置与记录。** 使用 Harbor 0.21.0、Terminal-Bench 2.1，保留原始 20 题的 +task ref，并在运行前核对缓存镜像摘要。模型仍为 OpenRouter 上的 +`deepseek/deepseek-v4-flash-0731`。容器安装本分支构建的 wheel，逐个核对包内 +Python 源文件与当前提交一致；运行版本为 `0.8.1.dev28+g577a1ab7f`。依赖固定为 +Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0、Pydantic 2.13.5。 + +每次回复上限 65536 tokens，每题最多 50 次完整回复，并发 2；每题只尝试一次， +Harbor 整题自动重试为 0。agent 执行上限 3600 秒、收尾宽限 120 秒、Harbor +外层上限 3780 秒，安装上限 3600 秒，官方 verifier 保留原始时限。沿用此前的 +依赖缓存和被动 HTTP 记录器；容器内仅做轻量事件计数，完整 Journal／HTTP +记录收集到宿主机后分析。实时计数器曾修正为兼容无空格的 JSON 并单独重启， +没有重启评测或修改 agent、题目和预算。 + +| 指标 | 本次结果 | +| --- | --- | +| 已结束的 trial | 20/20 | +| 实际进入 agent 执行 | 18 题；另外 2 题安装失败，未调用模型 | +| 官方评分 | reward 1：**11 题（55%）**；reward 0：**6 题**；未评分：**3 题** | +| 正常完成且通过 | **8 题（40%）**：原生终态为 `completed`，无 Harbor 异常，未触及执行时限 | +| Journal 模型调用 | **576 次启动，576 次完整返回** | +| `model.failed` / HTTP 响应体中断 | **0 / 0** | +| 触发流恢复 / 恢复成功 | **0 / 0**；本轮没有触发恢复分支 | + +逐题结果如下。“轮数耗尽”指原生终态为 `max_turns_exhausted`；它与官方 verifier +是否判定产物通过是两件事。 + +| 题目 | Reward | 执行或验证结果 | +| --- | --- | --- | +| `write-compressor` | 1 | 正常完成并通过 | +| `torch-tensor-parallelism` | 未评分 | agent 正常完成；verifier 超过原始 900 秒时限 | +| `schemelike-metacircular-eval` | 1 | 50 轮耗尽；产物通过 | +| `kv-store-grpc` | 0 | agent 正常完成，产物未通过 | +| `pypi-server` | 1 | 正常完成并通过 | +| `dna-assembly` | 0 | agent 正常完成，产物未通过 | +| `torch-pipeline-parallelism` | 未评分 | 安装阶段使用 Python 3.12.3，不满足项目要求的 Python >=3.13 | +| `qemu-alpine-ssh` | 未评分 | 安装阶段下载 Python 3.14.0，三次重试后仍收到 HTTP 504 | +| `openssl-selfsigned-cert` | 1 | 正常完成并通过 | +| `regex-chess` | 1 | 50 轮耗尽；产物通过 | +| `log-summary-date-ranges` | 0 | agent 正常完成，产物未通过 | +| `model-extraction-relu-logits` | 1 | 正常完成并通过 | +| `path-tracing` | 1 | 正常完成并通过 | +| `regex-log` | 1 | 正常完成并通过 | +| `caffe-cifar-10` | 0 | 50 轮耗尽，产物未通过 | +| `mteb-leaderboard` | 0 | 50 轮耗尽，产物未通过 | +| `llm-inference-batching-scheduler` | 1 | 50 轮耗尽；产物通过 | +| `pytorch-model-recovery` | 1 | 正常完成并通过 | +| `circuit-fibsqrt` | 0 | 50 轮耗尽，产物未通过 | +| `merge-diff-arc-agi-task` | 1 | 正常完成并通过 | + +两次安装失败均被 Harbor 记为 `NonZeroAgentExitCodeError`，发生在 agent 启动前； +tensor 的异常为 `VerifierTimeoutError`。这些不是模型流中断，不能把 20 个 trial +结束写成“20 题都完成了模型执行”。同样，11 次 reward 1 中的 Scheme、regex-chess、 +scheduler 是轮数耗尽后产物通过，不计入 8 次正常完成且通过。 + +**恢复能力的证据边界。** 本轮 576 次模型调用均完整返回，没有观察到真实流中断， +因此只能记录“此次未出现流中断”，不能据此声称“实跑中断后恢复成功”。本轮启动时 +另行运行 `tests/test_stream_recovery.py`，结果为 **17 passed，0 failed，0 skipped**， +覆盖两种 HTTP 异常族、重试耗尽和时间窗口、丢弃中断回复中的工具调用，以及保留 +已有工具结果等行为。这些自动化测试提供恢复机制的验证，但不能替代真实中断样本。 +采样、路由、缓存和服务端状态未受控；与此前 20 题的分数变化也不能归因于流恢复。 + +本地 job 为 `jobs/tb21-stream-recovery-pilot20-20260921/`，实验记录目录为 +`jobs/tb21-stream-recovery-pilot20-20260921-record/`。其中 `manifest.json` 保存源码、 +wheel、任务与镜像信息，`summary.json` 保存逐题统计，`report.md` 为结果摘要， +`stream-recovery-tests.json` 保存专项测试结果。原始 Journal、HTTP 记录、trajectory +和 verifier 输出保留在各 trial 目录；这些原始数据位于 Git 忽略的 `jobs/` 中。 + +#### 超时停止 + +“超时停止”指的是：任务达到规定的运行时间后，要确保容器里的 agent 真正停止 +工作，再开始验证结果。本轮 Harbor 已经判定任务超时，但 agent 仍在继续调用模型。 + +具体发生在 `pytorch-model-recovery`，该题的 agent 运行时限为 900 秒，即 15 分钟。 +以 agent 开始执行为起点，实际时间线如下： + +| 时间 | 实际发生的事情 | +| --- | --- | +| 约第 900 秒 | Harbor 判定超时，记录 `AgentTimeoutError` | +| 约第 901 秒 | verifier 开始验证任务结果 | +| 约第 929、939 秒 | agent 又启动了两次模型调用 | +| 约第 1063 秒 | agent 因 `edit` 缺少 `path` 异常而失败，记录 `run.failed` | + +Harbor 的“已经超时”没有及时转化为容器内 agent 的“停止执行”，带来两个后果： + +- 验证期间 agent 仍在运行，可能继续修改 verifier 正在检查的文件，影响结果的 + 可靠性。因此这题虽然得到 `reward=1`，也不能当作正常完成的成功样本。 +- 超过时限后仍产生新的模型调用和费用，任务的时间边界没有得到落实。 + +拟议修复目标是： + +1. 到达任务时限后，停止 agent 的执行，阻止它继续发起模型和工具调用。 +2. 清理需要终止的工具子进程，避免 agent 退出后仍有命令继续修改文件。 +3. 确认执行已停止，再启动 verifier，并准确记录超时状态。 + +目前确认的是超时后 agent 仍在运行；具体是哪一层的取消或进程清理没有生效， +还需要进一步定位。这项修复的重点是让时间限制真正生效，单纯延长 900 秒无法 +解决超时后继续执行的问题。 + +#### trajectory 采集 + +“trajectory 采集”指的是：确保 agent 的结构化执行记录能被保存，并被 Harbor +及时读取，用来汇总执行步骤、token 和费用。本轮暴露的问题是文件后来有了， +但 Harbor 汇总时没有拿到。 + +正常情况下，agent 运行时持续记录模型回复、工具调用、执行结果和用量，结束时 +将这些记录导出为 ATIF 格式的 `trajectory.json`。Harbor 随后读取并校验轨迹， +据此汇总步骤、token 和费用。这意味着“agent 已停止”“轨迹已保存”“评测已采集” +是需要按顺序完成的三个环节。 + +本轮 `pytorch-model-recovery` 的采集过程出现了时序问题： + +1. Harbor 判定超时后开始采集，此时没有拿到 trajectory，记录为 `missing`。 +2. agent 仍在运行，后来收尾才写出有效的 ATIF 文件。 +3. 文件最终出现在结果目录里，但 Harbor 原始结果中的 token 仍为 `null`，没有随 + 文件出现而自动补齐。 + +因此，最终 20/20 份 ATIF 都通过校验，但只有 19/20 题的 Harbor 原始指标与最终 +ATIF 一致。文件最终存在、格式有效，并不表示评测汇总时已经成功采集到它。 + +拟议修复目标是：正常完成、发生异常或超时时，都应尽可能保留截至停止时的执行 +记录，并让 Harbor 在汇总前读到它；记录不完整时，也要明确标注。 + +这需要与“超时停止”一起设计：先让执行停止并在有限时间内完成收尾，再采集和 +汇总。当前轨迹导出依赖 agent 完成收尾；如果直接强制杀死进程，轨迹可能来不及 +保存。因此还需考虑定期保存快照，或从已落盘的 Journal 恢复轨迹等 +方案。具体方案尚未确定，修复目标是让实际执行记录、保存的 trajectory 和 Harbor +汇总指标能够对应起来。 + +#### 完善延迟费用补查 + +“完善延迟费用补查”指的是：任务结束时还没查到的模型调用费用，要能在稍后继续 +查询，并补齐费用统计。 + +模型回复已经返回时，响应里可能没有实际费用，账单查询接口也暂时查不到金额。 +此时 agent 保存该次调用的 generation ID，作为后续关联账单的标识，再用它查询 +这次调用的 `total_cost`。暂时查不到只能标记为待查或未知，不能当成没有费用。 + +当前 agent 已经会在运行收尾时，自动对缺少费用且有 generation ID 的调用逐笔 +补查。每笔默认最多尝试 6 次，重试间隔为 `1、2、4、8、15` 秒。这些间隔 +累计为 30 秒，还要加上查询请求本身的耗时，因此 30 秒并非整个补查过程的耗时 +上限。超过尝试次数仍未查到，就保留费用不完整的状态。上述收尾补查已经是 agent +的正式能力；当前尚未提供正式的独立后续补查入口。 + +本轮 13 道题的原始 trajectory 费用不完整，共有 18 次调用未在收尾补查中取得 +费用。在 2026-09-06 本轮批量运行期间及结束后，通过独立脚本分批查询同一个费用 +接口，最终补齐了这 18 笔费用。 + +**这次独立补查使用的是临时脚本，尚未纳入正式流程。** 脚本为本地 +`jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`，保存在被 Git +忽略的 `jobs/` 目录中，作为本次试跑的一次性工作流记录归档。它需要单独运行， +没有接入正式 CLI、Harbor adapter 或固定的评测后处理步骤。因此，这次通过临时 +脚本查全费用，并不表示 agent 已具备自动完成后续补查的能力。 + +补查前后的费用为： + +| 统计口径 | 费用（USD） | +| --- | ---: | +| 最终 trajectory 中的已知费用小计 | 0.11053603 | +| 独立补查后的完整费用 | 0.119200332 | + +补查后的金额覆盖全部 267 次已记录模型回复。这说明收尾阶段的有限尝试仍会留下 +需要稍后补查的费用，不能把当时已知的小计视为完整总额。 + +拟议方向是把本次临时完成的后续补查做成可重复使用、能接入评测流程的正式能力， +并支持任务结束后独立运行：读取尚未取得费用的 +generation ID，稍后重新查询，保存补查回执，再生成更新后的费用汇总。该流程 +需要保留原始结果，避免同一笔费用被重复计入，并明确区分已知费用小计和完整 +总费用。仍查不到的金额保持未知，不能填成零；补查失败也不应改变题目的评分。 + +具体采用独立命令还是批量评测后的自动步骤，尚未确定。重点是让补查可以跨越任务 +收尾阶段继续进行，避免为了等待账单而长时间拖住 agent 退出。 + +### 更换模型复跑未通过的 6 题 + +**2026-09-25 实跑。** 上一轮流中断恢复复测在 `deepseek/deepseek-v4-flash-0731` +下，20 题里有 6 题 reward 为 0：`kv-store-grpc`、`dna-assembly`、 +`log-summary-date-ranges`、`caffe-cifar-10`、`mteb-leaderboard`、`circuit-fibsqrt`。 +为了判断这些失败是否受模型限制，本轮用同一批题、同一套运行条件，只更换模型把它们 +重跑一遍。这不是受控实验：每题只跑 1 次，路由、采样、缓存和服务端后端都没有固定。 + +**模型选择。** 目标是换一个 DeepSeek Flash 模型。OpenRouter 上并没有 +`deepseek-flash` 这个 slug；`deepseek/deepseek-v4-flash-latest` 等别名会被 +Anthropic Messages 接口判为无效模型。最终使用 **`deepseek/deepseek-v4.1-flash`** +（V4.1 Flash），并先发一次最小 Messages 请求确认它可用且支持工具调用。此前还曾用 +`deepseek/deepseek-v4-flash`（V4 Flash 0423）启动过一轮，但在完成前被中止，产物 +保留在 `jobs/tb21-v4flash-6task-20260925/`，不作为实验结果；本文只记录 V4.1 Flash +这轮。 + +**固定条件。** + +| 项目 | 本轮配置 | +| --- | --- | +| Job | `tb21-v41flash-6task-20260925` | +| Agent 源码 | `main` 提交 `1386df48ef9c84d7b65c33fcf0f903fad7036a79`；wheel `0.8.1.dev30+g1386df48e` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| Dataset | `terminal-bench/terminal-bench-2-1`，`sha256:7d7bdc1c…a0699a` | +| 题目 | 上述 6 题，沿用各自固定 task ref | +| 生成／轮数 | 每次回复最多 65536 tokens；每题最多 50 次完整回复 | +| 时限 | agent 3600 秒、收尾宽限 120 秒、Harbor 外层 3780 秒、安装 3600 秒；verifier 保留题目原始时限 | +| 并发／重试 | 并发 2；每题 1 次尝试，Harbor 自动重试 0 | +| 依赖 | Anthropic SDK 1.5.0、HTTPX 0.28.1、HTTPX2 2.12.0、Pydantic 2.13.5 | +| 镜像 | 沿用基线缓存镜像，运行前核对 RepoDigests | + +**结果。** 6 题全部结束、全部得到官方评分，**通过 4 题、未通过 2 题**。 + +| 指标 | 本轮结果 | +| --- | --- | +| 通过／未通过／未评分 | **4 ／ 2 ／ 0** | +| Journal 模型调用 | **206 次启动，206 次完整返回** | +| `model.failed` / HTTP 响应体中断 | **0 / 0** | +| input／cache／output tokens | 8,122,046 ／ 7,444,992 ／ 219,443 | +| 模型费用 | **$0.278759041**（逐次回执完整） | +| 基础设施异常 | 0 | + +| 题目 | Reward | 终态 | 模型回复 | 费用（USD） | +| --- | ---: | --- | ---: | ---: | +| `kv-store-grpc` | 1 | `completed` | 17 | 0.008440065 | +| `dna-assembly` | 1 | `completed` | 34 | 0.069021966 | +| `log-summary-date-ranges` | 1 | `completed` | 6 | 0.005224672 | +| `circuit-fibsqrt` | 1 | `completed` | 49 | 0.080010864 | +| `caffe-cifar-10` | 0 | `max_turns_exhausted` | 50 | 0.065742942 | +| `mteb-leaderboard` | 0 | `max_turns_exhausted` | 50 | 0.050318532 | + +**两题仍未通过，原因都是 50 轮耗尽，不是崩溃或传输故障。** + +- `caffe-cifar-10`：CPU 训练太慢，50 轮内只走到 `Iteration 200`， + `cifar10_quick_iter_500.caffemodel` 与 `Iteration 500` 都不存在；官方 6 项测试 + 通过 2 项、失败 3 项（另有 1 项通过）。瓶颈是轮数上限加训练耗时。 +- `mteb-leaderboard`：始终没有生成 `/app/result.txt`，官方 2 项测试全部失败； + 模型一直在阅读 MTEB 源码，50 轮耗尽仍未写出结果文件。这题在每一轮历史实验里 + 都是同一种死法。 + +**与历史的对比。** 这 6 题在 2026-09-21 的 0731 运行中全部 reward 0；换到 +V4.1 Flash 后： + +- `kv-store-grpc`、`log-summary-date-ranges` 恢复通过（它们在更早的 8192 与 + 09-14~15 运行中也曾通过，属于回归恢复）。 +- `dna-assembly` **首次通过**，且是干净的 `completed`；此前所有尝试都是 0 或未评分。 +- `circuit-fibsqrt` **首次干净通过**；此前两次 reward 1 都伴随执行上限或轨迹重建异常。 +- `caffe-cifar-10`、`mteb-leaderboard` 仍未通过，且历史上从未通过。 + +**证据边界。** 换模型的同时没有控制路由、采样、缓存和后端，每题也只跑了 1 次， +因此 **0/6 → 4/6 不能直接归因于模型差异**，只能作为一个待验证的信号。 + +**产物位置。** 原始 trial 在 `jobs/tb21-v41flash-6task-20260925/`，实验记录目录为 +`jobs/tb21-v41flash-6task-20260925-record/`（`manifest.json`、`summary.json`、 +`report.md`）；这些位于 Git 忽略的 `jobs/`，未上传。版本管理保存结构化结果 +[`tb21-v41flash-6task-20260925.json`](../../../benchmarks/harbor/results/tb21-v41flash-6task-20260925.json) +与本节汇总。 + +### 实验一：把 max_turns 提到 100 + +**2026-09-26 实跑。** 上一轮（`tb21-v41flash-6task-20260925`）里 +`caffe-cifar-10` 与 `mteb-leaderboard` 都是 50 轮耗尽（`max_turns_exhausted`）。 +Terminal-Bench / Harbor 对 agent 的硬约束是**墙钟时间**（本题原生 +`[agent].timeout_sec = 3600`），并不包含轮数上限；50 轮是 nanoPyCodeAgent 自己的 +成本护栏（`adapter.py:18`、`agent.py:82`）。因此本轮只改一件事：**把 `max_turns` +从 50 提到 100，其余设置保持不变**。为让这次运行合规，**移除了 Harbor 层的 +`override_timeout_sec` 与 `override_setup_timeout_sec`**，改用题目原生时限，agent +在题目 3600 秒内自行限时 3480 秒并预留 120 秒收尾。 + +| 项目 | 本轮配置 | +| --- | --- | +| Job | `tb21-exp1-turn100-20260926` | +| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `41476d451`；wheel `0.8.1.dev32+g41476d451` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| Dataset | `terminal-bench/terminal-bench-2-1`，`sha256:7d7bdc1c…a0699a` | +| 题目 | `caffe-cifar-10`、`mteb-leaderboard`，沿用固定 task ref | +| 生成／轮数 | 每次回复最多 65536 tokens；每题最多 **100** 次完整回复 | +| 时限 | 题目原生 agent 3600 秒；agent 自限 3480 秒 + 120 秒收尾；**未覆盖任何 Harbor timeout** | +| 并发／重试 | 并发 2；每题 1 次尝试；Harbor 自动重试 0 | + +**结果：2 题全部通过，但两题的死法不同。** + +| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | +| --- | ---: | --- | ---: | ---: | ---: | +| `mteb-leaderboard` | 1 | `completed` | 70 | 1666 s | 0.054191917 | +| `caffe-cifar-10` | 1 | `DiagnosticDeadlineExceeded` | 69 | 3600 s | 0.039895559 | + +- `mteb-leaderboard`：**历史上第一次通过**，而且是干净的 `completed`——第 70 轮 + 写完 `/app/result.txt` 并主动结束。这说明上一轮的失败**不是能力问题，而是 50 轮 + 这个自设上限卡住了它**：它在第 39 轮就已经算出正确答案，50 轮时还在复核，100 轮 + 时终于收敛。 +- `caffe-cifar-10`：官方 6 项测试全通过，但 agent 没有自己收尾——它在 + `Iteration 500` 训练完成、并用训练好的模型跑完测试之后，又去构建 pycaffe + (`make py`)、装 `python3-skimage` 等额外工作，直到撞上 3480 秒自限与 3600 秒 + 硬上限被强杀（`deadline_reached=true`、`forced_kill=true`）。**奖励来自超时前 + 已落盘的产物**，不是 agent 主动结束；轨迹因此只能由 supervisor 从 journal 重建 + （`trajectory_source=reconstructed`）。 + +**结论。** 这两题印证了同一判断的两面： + +1. 把轮数上限提到 100 确实让 `mteb-leaderboard` 通过——**自设的轮数上限曾经是 + 绑定约束**。 +2. 但 `caffe-cifar-10` 表明，**光放宽轮数并不能让 agent 学会收尾**：它仍会一路 + 做到被外部强杀。这正是"时间预算 + 预算注入 + 到点停止"（实验二）要解决的问题。 + +**证据边界。** 每题只跑 1 次，路由、采样、缓存、后端都未固定；这只是一个针对性 +信号，不是通过率的受控估计。 + +**产物位置。** 原始 trial 在 `jobs/tb21-exp1-turn100-20260926/`，实验记录在 +`jobs/tb21-exp1-turn100-20260926-record/`；这些位于 Git 忽略的 `jobs/`，未上传。 +版本管理保存结构化结果 +[`tb21-exp1-turn100-20260926.json`](../../../benchmarks/harbor/results/tb21-exp1-turn100-20260926.json) +与本节汇总。 + +### 实验二：时间预算、预算注入与到点停止 + +**2026-09-26 实跑。** 实验一证明"放宽轮数"能让两题通过，但 `caffe-cifar-10` +仍是被外部强杀（`forced_kill=true`、轨迹靠重建）。本轮在实验一的基础上加三样东西， +其余不变： + +- **时间预算**：新增 `--time-budget-seconds`（Harbor 侧 kwarg + `time_budget_seconds`）。本轮取 3420 秒，落在题目原生 3600 秒之内、supervisor + 3480 秒自限之前。 +- **预算注入**：每轮把剩余时间写进 system prompt；进入收尾窗口 + （`max(15% 预算, 180 秒)`）后升级为"立即停止调研、写出产物、然后收尾"的强提示。 +- **到点停止**：预算耗尽时返回新终态 `time_budget_exhausted`，在下一次调用或执行 + 工具之前停下，并写进 Journal 与 ATIF。 + +| 项目 | 本轮配置 | +| --- | --- | +| Job | `tb21-exp2-timebudget-20260926` | +| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `eb77fd578`；wheel `0.8.1.dev34+geb77fd578` | +| Model | `openrouter/deepseek/deepseek-v4.1-flash` | +| 时限 | 题目原生 agent 3600 秒；agent 预算 3420 秒；supervisor 自限 3480 秒 + 120 秒收尾；**未覆盖任何 Harbor timeout** | +| 其余 | 每题最多 100 次回复、每次最多 65536 tokens、并发 2、每题 1 次、重试 0，与实验一一致 | + +**结果：2 题全部通过，而且这次两题都自己干净收尾。** + +| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | +| --- | ---: | --- | ---: | ---: | ---: | +| `mteb-leaderboard` | 1 | `completed` | 50 | 865 s | 0.481624230 | +| `caffe-cifar-10` | 1 | `completed` | 61 | 2657 s | 0.374407898 | + +- `mteb-leaderboard`：50 轮、865 秒完成，比实验一的 70 轮、1666 秒**快了一倍**， + 最终消息明确写出答案 `GritLM/GritLM-7B` 并说明已写入 `/app/result.txt`。 +- `caffe-cifar-10`：61 轮、2657 秒**主动收尾**——supervisor 显示 + `deadline_reached=false`、`forced_kill=false`、`trajectory_source=native`， + 与实验一"训练完成后继续构建 pycaffe、最终被强杀、轨迹靠重建"形成对照。 + 最终消息是"All requirements are met"，训练恰好 500 步、测试精度 55.17%。 + +**两轮实验并列对比（同为 2 题、各 1 次）：** + +| 指标 | 实验一（max_turns=100） | 实验二（+ 3420s 预算） | +| --- | ---: | ---: | +| 通过 | 2 / 2 | 2 / 2 | +| 干净 `completed` | 1 / 2 | **2 / 2** | +| 触发截止／强杀 | 1（caffe） | **0** | +| 轨迹需重建 | 1（caffe） | **0** | +| 总模型调用 | 139 | 111 | +| input tokens | 340,804 | 2,919,588 | +| cache tokens | 4,289,280 | 1,408 | +| 总费用（USD） | 0.094 | **0.856** | + +**发现一：时间预算让 agent 自己收尾。** 两题都在预算耗尽之前主动结束， +`deadline_reached` 与 `forced_kill` 全为 false，轨迹全部是原生的。这正好补上了 +实验一暴露的"能做完但不会停"。 + +**发现二：逐轮改写 system prompt 破坏前缀缓存；总费用上升但被路由混淆。** +表面上，实验二的 input tokens 是实验一的约 8.6 倍（2.92M 对 0.34M），cache +tokens 从 4.29M 掉到 1.4K，总费用从 $0.094 涨到 $0.856。按 provider 拆开后有两个 +原因： + +1. **system prompt 每轮都变，前缀缓存失效。** DeepInfra 是两次运行里唯一稳定上报 + 缓存的 provider。实验一 121 次 DeepInfra 调用平均每次命中 29,116 个缓存 tokens； + 实验二 23 次 DeepInfra 调用平均只命中 61 个。同一 provider 上前缀缓存几乎归零， + 与"每轮改写 system prompt 使最长公共前缀失效"一致。 +2. **路由发生偏移（未受控）。** 实验一 139 次调用里 121 次落在 DeepInfra；实验二 + 111 次里 Together 48、Parasail 29、DeepInfra 23、Relace 10。Together／Parasail／ + Relace 在两次运行里都不上报缓存读取。因此总费用的 8.6 倍**不能单独归因于注入**， + 路由变化是叠加的混淆因素。 + +无论哪种原因，修复方向一致：**保持 system prompt 固定，把预算提示作为对话尾部 +的一条 user/tool 消息追加**（尾部变化不影响前缀缓存），或只在跨阈值时注入。本次先 +如实记录，修复方案留待下一轮。 + +**证据边界。** 每题仍只跑 1 次，路由、采样、缓存与后端都未固定；"更快、更干净" +是针对性信号，不是受控估计，也不能排除单次运行波动。唯一能直接归因的是机制本身： +`deadline_reached=false`、`forced_kill=false`、`trajectory_source=native` 是 +"agent 自己停"的直接证据。 + +**产物位置。** 原始 trial 在 `jobs/tb21-exp2-timebudget-20260926/`，实验记录在 +`jobs/tb21-exp2-timebudget-20260926-record/`；位于 Git 忽略的 `jobs/`，未上传。 +版本管理保存结构化结果 +[`tb21-exp2-timebudget-20260926.json`](../../../benchmarks/harbor/results/tb21-exp2-timebudget-20260926.json) +与本节汇总。 + +### 实验三：把预算提示移到对话尾部（修复缓存） + +**2026-09-27 实跑。** 实验二发现逐轮改写 system prompt 会让 provider 的前缀缓存 +失效。本轮唯一的改动是：**system prompt 保持固定，把预算提示追加到对话尾部** +（第 1 轮跟在任务文本后，之后跟在 tool 结果后），其余与实验二完全一致（max_turns +100、预算 3420 秒、无任何 Harbor timeout 覆盖）。 + +| 项目 | 本轮配置 | +| --- | --- | +| Job | `tb21-exp3-timebudget-tail-20260926` | +| Agent 源码 | 分支 `experiment/max-turns-and-time-budget`，提交 `83e5c064d`；wheel `0.8.1.dev37+g83e5c064d` | +| 其余 | 与实验二相同：预算 3420 秒、每题最多 100 次回复、无 timeout 覆盖 | + +**结果：2 题全部通过。** + +| 题目 | Reward | 终态 | 模型回复 | 时长 | 费用（USD） | +| --- | ---: | --- | ---: | ---: | ---: | +| `caffe-cifar-10` | 1 | `completed` | 59 | 3333 s | 0.048236604 | +| `mteb-leaderboard` | 1 | `max_turns_exhausted` | 100 | 1365 s | 0.138716976 | + +**缓存修复已验证。** 两轮对比（同为 2 题）： + +| 指标 | 实验二（system 注入） | 实验三（尾部注入） | +| --- | ---: | ---: | +| input tokens | 2,919,588 | **108,646** | +| cache tokens | 1,408 | **8,410,880** | +| 总费用（USD） | 0.856 | **0.187** | + +更直接的证据来自同一 provider 的前后对比：实验二里 Parasail 的 29 次调用命中 0 个 +缓存读取；实验三里 Parasail 承担了 159 次中的 157 次，平均每次命中约 **53,572** 个 +缓存 tokens。同一 provider 上从 0 恢复到 5 万多，与"system 固定 + 前缀缓存生效" +一致。 + +**但收敛行为变弱了。** 实验二的 `mteb-leaderboard` 在 50 轮主动 `completed`； +实验三它写对了 `/app/result.txt`（reward 仍为 1），却一路做到第 100 轮被 +`max_turns_exhausted` 截断，全程没有输出任何收尾文本。两种可能：尾部提示嵌在 +tool 结果里，显著性不如 system prompt；也可能只是单次运行波动。无论哪种，**这一 +轮仍只跑 1 次，不能区分二者**。 + +**代码 review 与修复。** 借独立的 codex `/review`（按 main 分支对比）复查了本分支 +的实现，它提出 3 个问题并已修复（见下节）： + +- **[P1] 只在批次前检查一次截止时间**：一次回复里有多个工具调用时，第一个工具 + 耗尽预算后其余仍会执行。改为**执行每个工具前重新检查**。 +- **[P2] 注入的提示没有进入轨迹**：预算提示是改变行为的外部输入，却既不在 Event + Journal 也不在 ATIF 里。新增 `input.injected` 事件和对应的 ATIF step。 +- **[P2] 新终态没有版本化**：`time_budget_exhausted` 让新写入仍声称 schema v3， + 而旧 v3 reader 会拒绝它，更新后的 reader 又会错误接受历史 v1/v2 记录。按 + `response_truncated` 的先例**把 Schema 升到 v4**。 + +**证据边界。** 每题仍只跑 1 次，路由未受控（实验三 157/159 次落在 Parasail）。 +缓存差异的方向明确，但"收敛变弱"只能算信号。 + +**产物位置。** 原始 trial 在 `jobs/tb21-exp3-timebudget-tail-20260926/`，实验记录在 +`jobs/tb21-exp3-timebudget-tail-20260926-record/`。版本管理保存结构化结果 +[`tb21-exp3-timebudget-tail-20260926.json`](../../../benchmarks/harbor/results/tb21-exp3-timebudget-tail-20260926.json) +与本节汇总。