[Harrilee]

Summary

Enable registered speculative-decoding schemes in offline prediction and AgentX replay using main's unified speculative cost API. engine.speculation now carries the canonical scheme parameters and draft identity through resolve_speculation_config and ForwardPassPerfModel::best_available, with explicit acceptance and seed inputs kept in the replay layer.

engine:
  speculation:
    kind: mtp
    params: {depth: 3}
    expected_accepted_tokens: 1.5
    seed: 42
  workers:
    aggregated:
      kv_cache:
        capacity: {type: fixed, blocks: 4096}

The capacity is illustrative: callers must reserve memory for draft weights and draft KV before supplying the available target KV blocks.

• Reuse main's cost schema, model/backend restrictions, draft resolution, estimator selection and fallback policy. Explicit FPM with fallback denied still rejects unsupported speculative costs; allowed fallback can select OP while retaining the scheme.
• Use the existing linear NextN executor for 1–5 draft tokens, including supported EAGLE chains. Reject trees, inconsistent verification widths and unsupported runner compositions. Generic replay requires default AIC timing and fixed KV capacity; acceptance is explicit and does not enter cost identity.
• Preserve legacy NextN and flat Ngram inputs. Generic schemes are prediction-only, with offline vLLM/SGLang aggregated or P/D workers subject to existing scheme/model gates. Runner support is an explicit capability, defaulting to false for optional stacks.
• Keep flat and canonical replay inputs consistent: allow canonical estimator fallback for Ngram, infer omitted rank depth from canonical timing, and enforce the same backend limits in Python and native replay.
• Document the public configuration and runner contract, and cover malformed input, disabled speculation, draft-loading errors, fallback, acceptance and real native replay.

Reconciled with main at 9572f2e68. The cost schema, canonical cost model, model-family gates, scheduler algorithms, cost formulas, profile data and parity goldens have no changes relative to that base. This PR provides functional replay support; it does not establish prediction accuracy or measured hardware speedup.

Validation

Local checks on ee42d39dd12bcefd4d55cc72cccba14956cc858b:

• Public runner, Ngram/AgentX CLI and sweeper engine-request suites: 624 passed. New cases exercise FPM allow/deny fallback, inferred/explicit depth equivalence, conflicting depth, and Python/native backend rejection with preserved legacy NextN support.
• Canonical speculative cost API/resolution tests: 88 passed.
• Native Rust Python/replay tests: 52 passed.
• Ruff lint/format, Rust formatting, whitespace and documentation links passed.
• Independent Codex review found no remaining actionable issues on this commit.

Hosted CI results are reported by the checks below. Earlier dashboard accuracy measurements are not requalification evidence for this revision.

Paired adapter: Dynamo #15627. Updating its source pins and requalifying that adapter remains separate downstream work after AISimulate is merged.

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• GitHub Check: Prediction Regression / Collect prediction snapshot (new)
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Linked repositories findings

ai-dynamo/dynamo

Inspected the branch for open PR #15627 at 96e5b9c8 (not the default branch).

• components/src/dynamo/replay/config.py:143–189 is a concrete consumer of generic speculation: it validates the selection, preserves its cost config in AIS timing, and lowers accepted-token depth, verification width, acceptance rates, and seed to scheduler controls. It rejects mixing these controls with legacy fields. The native boundary maps those fields into EngineConfig in lib/mocker/src/engine_adapter.rs:123–126; lib/mocker/src/common/protocols.rs:1328–1348 checks them against resolved scheme geometry.
• components/src/dynamo/replay/tests/test_simulation.py:829–862 covers MTP, ngram, Eagle3, DFlash, DSpark, and draft-model selections. Eagle3 explicitly tests accepted depth 3 alongside verify width 16, confirming those are separate values at this boundary.
• The branch pins aisimulate-core to 8e69498db4e15b1b8bc3cef05d350ab9641d696d and the Python package to aisimulate==0.13.0 (Cargo.toml:61–62, pyproject.toml:15–18). This is branch-specific integration evidence, not a claim about the default branch.
• A separate router simulation conversion remains scalar-shaped: components/src/dynamo/router/simulation/provider.py:178–184 builds params.num_speculative_tokens from engine.speculation["num_speculative_tokens"]. Keep this legacy conversion distinct from the generic AgentX replay path when assessing which inputs are supported through each adapter.

ai-dynamo/aiconfigurator

Inspected at f254959e as the frozen migration/compatibility reference; it is not the runtime dependency.

• Its Rust SpeculativeConfig contains only optional nextn, documented as MTP depth (aic-core/rust/aiconfigurator-core/src/config.rs:214–224). Its Python input contract requires an explicit nextn_accepted assumption for active MTP (src/aiconfigurator/sdk/speculative.py:28–50).
• The legacy MTP compute scaling is specifically (nextn + num_layers) / num_layers, with accepted-token progress applied above the engine (aic-core/src/aiconfigurator_core/sdk/models/helpers.py:1033–1042). This is useful compatibility context, but does not establish generic scheme cost formulas in that repository.

🔇 Additional comments (1)

crates/core/src/python.rs (1)

193-193: 🎯 Functional Correctness

The available source shows validate_public_agentic_engine runs before run_with_input, and apply_resolved_speculation can populate aic_nextn. It does not show whether the resolved value reaches the host-offload configuration or whether a later guard rejects it. The finding cannot be decided from this evidence.

📝 Summary

Risk: High. Three areas need human attention:

1. Scheme resolution and configuration contracts: Rust replay configuration now carries generic scheme identity and parameters through the Python SDK registry. Review configuration round-tripping, draft-config resolution, and explicit rejection of unsupported scheme/backend combinations.
2. Verification width and KV capacity: The change separates accepted draft depth from verification width and changes vLLM and SGLang reservation calculations. Review tree-scheme geometry, explicit KV-capacity requirements, and compatibility with legacy nextn and aic_nextn.
3. Merge and qualification order: The author says AISimulate must merge before Dynamo replaces its temporary source pins. Requalification is required after pin replacement.

Changed behavior and public contracts

• ForwardPassSpeculationConfig changes from an N-gram-only enum to a public struct with kind, JSON params, and optional draft identity fields. ResolvedSpeculationConfig exposes scheme kind, verification width, and maximum accepted draft tokens.
• Engine replay accepts supported schemes through the SDK registry and canonical best_available. Existing NextN and N-gram inputs remain, subject to restrictions on combining legacy depth inputs with explicit speculation.
• Replay applies resolved acceptance assumptions, seed, and verification width. Generic schemes require explicit KV capacity. Existing family, backend, timing, adapter, and offload constraints still apply.
• vLLM and SGLang reservation calculations now account for verification width and remaining output tokens.
• The author reports no new cost formula, automatic draft-memory estimate, or search/recommendation feature. FPM estimator selection, formulas, and data remain unchanged. MTP cost and attention approximations remain uncalibrated.

Evidence supplied

• The author reports 1,198 Python checks passed for final source 8e69498d. The native suite reportedly passed 2,048 checks at 3fa82b95, with one existing fixture ignored. The author says the final commit removed an unused Python inspection binding and incidental compatibility code, and that the native wheel was rebuilt.
• The author reports all 8 installed Engine CLI scenarios passed. Dashboard replay reportedly covered 15 cohorts with speculation on and off, with zero changed prediction records after shrinking and 18,818 valid requests paired. Two original measurement failures remain excluded.
• Reported run-mean E2E bias is +32.16%, and MAPE is 54.41%. Six MiniMax EAGLE3 probes reportedly hit the existing family rejection; the corresponding Dashboard cohorts are labeled MTP surrogates. Qualification is functional_only. DSV4 acceptance remains a user hypothesis, and P/D retains scalar KV transfer.
• The paired adapter is Dynamo #15627. The author reports that its temporary source pins must be replaced and requalified after AISimulate merges.

Evidence still missing

• Test and replay results are author-reported; no independent run results are supplied. Results from different revisions and reports are not evidence of one final test run.
• No current review findings or severity counts are supplied. Review severity counts are unavailable. Test results do not establish review approval or merge readiness.
• The supplied evidence does not show post-merge Dynamo pin replacement or requalification.

This change adds configurable speculation schemes and draft-model resolution. It carries resolved scheme geometry into engine models and replay configuration. vLLM and SGLang use explicit verification width and accepted-draft limits when sizing decode reservations.

Changes: Speculation configuration and execution

Layer / File(s)	Summary
Scheme and draft configuration crates/core/src/perfmodel/fpm/config.rs, python/aisimulate/src/aisimulate/config/engine.py, python/aisimulate/src/aisimulate_core/sdk/speculation/*, python/aisimulate/src/aisimulate_core/sdk/models/__init__.py, python/aisimulate/src/aisimulate/compiler.py, python/aisimulate/src/aisimulate/main.py, docs/core-api.md, python/aisimulate/tests/unit/sdk/speculation/*	Speculation configuration supports scheme identity, parameters, and optional draft-model data. Resolution and validation cover supported kinds, acceptance assumptions, backend constraints, and draft geometry.
Resolved metadata through model compilation crates/core/src/perfmodel/config.rs, crates/core/src/perfmodel/engine/runtime.rs, crates/core/src/perfmodel/fpm/model.rs, crates/core/src/perfmodel/{mod.rs,engine/spec.rs}, crates/core/src/lib.rs, python/aisimulate/src/aisimulate_core/sdk/engine.py	Resolved scheme kind, verification width, and maximum accepted draft tokens are carried in engine configuration. The engine validates and stores the metadata, and the performance model exposes it.
Replay validation and role configuration crates/core/src/python.rs, python/aisimulate/src/aisimulate/{compiler.py,main.py,runner.py,sweeper/replay.py}, tests/test_{runner,ngram_cli}.py, python/aisimulate/tests/integration/test_agentic_speculation.py	Replay parsing and validation support scheme-based speculation and apply resolved geometry to rank configuration. Runner capability and compatibility checks account for speculation settings. Integration tests cover replay, compatibility, and validation.
Verification width and decode reservations crates/core/src/engine/common/*, crates/core/src/engine/config.rs, crates/core/src/engine/scheduler/rank.rs, crates/core/src/engine/scheduler/{sglang,vllm}/*	Engine configuration accepts an explicit verification width. vLLM and SGLang use verification width, accepted-draft limits, and remaining output length when sizing decode lookahead and page reservations.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🔵 Low · up to 2b409

Speculation still rejects workers without fixed KV capacity, but the error does not identify which role failed, which can add troubleshooting in multi-role configurations. The remaining merge risk is bounded.

Pre-merge checks: 6 · 1 · 1

Failed checks: 1 warning · 1 inconclusive

Check name	Status	Explanation	Resolution
Cross-Layer Contract		The new generic SchemeSpeculationConfig path leaves a stale Python consumer. EnginePredictionConfig.speculation now accepts generic schemes (`python/aisimulate/src/aisimulate/config/engine.py:314-…	Update state_size.resolve_state_size to use a scheme-neutral draft-depth value, or omit the speculative-token metadata where it does not affect state-cache geometry. Add a regression test that compiles generic kind: ngram with inferred …
Review Evidence		The description reports results (1,198 Python checks, 2,048 native checks, CLI scenarios, and Dashboard replay) and negative probes, but it does not name the commands used or clearly classify the evid…	Provide the exact validation commands and label each result by evidence type (local, fake-runner, parity, or production). Clarify the hosted-CI reference if it is meant to refer to a workflow run; then the evidence can be assessed against t…

Passed checks: 6 passed

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Modeling And Data Evidence		The PR changes replay selection and speculative KV reservation, but it does not refresh performance data or add new cost formulas. The new integration test provides a reproducible MTP before/after com…
Compatibility Boundaries		The compatibility boundaries remain intact. Rust and Python both use ENGINE_CONFIG_SCHEMA_VERSION 1; Python obtains ENGINE_SPEC_SCHEMA_VERSION from the Rust binding. The new EngineConfig and Speculati…
Title check		The title precisely describes enabling generic speculative decoding in AgentX replay and avoids vague wording.
Description check		The description clearly explains the behavioral change, affected consumers, compatibility constraints, public configuration, validation results, reviewed commits, and related work. It does not use the…

Full details: Modeling And Data Evidence

Explanation

The PR changes replay selection and speculative KV reservation, but it does not refresh performance data or add new cost formulas. The new integration test provides a reproducible MTP before/after comparison: it compares the selected model graph and static latency with legacy nextn=3, then compares per-request replay rows for the two configurations. Provenance is checked through canonical diagnostics and retained draft configuration. Tree behavior has focused checks for deterministic replay and expected short-tail KV reservation. The PR also documents that these simulations do not establish measured hardware speedup. This is not binary-only validation, and the changes do not introduce an unexplained data refresh.

Full details: Compatibility Boundaries

Explanation

The compatibility boundaries remain intact. Rust and Python both use ENGINE_CONFIG_SCHEMA_VERSION 1; Python obtains ENGINE_SPEC_SCHEMA_VERSION from the Rust binding. The new EngineConfig and SpeculativeConfig fields are optional/defaulted JSON fields, and the EngineSpec positional OpSpec layout did not change, so the existing schema version policy does not call for an EngineSpec version bump. Python forwards resolved scheme dimensions through the Rust EngineConfig JSON path, and the documentation describes the scheme identity, replay-only acceptance/seed, and relevant defaults. The PR changes no package manifests, lockfiles, or artifact scripts. The unchanged release artifact checks still enforce one AISimulate wheel and one crate, and prohibit Dynamo dependencies.

Full details: Description check

Explanation

The description clearly explains the behavioral change, affected consumers, compatibility constraints, public configuration, validation results, reviewed commits, and related work. It does not use the required template headings or explicitly provide every requested field, such as risk level, review starting points, full CI status, provenance, and tracking entries, but the critical information is substantially present.

Full details: Cross-Layer Contract

Explanation

The new generic SchemeSpeculationConfig path leaves a stale Python consumer. EnginePredictionConfig.speculation now accepts generic schemes (python/aisimulate/src/aisimulate/config/engine.py:314-366, 619), including registry ngram, but state_size.resolve_state_size still reads engine.speculation.num_speculative_tokens (python/aisimulate/src/aisimulate/state_size.py:25-35). For a generic kind: ngram input with inferred state_cache.bytes_per_request, _deployment calls this function before compiling the replay (python/aisimulate/src/aisimulate/compiler.py:231-239), so it raises AttributeError instead of materializing the configuration. This combination passes the current validation: generic ngram resolves through the SDK registry (speculation/ngram.py:50-71), and state cache is allowed for fixed-capacity aggregated vLLM when prefix_match_unit is absent (config/engine.py:265-284, 1066-1074). The new integration tests cover generic schemes without inferred state cache, so this consumer path is untested.

Resolution

Update state_size.resolve_state_size to use a scheme-neutral draft-depth value, or omit the speculative-token metadata where it does not affect state-cache geometry. Add a regression test that compiles generic kind: ngram with inferred state-cache sizing through CorePredictionConfig and prediction_to_replay_spec. If that combination is not supported, reject it during configuration validation with a clear error before resolve_state_size runs.

Full details: Review Evidence

Explanation

The description reports results (1,198 Python checks, 2,048 native checks, CLI scenarios, and Dashboard replay) and negative probes, but it does not name the commands used or clearly classify the evidence as local, fake-runner, parity, or production. It says hosted CI is reported by the checks below, but it does not cite an inactive nested workflow as hosted CI. The workflow inspection found reusable workflow_call definitions, but no evidence in the description ties them to its hosted-CI statement.

Resolution

Provide the exact validation commands and label each result by evidence type (local, fake-runner, parity, or production). Clarify the hosted-CI reference if it is meant to refer to a workflow run; then the evidence can be assessed against the check.

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Review comments at @python/aisimulate/src/aisimulate/runner.py:
- Around line 1812-1814: Validate the `speculation` value in the `timing_config`
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🔇 Additional comments (2)

crates/core/src/python.rs (1)

193-215: 🩺 Stability & Availability

The proposed extra gate is unnecessary. Both production call paths resolve role timing before calling validate_public_agentic_engine. For explicit speculation with no aic_nextn, materialize_aic_capacity rejects the nonzero speculation depth against aic_nextn.unwrap_or(0) before the validator runs. No accepted production path skips the MTP prerequisites.

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64-64: 🎯 Functional Correctness

Generated CODEOWNERS is up to date.

CODEOWNERS contains both replay and shared-owner entries for tests/test_mtp_cli.py with the owners declared in .github/codeowners/areas.yaml.

[Harrilee]

Reworked this PR from MTP-only enablement to API enablement + algorithm override through AISimulate's existing generic SD interface.

• AgentX now passes the selected scheme, SDK parameters and draft identity through the canonical best_available path. Supported selections use the existing MTP/Ngram/EAGLE3/DFlash/draft-model/DSpark materializers; unsupported family/backend combinations fail explicitly.
• Accepted path depth and verification width are passed separately so tree schemes retain their existing draft/verification behavior. No new MTP cost formula was added. FPM estimator selection, formulas and data are unchanged.
• Rebased onto current main, resolved the shared engine-validation/G2 conflicts, and retained the existing non-SD G2 support. Also fixed malformed speculation mappings to return ValueError.

Retested the installed builds after rebase: AISimulate 1,268 Python and 2,044 native checks passed (one existing fixture-dependent test ignored); all 8 Engine and 16 Dynamo CLI scenarios passed. Dashboard SD-on/off replay passed for all 15 cohorts, with zero changed predictions in either mode across 18,818 valid measured requests. Overall run-mean E2E bias remains +32.16%, MAPE 54.41%. Six MiniMax EAGLE3 probes hit the existing unsupported-family error, so those displayed runs remain explicitly labelled MTP surrogates. No acceptance tuning or accuracy improvement is claimed.

I am resolving the two threads attached to the superseded MTP-only wiring. The inherited MTP cost/attention approximations raised there remain valid modeling limitations; this update does not claim those approximations have been calibrated or corrected. Those modeling changes are separate from this API integration.

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Review comments at @python/aisimulate/src/aisimulate/config/engine.py:
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• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
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• python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py
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📓 Path-based instructions (11)

Preserve the Rust single oracle: Python may describe operations, load raw data, orchestrate, and present results, but must not compute per-op performance values.

⚙️ CodeRabbit configuration file

Files:

• python/aisimulate/src/aisimulate_core/sdk/engine.py
• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
• python/aisimulate/src/aisimulate_core/sdk/models/__init__.py

Check unified CLI, Replay, Sweeper, and orchestration behavior together.

⚙️ CodeRabbit configuration file

Files:

• python/aisimulate/src/aisimulate/compiler.py
• python/aisimulate/src/aisimulate/main.py
• python/aisimulate/src/aisimulate/runner.py
• python/aisimulate/src/aisimulate/config/engine.py

Enforce the single-oracle and golden-diff rules in python/aisimulate/.claude/rules/rust-core/parity.md.

⚙️ CodeRabbit configuration file

Files:

• crates/core/src/perfmodel/py.rs
• crates/core/src/perfmodel/fpm/model.rs
• crates/core/src/perfmodel/fpm/config.rs

Treat top-level exports and bindings as public and release boundaries.

⚙️ CodeRabbit configuration file

Files:

• crates/core/src/python.rs

Require coverage of the changed behavior and its negative or boundary cases.

⚙️ CodeRabbit configuration file

Files:

• tests/test_ngram_cli.py

Check commands, defaults, supported runtimes, public names, and claims against executable behavior.

⚙️ CodeRabbit configuration file

Files:

• docs/core-api.md

Read REVIEW.md before commenting.

⚙️ CodeRabbit configuration file

Files:

• crates/core/src/perfmodel/py.rs
• crates/core/src/perfmodel/fpm/model.rs
• tests/test_ngram_cli.py
• python/aisimulate/src/aisimulate_core/sdk/engine.py
• docs/core-api.md
• python/aisimulate/src/aisimulate/compiler.py
• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
• python/aisimulate/src/aisimulate/main.py
• python/aisimulate/tests/integration/test_agentic_speculation.py
• python/aisimulate/src/aisimulate_core/sdk/models/__init__.py
• crates/core/src/perfmodel/fpm/config.rs
• python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py
• crates/core/src/python.rs
• python/aisimulate/src/aisimulate/runner.py
• python/aisimulate/src/aisimulate/config/engine.py

Source excerpt: Do NOT add Python-side interpolation, roofline/SOL formulas, empirical-utilization estimates, or per-call table lookups anywhere under `python/aisimulate/src/aisimulate_core/sdk/` (banned def shapes: the `_query_*` and `_loo...

📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/rust-core/parity.md)

Files:

• crates/core/src/perfmodel/py.rs
• crates/core/src/perfmodel/fpm/model.rs
• python/aisimulate/src/aisimulate_core/sdk/engine.py
• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
• python/aisimulate/src/aisimulate_core/sdk/models/__init__.py
• crates/core/src/perfmodel/fpm/config.rs

Before making any change under: `python/aisimulate/src/aisimulate/generator/**` MUST read: `python/aisimulate/.claude/rules/generator-development.md` Before making any change under `python/aisimulate/collector/**` MUST read: `python/aisimul...

📄 CodeRabbit inference engine (AGENTS.md)

Files:

• crates/core/src/perfmodel/py.rs
• crates/core/src/perfmodel/fpm/model.rs
• tests/test_ngram_cli.py
• python/aisimulate/src/aisimulate_core/sdk/engine.py
• docs/core-api.md
• python/aisimulate/src/aisimulate/compiler.py
• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
• python/aisimulate/src/aisimulate/main.py
• python/aisimulate/tests/integration/test_agentic_speculation.py
• python/aisimulate/src/aisimulate_core/sdk/models/__init__.py
• crates/core/src/perfmodel/fpm/config.rs
• python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py
• crates/core/src/python.rs
• python/aisimulate/src/aisimulate/runner.py
• python/aisimulate/src/aisimulate/config/engine.py

Source excerpt: Only workflows under the repository-root `.github/workflows/` run for this repository.

📄 CodeRabbit inference engine (REVIEW.md)

Files:

• crates/core/src/perfmodel/py.rs
• crates/core/src/perfmodel/fpm/model.rs
• tests/test_ngram_cli.py
• python/aisimulate/src/aisimulate_core/sdk/engine.py
• docs/core-api.md
• python/aisimulate/src/aisimulate/compiler.py
• python/aisimulate/src/aisimulate_core/sdk/speculation/base.py
• python/aisimulate/src/aisimulate/main.py
• python/aisimulate/tests/integration/test_agentic_speculation.py
• python/aisimulate/src/aisimulate_core/sdk/models/__init__.py
• crates/core/src/perfmodel/fpm/config.rs
• python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py
• crates/core/src/python.rs
• python/aisimulate/src/aisimulate/runner.py
• python/aisimulate/src/aisimulate/config/engine.py

Source excerpt: Keep upstream AIC Python code/data in `python/aisimulate/src/aisimulate/` and `python/aisimulate/src/aisimulate_core/`.

📄 CodeRabbit inference engine (DEVELOPMENT.md)

Files:

• python/aisimulate/src/aisimulate/compiler.py
• python/aisimulate/src/aisimulate/main.py
• python/aisimulate/src/aisimulate/runner.py
• python/aisimulate/src/aisimulate/config/engine.py

🪛 ast-grep (0.45.3)

python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py

[info] 537-537: use jsonify instead of json.dumps for JSON output
Context: json.dumps(EAGLE3_CONFIG)
Note: [CWE-116] Improper Encoding or Escaping of Output.

(use-jsonify)

🪛 LanguageTool

docs/core-api.md

[grammar] ~449-~449: Ensure spelling is correct
Context: ...egacy nextn/nextn_accepted and flat ngram inputs remain available; do not combine...

(QB_NEW_EN_ORTHOGRAPHY_ERROR_IDS_1)

🔀 Multi-repo context

Linked repositories findings

ai-dynamo/dynamo

Inspected the branch for open PR #15627 at 96e5b9c8 (not the default branch).

• components/src/dynamo/replay/config.py:143–189 is a concrete consumer of generic speculation: it validates the selection, preserves its cost config in AIS timing, and lowers accepted-token depth, verification width, acceptance rates, and seed to scheduler controls. It rejects mixing these controls with legacy fields. The native boundary maps those fields into EngineConfig in lib/mocker/src/engine_adapter.rs:123–126; lib/mocker/src/common/protocols.rs:1328–1348 checks them against resolved scheme geometry.
• components/src/dynamo/replay/tests/test_simulation.py:829–862 covers MTP, ngram, Eagle3, DFlash, DSpark, and draft-model selections. Eagle3 explicitly tests accepted depth 3 alongside verify width 16, confirming those are separate values at this boundary.
• The branch pins aisimulate-core to 8e69498db4e15b1b8bc3cef05d350ab9641d696d and the Python package to aisimulate==0.13.0 (Cargo.toml:61–62, pyproject.toml:15–18). This is branch-specific integration evidence, not a claim about the default branch.
• A separate router simulation conversion remains scalar-shaped: components/src/dynamo/router/simulation/provider.py:178–184 builds params.num_speculative_tokens from engine.speculation["num_speculative_tokens"]. Keep this legacy conversion distinct from the generic AgentX replay path when assessing which inputs are supported through each adapter.

ai-dynamo/aiconfigurator

Inspected at f254959e as the frozen migration/compatibility reference; it is not the runtime dependency.

• Its Rust SpeculativeConfig contains only optional nextn, documented as MTP depth (aic-core/rust/aiconfigurator-core/src/config.rs:214–224). Its Python input contract requires an explicit nextn_accepted assumption for active MTP (src/aiconfigurator/sdk/speculative.py:28–50).
• The legacy MTP compute scaling is specifically (nextn + num_layers) / num_layers, with accepted-token progress applied above the engine (aic-core/src/aiconfigurator_core/sdk/models/helpers.py:1033–1042). This is useful compatibility context, but does not establish generic scheme cost formulas in that repository.

🔇 Additional comments (15)

crates/core/src/perfmodel/fpm/config.rs (1)

448-471: LGTM!

python/aisimulate/src/aisimulate_core/sdk/speculation/base.py (1)

85-94: LGTM!

python/aisimulate/src/aisimulate_core/sdk/models/__init__.py (1)

276-281: LGTM!

crates/core/src/perfmodel/py.rs (1)

1669-1679: LGTM!

docs/core-api.md (1)

418-450: LGTM!

python/aisimulate/tests/unit/sdk/speculation/test_dense_draft_schemes.py (1)

504-599: LGTM!

crates/core/src/perfmodel/fpm/model.rs (1)

399-399: LGTM!

python/aisimulate/src/aisimulate/compiler.py (1)

18-18: LGTM!

Also applies to: 43-46

python/aisimulate/src/aisimulate/main.py (1)

26-26: LGTM!

Also applies to: 105-110

python/aisimulate/src/aisimulate/runner.py (2)

1408-1419: LGTM!

---

1973-1980: LGTM!

crates/core/src/python.rs (1)

1089-1096: LGTM!

Also applies to: 1129-1133, 1033-1034, 3836-3849

tests/test_ngram_cli.py (1)

188-188: LGTM!

Also applies to: 345-345

python/aisimulate/tests/integration/test_agentic_speculation.py (1)

3-165: LGTM!

python/aisimulate/src/aisimulate_core/sdk/engine.py (1)

415-417: 🎯 Functional Correctness

The reported width mismatch is refuted. Materialization sets _nextn to verify_width - 1, not to accepted depth. For the stated width of 10, _nextn is 9, so the engine emits metadata with verify_width: 10, which satisfies the Rust check. The zero-_nextn path described in the comment does not apply to this materialized scheme.

[tedzhouhk]

Reviewed d83d417 with a freshly built native library. 1,424 existing tests passed, including both frozen-golden parity suites. Three additional boundary probes reproduced the inline P2 findings below; these concern input/fallback consistency, not a claim of newly incorrect GPU cost formulas.