From 256001ca806f9d7e302bea967850db214f3d8e4e Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 00:35:22 +0000
Subject: [PATCH 01/10] Add training-free choice readout evaluation matrix

---
 jevany/letter_predictor.py                   | 158 ++++++--
 jevany/letter_readout.py                     |  35 +-
 scripts/evaluate_choice_readout_matrix.py    | 359 +++++++++++++++++++
 scripts/fit_choice_ensemble.py               | 298 +++++++++++++++
 tests/test_evaluate_choice_readout_matrix.py | 129 +++++++
 tests/test_fit_choice_ensemble.py            |  65 ++++
 tests/test_letter_predictor.py               |  65 ++++
 tests/test_letter_readout.py                 |  25 +-
 8 files changed, 1073 insertions(+), 61 deletions(-)
 create mode 100644 scripts/evaluate_choice_readout_matrix.py
 create mode 100644 scripts/fit_choice_ensemble.py
 create mode 100644 tests/test_evaluate_choice_readout_matrix.py
 create mode 100644 tests/test_fit_choice_ensemble.py

diff --git a/jevany/letter_predictor.py b/jevany/letter_predictor.py
index f5c9e4b..e0856f9 100644
--- a/jevany/letter_predictor.py
+++ b/jevany/letter_predictor.py
@@ -23,7 +23,7 @@
 from .data import api_request, materialize
 from .device import sync
 from .letter_readout import (
-    LETTERS,
+    CHOICE_SYMBOLS,
     SYSTEM_PROMPT,
     build_prompt,
     letter_token_ids,
@@ -61,17 +61,19 @@ def _safe_chat_text(tokenizer, text: str) -> str:
     return escaped
 
 
-def _release_unused_output_head(decision_model) -> bool:
-    """Drop references to the full-vocabulary head unused by letter readout."""
+def _release_unused_output_head(decision_model, *, retain_native: bool = False) -> bool:
+    """Drop the full-vocabulary head unless a native LM-token pass needs it."""
 
     adapter = getattr(decision_model, "adapter", None)
     adapter_head = getattr(adapter, "_output_embeddings", None)
     native_head = getattr(decision_model, "lm_head", None)
+    if retain_native:
+        return False
     if adapter_head is None and native_head is None:
         return False
-    # The exact letter rows are copied immediately after this call.  Native
-    # lm-token inference is never invoked by this predictor, so both aliases
-    # can be detached even for direct-token checkpoints.
+    # Exact choice rows are copied from the tied embedding table or loaded
+    # independently from safetensors.  With no LM-token native pass, both
+    # aliases can therefore be detached to release the full vocabulary head.
     if native_head is not None:
         decision_model.lm_head = None
     if adapter_head is not None:
@@ -223,9 +225,10 @@ class LetterReadoutPredictor:
     """Benchmark predictor for an exact constrained first-token readout.
 
     Give either ``base`` for a frozen training-free model, or ``checkpoint``
-    to apply a JevAny LoRA before the same readout.  ``pointer_weight > 0``
-    additionally pools the checkpoint's native pointer probabilities in log
-    space; this costs one extra pointer-formatted prefill per request.
+    to apply a JevAny LoRA before the same readout.  ``native_weight > 0``
+    additionally pools the checkpoint's native probabilities in log space;
+    this costs one extra native-formatted prefill per request.  The legacy
+    ``pointer_weight`` spelling remains an alias for ``native_weight``.
     """
 
     def __init__(
@@ -237,7 +240,10 @@ def __init__(
         options: LoadOptions | None = None,
         revision: str | None = None,
         temperature: float = 1.0,
-        pointer_weight: float = 0.0,
+        pointer_weight: float | None = None,
+        native_weight: float | None = None,
+        return_components: bool = False,
+        exact_kernels: bool = True,
         max_tokens: int = 16_384,
     ) -> None:
         if (base is None) == (checkpoint is None):
@@ -247,11 +253,32 @@ def __init__(
         self.temperature = float(temperature)
         if not math.isfinite(self.temperature) or self.temperature <= 0:
             raise ValueError("temperature must be finite and positive")
+        if pointer_weight is not None and native_weight is not None:
+            raise ValueError("give at most one of native_weight or pointer_weight")
+        weight_source = (
+            "native_weight" if native_weight is not None
+            else "pointer_weight" if pointer_weight is not None
+            else "default"
+        )
+        selected_weight = (
+            native_weight if native_weight is not None
+            else pointer_weight if pointer_weight is not None
+            else 0.0
+        )
         # Reuse the same validation as the actual combination path.
-        geometric_blend([1.0], [1.0], pointer_weight)
-        self.pointer_weight = float(pointer_weight)
-        if checkpoint is None and self.pointer_weight:
-            raise ValueError("pointer_weight requires a JevAny checkpoint")
+        geometric_blend([1.0], [1.0], selected_weight)
+        self.native_weight = float(selected_weight)
+        # Public compatibility alias for existing runtime/CLI integrations.
+        self.pointer_weight = self.native_weight
+        if checkpoint is None and self.native_weight:
+            raise ValueError("native_weight requires a JevAny checkpoint")
+        if type(return_components) is not bool:
+            raise TypeError("return_components must be a boolean")
+        if type(exact_kernels) is not bool:
+            raise TypeError("exact_kernels must be a boolean")
+        self.return_components = return_components
+        self.exact_kernels = exact_kernels
+        self.exact_cuda_kernels_applied = device == "cuda" and exact_kernels
         if type(max_tokens) is not int or max_tokens < 2:
             raise ValueError("max_tokens must be an integer >= 2")
         self.max_tokens = max_tokens
@@ -261,7 +288,22 @@ def __init__(
             raise ValueError("native-head temperature is not used by letter readout; use temperature")
         if self.options.cuda_graphs:
             raise ValueError("CUDA graph capture is available only for native readout")
+        if self.exact_cuda_kernels_applied:
+            # Match LocalPredictor: benchmark evaluation disables approximate
+            # TF32 and fused SDPA kernels, while exact_kernels=False retains
+            # the serving-style PyTorch defaults.
+            torch.backends.cuda.matmul.allow_tf32 = False
+            torch.backends.cudnn.allow_tf32 = False
+            torch.backends.cuda.enable_flash_sdp(False)
+            torch.backends.cuda.enable_mem_efficient_sdp(False)
         self.checkpoint: Checkpoint | None = None
+        self.native_model = None
+        self.native_decision_mode = None
+        self._needs_native = checkpoint is not None and bool(
+            self.native_weight or self.return_components
+        )
+        # Compatibility alias retained for callers predating generalized
+        # native blending.
         self.pointer_model = None
 
         if checkpoint is not None:
@@ -270,10 +312,10 @@ def __init__(
             loaded = self.checkpoint = Checkpoint(checkpoint)
             if loaded.meta.special_embeddings:
                 raise ValueError("letter readout is not validated for checkpoints with trained special embeddings")
-            if self.pointer_weight and loaded.meta.decision_mode != "pointer":
-                raise ValueError("pointer_weight requires a native pointer checkpoint")
             self.preprocessor, decision_model = loaded.load(device, self.options)
+            self.native_model = decision_model
             self.pointer_model = decision_model
+            self.native_decision_mode = loaded.meta.decision_mode
             self.language_model = decision_model.lm
             canonical_base = loaded.meta.base
             canonical_revision = loaded.meta.base_revision
@@ -305,7 +347,10 @@ def __init__(
             projection_source, projection_revision = source, source_revision
             checkpoint_id, adapter_scale, adapter_applied = None, 0.0, False
 
-        released_output_head = _release_unused_output_head(decision_model)
+        released_output_head = _release_unused_output_head(
+            decision_model,
+            retain_native=(self._needs_native and self.native_decision_mode == "lm_token"),
+        )
         self.language_model.eval()
         override_config = (
             Path(self.options.base_load_path) / "config.json"
@@ -339,7 +384,7 @@ def __init__(
             hashlib.sha256(chat_template.encode()).hexdigest()
             if isinstance(chat_template, str) else None
         )
-        self.alias_token_ids = letter_token_ids(self.tokenizer, LETTERS)
+        self.alias_token_ids = letter_token_ids(self.tokenizer, CHOICE_SYMBOLS)
         flat_token_ids, alias_rows = [], {}
         for letter, token_ids in self.alias_token_ids.items():
             start = len(flat_token_ids)
@@ -368,12 +413,12 @@ def __init__(
             if not math.isfinite(self.output_softcap) or self.output_softcap <= 0:
                 raise ValueError("final_logit_softcapping must be finite and positive")
         self.provenance = {
-            "method": "exact option-letter choice projection",
-            "constraint_emulation": "exact decoded uppercase letters",
-            "prompt": "Cygnet-compatible",
+            "method": "training-free exact choice-token projection",
+            "constraint_emulation": "exact decoded one-character option IDs",
+            "prompt": "Cygnet-compatible A-Z; extended with a-z above 26 options",
             "system_prompt_sha256": hashlib.sha256(SYSTEM_PROMPT.encode()).hexdigest(),
             "chat_template_sha256": self.chat_template_sha256,
-            "prompt_format_version": 1,
+            "prompt_format_version": 2,
             "canonical_base": canonical_base,
             "canonical_revision": canonical_revision,
             "checkpoint": checkpoint_id,
@@ -384,10 +429,19 @@ def __init__(
             "letter_choice_token_rows": len(flat_token_ids),
             "output_softcap": self.output_softcap,
             "temperature": self.temperature,
+            "native_weight": self.native_weight,
+            "native_weight_argument": weight_source,
+            "native_decision_mode": self.native_decision_mode,
             "pointer_weight": self.pointer_weight,
+            "native_temperature": (
+                float(self.native_model.temperature) if self._needs_native else None
+            ),
             "pointer_temperature": (
-                float(self.pointer_model.temperature) if self.pointer_weight else None
+                float(self.native_model.temperature) if self._needs_native else None
             ),
+            "return_components": self.return_components,
+            "exact_kernels": self.exact_kernels,
+            "exact_cuda_kernels_applied": self.exact_cuda_kernels_applied,
             "max_tokens": self.max_tokens,
             "backbone_context_window": self.backbone_context_window,
             "effective_max_tokens": self.effective_max_tokens,
@@ -417,8 +471,10 @@ def _chat_ids(self, prompt: str) -> torch.Tensor:
 
     def _letter_question(self, state, question: dict) -> tuple[list[str], list[float], list[float], int]:
         keys, descriptions = question_options(question)
-        if len(keys) > len(LETTERS):
-            raise ValueError(f"exact letter readout supports at most {len(LETTERS)} options")
+        if len(keys) > len(CHOICE_SYMBOLS):
+            raise ValueError(
+                f"exact choice-token readout supports at most {len(CHOICE_SYMBOLS)} options"
+            )
         prompt = build_prompt(state, question.get("instructions") or "", descriptions)
         ids = self._chat_ids(prompt)
         output = self.language_model(
@@ -434,15 +490,17 @@ def _letter_question(self, state, question: dict) -> tuple[list[str], list[float
         if self.output_softcap is not None:
             selected_logits = self.output_softcap * torch.tanh(selected_logits / self.output_softcap)
         selected_logits = selected_logits.cpu()
-        aliases = {letter: self.alias_rows[letter] for letter in LETTERS[:len(keys)]}
+        aliases = {
+            symbol: self.alias_rows[symbol] for symbol in CHOICE_SYMBOLS[:len(keys)]
+        }
         readout = read_letter_distribution(selected_logits, aliases, self.temperature)
         probabilities = [readout.calibrated_probabilities[letter] for letter in aliases]
         calibrated_logits = [readout.raw_log_masses[letter] / self.temperature for letter in aliases]
         return keys, probabilities, calibrated_logits, int(ids.shape[1])
 
-    def _pointer(self, record: dict) -> tuple[list[list[float]], int]:
+    def _native(self, record: dict) -> tuple[list[list[float]], int]:
         internal = materialize(record)
-        encoded = self.pointer_model.encode(
+        encoded = self.native_model.encode(
             self.preprocessor,
             internal,
             max_state=self.effective_max_tokens,
@@ -451,11 +509,16 @@ def _pointer(self, record: dict) -> tuple[list[list[float]], int]:
         )
         if len(encoded["ids"]) > self.effective_max_tokens:
             raise ContextLengthError(
-                f"pointer request needs {len(encoded['ids'])} packed tokens; "
+                f"native request needs {len(encoded['ids'])} packed tokens; "
                 f"limit {self.effective_max_tokens}"
             )
         return [F.softmax(logits, -1).float().cpu().tolist()
-                for logits in self.pointer_model.forward(encoded)], len(encoded["ids"])
+                for logits in self.native_model.forward(encoded)], len(encoded["ids"])
+
+    def _pointer(self, record: dict) -> tuple[list[list[float]], int]:
+        """Compatibility alias for the generalized native checkpoint pass."""
+
+        return self._native(record)
 
     @torch.inference_mode()
     def __call__(self, record: dict) -> dict:
@@ -469,37 +532,54 @@ def __call__(self, record: dict) -> dict:
             self._letter_question(request["state"], question)
             for question in request["questions"].values()
         ]
-        pointer_rows, pointer_tokens = (None, 0)
-        if self.pointer_weight:
-            pointer_rows, pointer_tokens = self._pointer(record)
-            if len(pointer_rows) != len(letter_rows):
-                raise ValueError("pointer and letter question counts differ")
+        native_rows, native_tokens = (None, 0)
+        if self._needs_native:
+            native_rows, native_tokens = self._native(record)
+            if len(native_rows) != len(letter_rows):
+                raise ValueError("native and choice question counts differ")
 
         probabilities, logits = {}, {}
+        choice_probabilities, native_probabilities = {}, {}
         for index, (question_id, (keys, letter_p, letter_z, _tokens)) in enumerate(
             zip(request["questions"], letter_rows, strict=True)
         ):
-            if pointer_rows is None:
+            choice_probabilities[question_id] = dict(zip(keys, letter_p, strict=True))
+            if native_rows is not None:
+                if len(native_rows[index]) != len(keys):
+                    raise ValueError(
+                        f"native and choice option counts differ for question {question_id!r}"
+                    )
+                native_probabilities[question_id] = dict(
+                    zip(keys, native_rows[index], strict=True)
+                )
+            if native_rows is None or not self.native_weight:
                 selected_p, selected_z = letter_p, letter_z
             else:
                 selected_p, selected_z = geometric_blend(
-                    letter_p, pointer_rows[index], self.pointer_weight
+                    letter_p, native_rows[index], self.native_weight
                 )
             probabilities[question_id] = dict(zip(keys, selected_p, strict=True))
             logits[question_id] = dict(zip(keys, selected_z, strict=True))
         sync(self.device)
-        return {
+        result = {
             "probabilities": probabilities,
             "logits": logits,
             "inference_temperature": self.temperature,
             "latency_ms": (time.perf_counter() - started) * 1000,
-            "input_tokens": sum(row[3] for row in letter_rows) + pointer_tokens,
+            "input_tokens": sum(row[3] for row in letter_rows) + native_tokens,
             "readout": {
                 "method": self.provenance["method"],
                 "adapter_applied": self.provenance["adapter_applied"],
+                "native_weight": self.native_weight,
+                "native_decision_mode": self.native_decision_mode,
                 "pointer_weight": self.pointer_weight,
             },
         }
+        if self.return_components:
+            result["component_probabilities"] = {"choice": choice_probabilities}
+            if native_rows is not None:
+                result["component_probabilities"]["native"] = native_probabilities
+        return result
 
 
 __all__ = ["LetterReadoutPredictor", "geometric_blend", "question_options"]
diff --git a/jevany/letter_readout.py b/jevany/letter_readout.py
index 0b857f7..f38ac57 100644
--- a/jevany/letter_readout.py
+++ b/jevany/letter_readout.py
@@ -1,4 +1,4 @@
-"""One-token option-letter readout primitives for frozen causal LMs.
+"""One-token option-ID readout primitives for frozen causal LMs.
 
 The Cygnet-compatible prompt and letter aggregation semantics are adapted from
 the MIT-licensed ``blockbrain-ai/cygnet-recipe`` shim at commit ``3cf591c``
@@ -18,11 +18,18 @@
 from typing import Any
 
 
-LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+CYGNET_LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+# Preserve Cygnet's exact A-Z assignment for its supported range, then extend
+# with distinct lowercase one-character IDs for larger decision sets. This
+# covers JevJudge's 28-30-option text requests without dropping records.
+CHOICE_SYMBOLS = CYGNET_LETTERS + "abcdefghijklmnopqrstuvwxyz"
+# Backward-compatible implementation name. Public documentation calls these
+# option IDs and the method training-free choice readout.
+LETTERS = CHOICE_SYMBOLS
 SYSTEM_PROMPT = (
     "You are a calibration engine. You never answer in prose. You are given a state, a question and "
     "a numbered set of options, and you choose exactly one option. You reply with that option's "
-    "LETTER and nothing else — a single character, no words, no punctuation, no explanation."
+    "case-sensitive ID and nothing else — a single character, no words, no punctuation, no explanation."
 )
 
 _NEGATIVE_INFINITY = float("-inf")
@@ -63,7 +70,7 @@ def _ordered_descriptions(options: Mapping[Any, Any] | Sequence[Any]) -> list[An
 
 
 def build_prompt(state: Any, instructions: Any, options: Mapping[Any, Any] | Sequence[Any]) -> str:
-    """Build Cygnet's measured user prompt, assigning options A through Z.
+    """Build the measured user prompt, assigning one-character option IDs.
 
     Mapping insertion order or sequence order is the option order.  Structured
     state is rendered with ``indent=1`` as in Cygnet.  This function only
@@ -78,22 +85,22 @@ def build_prompt(state: Any, instructions: Any, options: Mapping[Any, Any] | Seq
     )
     lines = [state_text.rstrip(), "", instruction_text.rstrip(), "", "Options:"]
     lines.extend(f"{LETTERS[index]}. {description}" for index, description in enumerate(descriptions))
-    lines.extend(("", "Answer with the letter of exactly one option, and nothing else:"))
+    lines.extend(("", "Answer with the case-sensitive ID of exactly one option, and nothing else:"))
     return "\n".join(lines)
 
 
 def _validate_letters(letters: Sequence[str]) -> tuple[str, ...]:
     if isinstance(letters, (bytes, bytearray)):
-        raise TypeError("letters must be a sequence of A-Z strings")
+        raise TypeError("option IDs must be a sequence of one-character strings")
     selected = tuple(letters)
     if not selected:
-        raise ValueError("letters must not be empty")
+        raise ValueError("option IDs must not be empty")
     if len(selected) > len(LETTERS):
         raise ValueError(f"letters may contain at most {len(LETTERS)} entries")
-    if any(type(letter) is not str or len(letter) != 1 or letter not in LETTERS for letter in selected):
-        raise ValueError("letters must contain only single uppercase A-Z strings")
+    if any(type(letter) is not str or len(letter) != 1 or letter not in CHOICE_SYMBOLS for letter in selected):
+        raise ValueError("option IDs must be distinct single A-Z/a-z characters")
     if len(set(selected)) != len(selected):
-        raise ValueError("letters must not contain duplicates")
+        raise ValueError("option IDs must not contain duplicates")
     return selected
 
 
@@ -113,12 +120,12 @@ def _decode_token(tokenizer: Any, token_id: int) -> str:
 
 
 def letter_token_ids(tokenizer: Any, letters: Sequence[str]) -> dict[str, tuple[int, ...]]:
-    """Scan the vocabulary for every token ID decoding to each exact letter.
+    """Scan the vocabulary for every token ID decoding to each exact option ID.
 
     Token text is intentionally never used as a dictionary key: distinct token
     IDs may decode to identical text, and every such ID contributes probability
-    mass.  Tokens decoding to ``" A"``, ``"A."``, or ``"a"`` are excluded:
-    Cygnet's structured-output choice admits the exact uppercase string only.
+    mass. Tokens decoding to ``" A"`` or ``"A."`` are excluded. Case is
+    significant: ``A`` and ``a`` are different option IDs when both are used.
     ``len(tokenizer)`` must describe the full vocabulary, including added tokens.
     """
 
@@ -338,6 +345,8 @@ def read_letter_distribution(
 
 
 __all__ = [
+    "CHOICE_SYMBOLS",
+    "CYGNET_LETTERS",
     "LETTERS",
     "SYSTEM_PROMPT",
     "LetterReadout",
diff --git a/scripts/evaluate_choice_readout_matrix.py b/scripts/evaluate_choice_readout_matrix.py
new file mode 100644
index 0000000..7406c6e
--- /dev/null
+++ b/scripts/evaluate_choice_readout_matrix.py
@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+"""Evaluate training-free choice readout and a checkpoint's native readout.
+
+The script loads one model once, then scores the same frozen model on JevBench,
+Transfer-v9, Typed Decisions and JevJudge's text-only subset.  Checkpoint runs
+save both readout components so ensemble weights can be selected offline on a
+separate calibration split without repeating inference or looking at test
+labels.  Frozen-base runs save the training-free choice component only.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import platform
+import statistics
+import subprocess
+import time
+from collections import defaultdict
+from pathlib import Path
+import math
+
+import numpy as np
+import pyarrow
+import pyarrow.parquet as pq
+import peft
+import safetensors
+import torch
+import transformers
+
+from jevany.api import question_keys
+from jevany.benchmark import prediction_rows, summarize
+from jevany.checkpoint import LoadOptions
+from jevany.data import api_request
+from jevany.letter_predictor import LetterReadoutPredictor
+from jevany.suite import (
+    ENCODING,
+    digest,
+    load_split,
+    record_digest,
+    write_json,
+)
+
+
+def _typed_rows(dataset: Path, split: str) -> list[dict]:
+    path = dataset / "all" / f"{split}-00000-of-00001.parquet"
+    return pq.read_table(path).to_pylist()
+
+
+def _typed_record(row: dict) -> dict:
+    state = json.loads(row["state"])
+    questions = json.loads(row["questions"])
+    gold = json.loads(row["gold"])
+    converted = {}
+    for qid, question in questions.items():
+        answer = gold[qid]
+        value = dict(question)
+        keys = question_keys(value["type"], value.get("criteria"))
+        # Typed Decisions accuracy is ordered argmax agreement with the soft
+        # teacher distribution.  The dataset's convenience `label` differs on
+        # tied rows, so derive the target exactly as the benchmark scorer does.
+        label_key = max(keys, key=lambda key: float(answer["probabilities"][key]))
+        if value["type"] == "choice":
+            value["label"] = label_key
+        elif value["type"] == "noul":
+            value["label"] = label_key == "true"
+        else:
+            value["label"] = int(label_key)
+        value["target"] = answer["probabilities"]
+        value["src"] = f"typed_decisions/{row['workflow']}/{qid}"
+        converted[qid] = value
+    return {
+        "state": state,
+        "questions": converted,
+        "_meta": {
+            "id": row["id"],
+            "group_id": row["id"],
+            "source": "LocalLLaMA/typed-decisions",
+            "variant": "clean",
+            "split": row["split"],
+            "workflow": row["workflow"],
+        },
+    }
+
+
+def _attach_soft_gold(rows: list[dict], record: dict) -> None:
+    questions = record["questions"]
+    for row in rows:
+        target = questions[row["question"]].get("target")
+        if target is not None:
+            row["gold"] = [float(target[key]) for key in row["keys"]]
+
+
+def _component_prediction(probabilities: dict) -> dict:
+    return {"probabilities": probabilities}
+
+
+def _typed_soft_metrics(rows: list[dict], bins: int = 15) -> dict:
+    totals = [0] * bins
+    correct = [0] * bins
+    confidence = [0.0] * bins
+    kl, brier = 0.0, 0.0
+    for row in rows:
+        p = [float(value) for value in row["p"]]
+        gold = [float(value) for value in row["gold"]]
+        p_total, gold_total = sum(p), sum(gold)
+        p = [value / p_total for value in p]
+        gold = [value / gold_total for value in gold]
+        kl += sum(g * math.log(max(g, 1e-12) / max(value, 1e-12))
+                  for g, value in zip(gold, p, strict=True))
+        brier += sum((value - g) ** 2 for value, g in zip(p, gold, strict=True))
+        predicted = max(range(len(p)), key=p.__getitem__)
+        conf = max(p)
+        bucket = min(bins - 1, int(conf * bins))
+        totals[bucket] += 1
+        correct[bucket] += int(predicted == row["label"])
+        confidence[bucket] += conf
+    n = len(rows)
+    return {
+        "n": n,
+        "correct": sum(correct),
+        "accuracy": sum(correct) / n,
+        "kl_from_gold": kl / n,
+        "brier": brier / n,
+        "ece": sum(
+            totals[index] / n * abs(
+                correct[index] / totals[index] - confidence[index] / totals[index]
+            )
+            for index in range(bins) if totals[index]
+        ),
+        "mean_confidence": sum(confidence) / n,
+    }
+
+
+def evaluate(name: str, records: list[dict], predictor, destination: Path) -> dict:
+    destination.mkdir(parents=True, exist_ok=False)
+    component_rows: dict[str, list[dict]] = defaultdict(list)
+    latencies = []
+    started = time.time()
+    with (destination / "predictions.jsonl").open("w", encoding=ENCODING) as stream:
+        for index, record in enumerate(records, 1):
+            result = predictor(record)
+            components = result.get("component_probabilities") or {
+                "choice": result["probabilities"]
+            }
+            saved = {}
+            for component, probabilities in components.items():
+                if probabilities is None:
+                    continue
+                rows = prediction_rows(record, _component_prediction(probabilities))
+                _attach_soft_gold(rows, record)
+                component_rows[component].extend(rows)
+                saved[component] = probabilities
+            stream.write(json.dumps({
+                "id": record["_meta"]["id"],
+                "request_sha256": record_digest(api_request(record)),
+                "component_probabilities": saved,
+                "latency_ms": result["latency_ms"],
+                "input_tokens": result["input_tokens"],
+            }, allow_nan=False) + "\n")
+            stream.flush()
+            latencies.append(float(result["latency_ms"]))
+            if index % 25 == 0:
+                print(f"{name}: {index}/{len(records)}", flush=True)
+    reports = {}
+    for component, rows in component_rows.items():
+        write_json(destination / f"{component}-rows.json", rows)
+        reports[component] = summarize(rows)
+        if name == "typed_test":
+            reports[component]["typed_soft_metrics"] = _typed_soft_metrics(rows)
+    report = {
+        "dataset": name,
+        "records": len(records),
+        "questions": sum(len(record["questions"]) for record in records),
+        "components": reports,
+        "components_executed": sorted(component_rows),
+        "latency_scope": (
+            "end-to-end predictor call; checkpoint calls execute both choice and native "
+            "components, so these values are not per-component efficiency measurements"
+        ),
+        "latency_ms": {
+            "median": statistics.median(latencies),
+            "p95": sorted(latencies)[min(len(latencies) - 1, int(.95 * len(latencies)))],
+        },
+        "wall_seconds": time.time() - started,
+    }
+    write_json(destination / "report.json", report)
+    return report
+
+
+def _correct(report: dict, component: str) -> int:
+    clean = report["components"][component]["clean"]
+    return round(clean["n"] * clean["acc"])
+
+
+def _code_revision() -> str | None:
+    root = Path(__file__).resolve().parents[1]
+    result = subprocess.run(
+        ["git", "rev-parse", "HEAD"], cwd=root, text=True,
+        stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, check=False,
+    )
+    return result.stdout.strip() if result.returncode == 0 else None
+
+
+def _checkpoint_artifacts(predictor) -> dict | None:
+    checkpoint = predictor.checkpoint
+    if checkpoint is None:
+        return None
+    root = Path(checkpoint.path)
+    files = {}
+    for name in ("adapter_model.safetensors", "head.pt", "adapter_config.json"):
+        path = root / name
+        if path.is_file():
+            files[name] = {"sha256": digest(path), "bytes": path.stat().st_size}
+    return {"requested": checkpoint.requested, "resolved": str(root), "files": files}
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    source = parser.add_mutually_exclusive_group(required=True)
+    source.add_argument("--base")
+    source.add_argument("--checkpoint")
+    parser.add_argument("--base-load-path", required=True)
+    parser.add_argument("--revision")
+    parser.add_argument("--model-name", required=True)
+    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--jevbench", type=Path, required=True)
+    parser.add_argument("--transfer", type=Path, required=True)
+    parser.add_argument("--typed", type=Path, required=True)
+    parser.add_argument("--jevjudge", type=Path, required=True)
+    parser.add_argument("--expected-jevbench-native-correct", type=int)
+    parser.add_argument("--expected-transfer-native-correct", type=int)
+    parser.add_argument("--device", default="cuda")
+    parser.add_argument("--dtype", choices=("fp32", "bf16"), default="bf16")
+    parser.add_argument("--attn", choices=("eager", "sdpa"), default="sdpa")
+    parser.add_argument("--max-tokens", type=int, default=65_536)
+    args = parser.parse_args()
+    if args.revision and args.checkpoint:
+        parser.error("--revision accompanies --base, not --checkpoint")
+    if args.base and (args.expected_jevbench_native_correct is not None
+                      or args.expected_transfer_native_correct is not None):
+        parser.error("native parity assertions require --checkpoint")
+
+    args.out.mkdir(parents=True, exist_ok=False)
+    options = LoadOptions(
+        dtype={"fp32": torch.float32, "bf16": torch.bfloat16}[args.dtype],
+        merge=False,
+        temperature=None,
+        base_load_path=args.base_load_path,
+        attn=args.attn,
+    )
+    predictor = LetterReadoutPredictor(
+        base=args.base,
+        checkpoint=args.checkpoint,
+        revision=args.revision,
+        device=args.device,
+        options=options,
+        temperature=1.0,
+        native_weight=0.5 if args.checkpoint else 0.0,
+        return_components=bool(args.checkpoint),
+        exact_kernels=True,
+        max_tokens=args.max_tokens,
+    )
+
+    parity_datasets = {
+        "jevbench_public": load_split(args.jevbench, "development"),
+        "transfer_calibration": load_split(args.transfer, "development"),
+    }
+    reports = {
+        name: evaluate(name, records, predictor, args.out / name)
+        for name, records in parity_datasets.items()
+    }
+    if args.checkpoint:
+        checks = (
+            ("jevbench_public", args.expected_jevbench_native_correct),
+            ("transfer_calibration", args.expected_transfer_native_correct),
+        )
+        for dataset, expected in checks:
+            if expected is not None:
+                actual = _correct(reports[dataset], "native")
+                if actual != expected:
+                    raise RuntimeError(
+                        f"{dataset} native parity failed: expected {expected}, got {actual}"
+                    )
+    heldout_datasets = {
+        "transfer_test": load_split(args.transfer, "test", allow_test=True),
+        "typed_test": [_typed_record(row) for row in _typed_rows(args.typed, "test")],
+        "jevjudge_text": [
+            record for record in load_split(args.jevjudge, "test", allow_test=True)
+            if record["_meta"]["modality"] == "text"
+        ],
+    }
+    reports.update({
+        name: evaluate(name, records, predictor, args.out / name)
+        for name, records in heldout_datasets.items()
+    })
+
+    typed_manifest = json.loads((args.typed / ".hf-fetch.json").read_text())
+    manifest = {
+        "model": args.model_name,
+        "source": {"base": args.base, "checkpoint": args.checkpoint},
+        "checkpoint_artifacts": _checkpoint_artifacts(predictor),
+        "base_loading": predictor.base_loading,
+        "predictor": predictor.provenance,
+        "runtime": {
+            "python": platform.python_version(),
+            "torch": torch.__version__,
+            "cuda": torch.version.cuda,
+            "transformers": transformers.__version__,
+            "peft": peft.__version__,
+            "numpy": np.__version__,
+            "pyarrow": pyarrow.__version__,
+            "safetensors": safetensors.__version__,
+            "code_revision": _code_revision(),
+        },
+        "protocol": {
+            "kernel_policy": "LocalPredictor-compatible exact CUDA policy",
+            "dtype": args.dtype,
+            "attn": args.attn,
+            "temperature": 1.0,
+            "blend_selection": (
+                "not performed here; components are saved for fitting on Transfer-v9 "
+                "development only"
+            ),
+            "efficiency_scope": (
+                "base calls execute choice only; checkpoint calls execute choice and native. "
+                "Do not compare component efficiency from these end-to-end timings"
+            ),
+        },
+        "datasets": {
+            "jevbench_development_sha256": digest(args.jevbench / "development.jsonl"),
+            "jevbench_manifest_sha256": digest(args.jevbench / "manifest.json"),
+            "transfer_manifest_sha256": digest(args.transfer / "manifest.json"),
+            "transfer_development_sha256": digest(args.transfer / "development.jsonl"),
+            "transfer_test_sha256": digest(args.transfer / "test.jsonl"),
+            "typed_revision": typed_manifest["resolved_revision"],
+            "typed_test_sha256": digest(
+                args.typed / "all" / "test-00000-of-00001.parquet"
+            ),
+            "jevjudge_test_sha256": digest(args.jevjudge / "test.jsonl"),
+            "jevjudge_manifest_sha256": digest(args.jevjudge / "manifest.json"),
+        },
+        "reports": reports,
+    }
+    write_json(args.out / "manifest.json", manifest)
+    print(json.dumps({
+        "model": args.model_name,
+        "results": {
+            name: {
+                component: report["clean"]["acc"]
+                for component, report in value["components"].items()
+            }
+            for name, value in reports.items()
+        },
+    }, indent=2), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/fit_choice_ensemble.py b/scripts/fit_choice_ensemble.py
new file mode 100644
index 0000000..c4ae147
--- /dev/null
+++ b/scripts/fit_choice_ensemble.py
@@ -0,0 +1,298 @@
+#!/usr/bin/env python3
+"""Fit choice/native pooling on Transfer-v9 development and score frozen tests.
+
+The blend weight and temperature are selected only from the deterministic
+Transfer-v9 development partition emitted by ``evaluate_choice_readout_matrix``.
+Those fixed values are then applied unchanged to every held-out dataset.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+
+import numpy as np
+
+from jevany.suite import read_json, write_json
+
+
+EPS = 1e-12
+
+
+def load_components(directory: Path) -> tuple[list[dict], list[dict] | None]:
+    choice = read_json(directory / "choice-rows.json")
+    native_path = directory / "native-rows.json"
+    native = read_json(native_path) if native_path.exists() else None
+    if native is not None:
+        left = [(row["id"], row["question"], row["keys"], row["label"]) for row in choice]
+        right = [(row["id"], row["question"], row["keys"], row["label"]) for row in native]
+        if left != right:
+            raise ValueError(f"component rows are not aligned: {directory}")
+    return choice, native
+
+
+def headline_indices(rows: list[dict]) -> list[int]:
+    """Indices in the benchmark's accuracy cohort.
+
+    Transfer-v9 also contains non-clean robustness variants and unknowable
+    questions.  The benchmark never counts either in headline accuracy, so
+    neither may influence weight or temperature selection.
+    """
+
+    return [
+        index for index, row in enumerate(rows)
+        if row.get("variant", "clean") == "clean"
+        and row.get("source") != "unknowable"
+    ]
+
+
+def select_indices(rows: list[dict], indices: list[int]) -> list[dict]:
+    return [rows[index] for index in indices]
+
+
+def targets(rows: list[dict]) -> list[np.ndarray]:
+    values = []
+    for row in rows:
+        if "gold" in row:
+            target = np.asarray(row["gold"], dtype=np.float64)
+            target /= target.sum()
+        else:
+            target = np.zeros(len(row["p"]), dtype=np.float64)
+            target[row["label"]] = 1.0
+        values.append(target)
+    return values
+
+
+def pooled_logits(choice: list[dict], native: list[dict] | None, weight: float) -> list[np.ndarray]:
+    if native is None and weight:
+        raise ValueError("native weight requires native component rows")
+    result = []
+    for index, row in enumerate(choice):
+        left = np.log(np.maximum(np.asarray(row["p"], dtype=np.float64), EPS))
+        if native is None:
+            result.append(left)
+        else:
+            right = np.log(np.maximum(np.asarray(native[index]["p"], dtype=np.float64), EPS))
+            result.append((1 - weight) * left + weight * right)
+    return result
+
+
+def probabilities(logits: list[np.ndarray], temperature: float) -> list[np.ndarray]:
+    result = []
+    for values in logits:
+        scaled = values / temperature
+        scaled -= scaled.max()
+        p = np.exp(scaled)
+        result.append(p / p.sum())
+    return result
+
+
+def soft_nll(rows: list[dict], ps: list[np.ndarray]) -> float:
+    gold = targets(rows)
+    return float(np.mean([
+        -float(np.sum(target * np.log(np.maximum(p, EPS))))
+        for target, p in zip(gold, ps, strict=True)
+    ]))
+
+
+def fit_temperature(rows: list[dict], logits: list[np.ndarray]) -> tuple[float, float]:
+    """Golden-section search in log-temperature space with fixed bounds."""
+
+    lo, hi = math.log(.05), math.log(20.0)
+    ratio = (math.sqrt(5) - 1) / 2
+    x1, x2 = hi - ratio * (hi - lo), lo + ratio * (hi - lo)
+
+    def objective(log_temperature: float) -> float:
+        return soft_nll(rows, probabilities(logits, math.exp(log_temperature)))
+
+    f1, f2 = objective(x1), objective(x2)
+    for _ in range(80):
+        if f1 <= f2:
+            hi, x2, f2 = x2, x1, f1
+            x1 = hi - ratio * (hi - lo)
+            f1 = objective(x1)
+        else:
+            lo, x1, f1 = x1, x2, f2
+            x2 = lo + ratio * (hi - lo)
+            f2 = objective(x2)
+    point = (lo + hi) / 2
+    return math.exp(point), objective(point)
+
+
+def fit(rows: list[dict], choice: list[dict], native: list[dict] | None) -> dict:
+    candidates = [0.0] if native is None else [index / 100 for index in range(101)]
+    best = None
+    trace = []
+    for weight in candidates:
+        logits = pooled_logits(choice, native, weight)
+        temperature, nll = fit_temperature(rows, logits)
+        t1 = probabilities(logits, 1.0)
+        correct = sum(
+            int(int(p.argmax()) == row["label"])
+            for row, p in zip(rows, t1, strict=True)
+        )
+        item = {
+            "native_weight": weight,
+            "temperature": temperature,
+            "correct": correct,
+            "accuracy": correct / len(rows),
+            "nll": nll,
+        }
+        trace.append(item)
+        if best is None or (
+            -correct, nll, abs(weight - .5)
+        ) < (
+            -best["correct"], best["nll"], abs(best["native_weight"] - .5)
+        ):
+            best = item
+    return {"selected": best, "grid": trace}
+
+
+def ece(rows: list[dict], ps: list[np.ndarray], bins: int = 15) -> float:
+    totals = np.zeros(bins)
+    correct = np.zeros(bins)
+    confidence = np.zeros(bins)
+    for row, p in zip(rows, ps, strict=True):
+        conf = float(p.max())
+        bucket = min(bins - 1, int(conf * bins))
+        totals[bucket] += 1
+        correct[bucket] += int(int(p.argmax()) == row["label"])
+        confidence[bucket] += conf
+    n = len(rows)
+    return float(sum(
+        totals[index] / n * abs(correct[index] / totals[index] - confidence[index] / totals[index])
+        for index in range(bins) if totals[index]
+    ))
+
+
+def metrics(rows: list[dict], ps: list[np.ndarray], *, ece_bins: int = 10) -> dict:
+    gold = targets(rows)
+    cross_entropy = soft_nll(rows, ps)
+    gold_entropy = float(np.mean([
+        -float(np.sum(target * np.log(np.maximum(target, EPS)))) for target in gold
+    ]))
+    return {
+        "n": len(rows),
+        "correct": sum(int(int(p.argmax()) == row["label"]) for row, p in zip(rows, ps, strict=True)),
+        "accuracy": float(np.mean([
+            int(int(p.argmax()) == row["label"])
+            for row, p in zip(rows, ps, strict=True)
+        ])),
+        "cross_entropy_from_gold": cross_entropy,
+        "gold_entropy": gold_entropy,
+        "kl_from_gold": cross_entropy - gold_entropy,
+        "brier": float(np.mean([
+            float(np.sum((p - target) ** 2))
+            for target, p in zip(gold, ps, strict=True)
+        ])),
+        "ece": ece(rows, ps, bins=ece_bins),
+        "mean_confidence": float(np.mean([p.max() for p in ps])),
+    }
+
+
+def evaluate(directory: Path, selected: dict) -> dict:
+    choice, native = load_components(directory)
+    configurations = {
+        "choice_t1": (0.0, 1.0),
+        "transfer_dev_calibrated_choice": (0.0, selected["choice_temperature"]),
+    }
+    if native is not None:
+        configurations.update({
+            "native_shipped": (1.0, 1.0),
+            "fixed_blend_0_5": (.5, 1.0),
+            "transfer_dev_tuned_blend": (
+                selected["native_weight"], selected["blend_temperature"]
+            ),
+        })
+    indices = headline_indices(choice)
+    ece_bins = 15 if directory.name == "typed_test" else 10
+    results = {}
+    for name, (weight, temperature) in configurations.items():
+        ps = probabilities(pooled_logits(choice, native, weight), temperature)
+        headline = metrics(
+            select_indices(choice, indices),
+            [ps[index] for index in indices],
+            ece_bins=ece_bins,
+        )
+        headline["cohort"] = "clean and knowable"
+        headline["excluded_non_headline_rows"] = len(choice) - len(indices)
+        headline["ece_bins"] = ece_bins
+        results[name] = headline
+    return results
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--run", type=Path, required=True)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    manifest = read_json(args.run / "manifest.json")
+    calibration_dir = args.run / "transfer_calibration"
+    choice, native = load_components(calibration_dir)
+    indices = headline_indices(choice)
+    calibration_choice = select_indices(choice, indices)
+    calibration_native = select_indices(native, indices) if native is not None else None
+    choice_fit = fit(calibration_choice, calibration_choice, None)
+    blend_fit = fit(calibration_choice, calibration_choice, calibration_native)
+    selected = {
+        "choice_temperature": choice_fit["selected"]["temperature"],
+        "native_weight": blend_fit["selected"]["native_weight"],
+        "blend_temperature": blend_fit["selected"]["temperature"],
+        "selection_objective": (
+            "hard-label accuracy on the Transfer-v9 development clean/knowable "
+            "cohort; calibrated NLL and distance from 0.5 break ties"
+        ),
+    }
+    datasets = {}
+    for name in (
+        "transfer_calibration", "transfer_test", "typed_test", "jevbench_public",
+        "jevjudge_text"
+    ):
+        datasets[name] = evaluate(args.run / name, selected)
+    result = {
+        "model": manifest["model"],
+        "source_manifest": str(args.run / "manifest.json"),
+        "protocol": {
+            "selection_split": (
+                "Transfer-v9 development clean/knowable accuracy cohort; never any "
+                "reported test panel"
+            ),
+            "selection_rows": len(calibration_choice),
+            "weight_grid": "0.00 to 1.00 inclusive, step 0.01",
+            "pool": "log-linear/geometric",
+            "temperature_fit": "bounded scalar NLL minimization, T in [0.05, 20]",
+            "ece": "15 equal-width bins for Typed Decisions; 10 for every other dataset",
+            "selection_objective": "accuracy; calibrated NLL then distance from 0.5 break ties",
+            "transfer": "the selected weight and temperature are frozen for every held-out dataset",
+            "native_shipped": (
+                "the checkpoint's native probabilities, including its shipped inference "
+                "temperature; no additional temperature is applied"
+            ),
+            "fixed_blend_0_5": (
+                "equal log-linear pooling of T=1 choice probabilities and shipped native "
+                "probabilities; no additional temperature is applied"
+            ),
+            "transfer_dev_tuned_blend": (
+                "the Transfer-dev-selected native weight followed by the reported scalar "
+                "additional temperature"
+            ),
+        },
+        "selected": selected,
+        "selection": {"choice": choice_fit, "blend": blend_fit},
+        "datasets": datasets,
+    }
+    write_json(args.out, result)
+    print(json.dumps({
+        "model": result["model"],
+        "selected": selected,
+        "test_accuracy": {
+            dataset: {name: values["accuracy"] for name, values in scores.items()}
+            for dataset, scores in datasets.items() if dataset != "transfer_calibration"
+        },
+    }, indent=2), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_evaluate_choice_readout_matrix.py b/tests/test_evaluate_choice_readout_matrix.py
new file mode 100644
index 0000000..a600a21
--- /dev/null
+++ b/tests/test_evaluate_choice_readout_matrix.py
@@ -0,0 +1,129 @@
+import json
+import math
+
+import pytest
+
+from scripts.evaluate_choice_readout_matrix import (
+    _attach_soft_gold,
+    _typed_record,
+    _typed_soft_metrics,
+    evaluate,
+)
+
+
+def test_typed_record_uses_ordered_argmax_when_soft_gold_is_tied():
+    row = {
+        "id": "tie-case",
+        "split": "test",
+        "workflow": "unit",
+        "state": json.dumps({"context": "A tied teacher distribution"}),
+        "questions": json.dumps({
+            "decision": {
+                "type": "choice",
+                "criteria": {"first": "First option", "second": "Second option"},
+            }
+        }),
+        # The convenience label intentionally disagrees with the benchmark's
+        # ordered argmax rule and must not determine the converted target.
+        "gold": json.dumps({
+            "decision": {
+                "label": "second",
+                "probabilities": {"first": 0.5, "second": 0.5},
+            }
+        }),
+    }
+
+    record = _typed_record(row)
+
+    assert record["questions"]["decision"]["label"] == "first"
+    assert record["questions"]["decision"]["target"] == {
+        "first": 0.5,
+        "second": 0.5,
+    }
+
+
+def test_typed_soft_metrics_normalize_distributions_and_use_hard_labels():
+    rows = [
+        {"p": [8.0, 2.0], "gold": [3.0, 1.0], "label": 0},
+        {"p": [4.0, 6.0], "gold": [1.0, 1.0], "label": 0},
+    ]
+
+    result = _typed_soft_metrics(rows, bins=2)
+
+    expected_kl = (
+        0.75 * math.log(0.75 / 0.8)
+        + 0.25 * math.log(0.25 / 0.2)
+        + 0.5 * math.log(0.5 / 0.4)
+        + 0.5 * math.log(0.5 / 0.6)
+    ) / 2
+    assert result["n"] == 2
+    assert result["correct"] == 1
+    assert result["accuracy"] == pytest.approx(0.5)
+    assert result["kl_from_gold"] == pytest.approx(expected_kl)
+    assert result["brier"] == pytest.approx(0.0125)
+    assert result["ece"] == pytest.approx(0.2)
+    assert result["mean_confidence"] == pytest.approx(0.7)
+
+
+def test_attach_soft_gold_aligns_values_to_each_prediction_rows_key_order():
+    record = {
+        "questions": {
+            "choice": {"target": {"alpha": 0.1, "beta": 0.9}},
+            "score": {"target": {"0": 0.2, "1": 0.3, "2": 0.5}},
+        }
+    }
+    rows = [
+        {"question": "choice", "keys": ["beta", "alpha"]},
+        {"question": "score", "keys": ["2", "0", "1"]},
+    ]
+
+    _attach_soft_gold(rows, record)
+
+    assert rows[0]["gold"] == [0.9, 0.1]
+    assert rows[1]["gold"] == [0.5, 0.2, 0.3]
+
+
+def test_evaluate_base_only_result_saves_choice_component(tmp_path):
+    record = {
+        "state": "Choose the right option.",
+        "questions": {
+            "decision": {
+                "type": "choice",
+                "criteria": {"wrong": "Wrong", "right": "Right"},
+                "label": "right",
+                "target": {"right": 0.8, "wrong": 0.2},
+                "src": "unit/base-only",
+            }
+        },
+        "_meta": {
+            "id": "base-only",
+            "group_id": "base-only",
+            "source": "unit",
+            "variant": "clean",
+        },
+    }
+
+    def predictor(_record):
+        return {
+            # Reverse insertion order relative to criteria to exercise keyed
+            # probability alignment in the full evaluation path.
+            "probabilities": {"decision": {"right": 0.9, "wrong": 0.1}},
+            "latency_ms": 2.0,
+            "input_tokens": 12,
+        }
+
+    destination = tmp_path / "base"
+    report = evaluate("typed_test", [record], predictor, destination)
+
+    assert set(report["components"]) == {"choice"}
+    assert report["components"]["choice"]["clean"]["acc"] == pytest.approx(1.0)
+    assert not (destination / "native-rows.json").exists()
+    rows = json.loads((destination / "choice-rows.json").read_text())
+    assert rows[0]["keys"] == ["wrong", "right"]
+    assert rows[0]["p"] == pytest.approx([0.1, 0.9])
+    assert rows[0]["gold"] == pytest.approx([0.2, 0.8])
+    prediction = json.loads(
+        (destination / "predictions.jsonl").read_text().strip()
+    )
+    assert set(prediction["component_probabilities"]) == {"choice"}
+
diff --git a/tests/test_fit_choice_ensemble.py b/tests/test_fit_choice_ensemble.py
new file mode 100644
index 0000000..4e54d9a
--- /dev/null
+++ b/tests/test_fit_choice_ensemble.py
@@ -0,0 +1,65 @@
+import pytest
+
+from scripts.fit_choice_ensemble import (
+    fit,
+    headline_indices,
+    metrics,
+    pooled_logits,
+    probabilities,
+)
+
+
+def row(identity, p, label, gold=None):
+    value = {
+        "id": identity,
+        "question": "decision",
+        "keys": ["left", "right"],
+        "label": label,
+        "p": p,
+    }
+    if gold is not None:
+        value["gold"] = gold
+    return value
+
+
+def test_fit_selects_native_weight_on_calibration_accuracy_before_nll():
+    choice = [row("a", [.9, .1], 1), row("b", [.8, .2], 1)]
+    native = [row("a", [.1, .9], 1), row("b", [.2, .8], 1)]
+
+    result = fit(choice, choice, native)
+
+    assert result["selected"]["correct"] == 2
+    assert result["selected"]["native_weight"] > .5
+    assert len(result["grid"]) == 101
+
+
+def test_soft_gold_metrics_use_ordered_labels_and_report_kl():
+    rows = [row("a", [.75, .25], 0, gold=[.5, .5])]
+    ps = probabilities(pooled_logits(rows, None, 0), 1.0)
+
+    result = metrics(rows, ps)
+
+    assert result["accuracy"] == 1.0
+    assert result["brier"] == pytest.approx(.125)
+    assert result["kl_from_gold"] == pytest.approx(
+        result["cross_entropy_from_gold"] - result["gold_entropy"]
+    )
+
+
+def test_choice_only_fit_has_no_native_weight():
+    rows = [row("a", [.7, .3], 0), row("b", [.4, .6], 1)]
+
+    result = fit(rows, rows, None)
+
+    assert result["selected"]["native_weight"] == 0.0
+    assert len(result["grid"]) == 1
+
+
+def test_transfer_selection_uses_only_clean_knowable_headline_rows():
+    rows = [
+        {**row("headline", [.7, .3], 0), "variant": "clean", "source": "task"},
+        {**row("variant", [.7, .3], 0), "variant": "permuted", "source": "task"},
+        {**row("unknown", [.7, .3], 0), "variant": "clean", "source": "unknowable"},
+    ]
+
+    assert headline_indices(rows) == [0]
diff --git a/tests/test_letter_predictor.py b/tests/test_letter_predictor.py
index 85cb3d3..ca33be9 100644
--- a/tests/test_letter_predictor.py
+++ b/tests/test_letter_predictor.py
@@ -26,6 +26,21 @@ def test_letter_predictor_rejects_native_only_load_options_before_loading():
         )
 
 
+def test_letter_predictor_validates_generalized_native_arguments_before_loading():
+    with pytest.raises(ValueError, match="at most one"):
+        LetterReadoutPredictor(
+            checkpoint="unused", device="cpu", native_weight=0.5, pointer_weight=0.5,
+        )
+    with pytest.raises(ValueError, match="requires a JevAny checkpoint"):
+        LetterReadoutPredictor(base="unused", device="cpu", native_weight=0.5)
+    with pytest.raises(TypeError, match="return_components"):
+        LetterReadoutPredictor(checkpoint="unused", device="cpu", return_components=1)
+    with pytest.raises(TypeError, match="exact_kernels"):
+        LetterReadoutPredictor(checkpoint="unused", device="cpu", exact_kernels=1)
+    with pytest.raises(ValueError, match="pointer weight"):
+        LetterReadoutPredictor(checkpoint="unused", device="cpu", native_weight=1.01)
+
+
 def test_one_row_input_ids_normalizes_template_shapes_and_mappings():
     assert _one_row_input_ids(torch.tensor([1, 2])).tolist() == [[1, 2]]
     assert _one_row_input_ids({"input_ids": [[3, 4]]}).tolist() == [[3, 4]]
@@ -113,6 +128,56 @@ def test_release_unused_output_head_drops_lm_token_aliases():
     assert _release_unused_output_head(direct_token) is False
 
 
+def test_release_unused_output_head_retains_direct_token_native_head():
+    head = object()
+    direct_token = type("Model", (), {
+        "adapter": type("Adapter", (), {"_output_embeddings": head})(),
+        "lm_head": head,
+    })()
+
+    assert _release_unused_output_head(direct_token, retain_native=True) is False
+    assert direct_token.lm_head is head
+    assert direct_token.adapter._output_embeddings is head
+
+
+def test_return_components_exposes_unblended_choice_and_native_probabilities():
+    predictor = object.__new__(LetterReadoutPredictor)
+    predictor.device = "cpu"
+    predictor.temperature = 1.0
+    predictor.native_weight = 0.5
+    predictor.pointer_weight = 0.5
+    predictor.return_components = True
+    predictor._needs_native = True
+    predictor.native_decision_mode = "lm_token"
+    predictor.provenance = {
+        "method": "exact option-letter choice projection",
+        "adapter_applied": True,
+    }
+    predictor._letter_question = lambda state, question: (
+        ["left", "right"], [0.8, 0.2], [4.0, 1.0], 11,
+    )
+    predictor._native = lambda record: ([[0.2, 0.8]], 7)
+    record = {
+        "state": "state",
+        "questions": {"q": {
+            "type": "choice",
+            "instructions": "choose",
+            "criteria": {"left": "Left", "right": "Right"},
+            "label": "left",
+        }},
+    }
+
+    result = predictor(record)
+
+    assert result["component_probabilities"] == {
+        "choice": {"q": {"left": 0.8, "right": 0.2}},
+        "native": {"q": {"left": 0.2, "right": 0.8}},
+    }
+    assert result["probabilities"]["q"] == pytest.approx({"left": 0.5, "right": 0.5})
+    assert result["input_tokens"] == 18
+    assert result["readout"]["native_decision_mode"] == "lm_token"
+
+
 def test_question_options_preserve_choice_and_score_order_and_fix_noul_order():
     assert question_options({
         "type": "choice",
diff --git a/tests/test_letter_readout.py b/tests/test_letter_readout.py
index 9553ee9..c3dd823 100644
--- a/tests/test_letter_readout.py
+++ b/tests/test_letter_readout.py
@@ -4,6 +4,8 @@
 import pytest
 
 from jevany.letter_readout import (
+    CHOICE_SYMBOLS,
+    CYGNET_LETTERS,
     LETTERS,
     SYSTEM_PROMPT,
     build_prompt,
@@ -45,21 +47,26 @@ def test_build_prompt_matches_cygnet_structured_rendering_and_option_order():
         "A. Refund the card",
         "B. Issue store credit",
         "",
-        "Answer with the letter of exactly one option, and nothing else:",
+        "Answer with the case-sensitive ID of exactly one option, and nothing else:",
     ])
     assert prompt == expected
     assert prompt.index("A. Refund") < prompt.index("B. Issue")
     assert "thinking" not in prompt.lower()
-    assert "LETTER and nothing else" in SYSTEM_PROMPT
+    assert "case-sensitive ID and nothing else" in SYSTEM_PROMPT
 
 
-def test_prompt_accepts_all_letters_and_rejects_invalid_option_counts():
-    prompt = build_prompt("state", "choose", [f"option {index}" for index in range(26)])
-    assert f"{LETTERS[-1]}. option 25" in prompt
+def test_prompt_preserves_cygnet_az_then_extends_to_lowercase_ids():
+    prompt = build_prompt(
+        "state", "choose", [f"option {index}" for index in range(len(CHOICE_SYMBOLS))]
+    )
+    assert LETTERS == CHOICE_SYMBOLS
+    assert f"{CYGNET_LETTERS[-1]}. option 25" in prompt
+    assert f"{CHOICE_SYMBOLS[26]}. option 26" in prompt
+    assert f"{CHOICE_SYMBOLS[-1]}. option 51" in prompt
     with pytest.raises(ValueError, match="at least one"):
         build_prompt("state", "choose", [])
-    with pytest.raises(ValueError, match="at most 26"):
-        build_prompt("state", "choose", list(range(27)))
+    with pytest.raises(ValueError, match="at most 52"):
+        build_prompt("state", "choose", list(range(53)))
     with pytest.raises(TypeError, match="ordered mapping"):
         build_prompt("state", "choose", "not an option sequence")
 
@@ -161,8 +168,8 @@ def test_invalid_probability_distributions_are_rejected(probabilities):
 
 def test_invalid_letters_token_maps_and_logits_are_rejected():
     tokenizer = FakeTokenizer(["A", "not B"])
-    with pytest.raises(ValueError, match="only single uppercase"):
-        letter_token_ids(tokenizer, ("A", "b"))
+    with pytest.raises(ValueError, match="single A-Z/a-z"):
+        letter_token_ids(tokenizer, ("A", "1"))
     with pytest.raises(ValueError, match="duplicates"):
         letter_token_ids(tokenizer, ("A", "A"))
     with pytest.raises(ValueError, match="no one-token aliases for: B"):

From ae891ed97147b57b41a78a4bb4960b018db3bf42 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 00:49:09 +0000
Subject: [PATCH 02/10] Handle long-context choice evaluation and resume runs

---
 jevany/letter_predictor.py                   | 62 +++++++++++--
 scripts/evaluate_choice_readout_matrix.py    | 96 +++++++++++++++++++-
 tests/test_evaluate_choice_readout_matrix.py | 31 +++++++
 tests/test_letter_predictor.py               | 14 +++
 4 files changed, 191 insertions(+), 12 deletions(-)

diff --git a/jevany/letter_predictor.py b/jevany/letter_predictor.py
index e0856f9..1459d74 100644
--- a/jevany/letter_predictor.py
+++ b/jevany/letter_predictor.py
@@ -9,6 +9,7 @@
 from __future__ import annotations
 
 from collections.abc import Mapping
+from contextlib import nullcontext
 import hashlib
 import json
 import math
@@ -17,6 +18,7 @@
 
 import torch
 import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
 
 from .api import SystemOneRequest, question_keys
 from .checkpoint import Checkpoint, LoadOptions
@@ -244,6 +246,7 @@ def __init__(
         native_weight: float | None = None,
         return_components: bool = False,
         exact_kernels: bool = True,
+        efficient_long_context_tokens: int | None = None,
         max_tokens: int = 16_384,
     ) -> None:
         if (base is None) == (checkpoint is None):
@@ -278,7 +281,16 @@ def __init__(
             raise TypeError("exact_kernels must be a boolean")
         self.return_components = return_components
         self.exact_kernels = exact_kernels
-        self.exact_cuda_kernels_applied = device == "cuda" and exact_kernels
+        self.exact_cuda_kernels_applied = str(device).startswith("cuda") and exact_kernels
+        if (
+            efficient_long_context_tokens is not None
+            and (
+                type(efficient_long_context_tokens) is not int
+                or efficient_long_context_tokens < 2
+            )
+        ):
+            raise ValueError("efficient_long_context_tokens must be an integer >= 2 or None")
+        self.efficient_long_context_tokens = efficient_long_context_tokens
         if type(max_tokens) is not int or max_tokens < 2:
             raise ValueError("max_tokens must be an integer >= 2")
         self.max_tokens = max_tokens
@@ -442,6 +454,13 @@ def __init__(
             "return_components": self.return_components,
             "exact_kernels": self.exact_kernels,
             "exact_cuda_kernels_applied": self.exact_cuda_kernels_applied,
+            "efficient_long_context_tokens": self.efficient_long_context_tokens,
+            "long_context_attention": (
+                "fused SDPA (Flash/Efficient, no math fallback) at or above the "
+                "recorded threshold; exact math SDPA below it"
+                if self.efficient_long_context_tokens is not None
+                else None
+            ),
             "max_tokens": self.max_tokens,
             "backbone_context_window": self.backbone_context_window,
             "effective_max_tokens": self.effective_max_tokens,
@@ -449,6 +468,26 @@ def __init__(
             "device": device,
         }
 
+    def _uses_efficient_attention(self, token_count: int) -> bool:
+        return (
+            str(self.device).startswith("cuda")
+            and getattr(self, "efficient_long_context_tokens", None) is not None
+            and token_count >= self.efficient_long_context_tokens
+        )
+
+    def _attention_context(self, token_count: int):
+        if not self._uses_efficient_attention(token_count):
+            return nullcontext()
+        # Exact math SDPA materializes an O(sequence^2) attention matrix.  A
+        # few public JevJudge text records exceed 30K tokens, which can require
+        # roughly 60 GiB for that matrix alone.  Release cached short-record
+        # workspaces and force a memory-linear fused backend for those records.
+        torch.cuda.empty_cache()
+        return sdpa_kernel(
+            [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION],
+            set_priority=True,
+        )
+
     def _chat_ids(self, prompt: str) -> torch.Tensor:
         messages = [
             {"role": "system", "content": SYSTEM_PROMPT},
@@ -477,11 +516,12 @@ def _letter_question(self, state, question: dict) -> tuple[list[str], list[float
             )
         prompt = build_prompt(state, question.get("instructions") or "", descriptions)
         ids = self._chat_ids(prompt)
-        output = self.language_model(
-            input_ids=ids,
-            attention_mask=torch.ones_like(ids),
-            use_cache=False,
-        )
+        with self._attention_context(int(ids.shape[1])):
+            output = self.language_model(
+                input_ids=ids,
+                attention_mask=torch.ones_like(ids),
+                use_cache=False,
+            )
         hidden = output.last_hidden_state[0, -1]
         projection = self.letter_projection
         selected_logits = F.linear(
@@ -512,8 +552,10 @@ def _native(self, record: dict) -> tuple[list[list[float]], int]:
                 f"native request needs {len(encoded['ids'])} packed tokens; "
                 f"limit {self.effective_max_tokens}"
             )
-        return [F.softmax(logits, -1).float().cpu().tolist()
-                for logits in self.native_model.forward(encoded)], len(encoded["ids"])
+        with self._attention_context(len(encoded["ids"])):
+            rows = [F.softmax(logits, -1).float().cpu().tolist()
+                    for logits in self.native_model.forward(encoded)]
+        return rows, len(encoded["ids"])
 
     def _pointer(self, record: dict) -> tuple[list[list[float]], int]:
         """Compatibility alias for the generalized native checkpoint pass."""
@@ -567,6 +609,10 @@ def __call__(self, record: dict) -> dict:
             "inference_temperature": self.temperature,
             "latency_ms": (time.perf_counter() - started) * 1000,
             "input_tokens": sum(row[3] for row in letter_rows) + native_tokens,
+            "efficient_long_context_attention_used": (
+                any(self._uses_efficient_attention(row[3]) for row in letter_rows)
+                or self._uses_efficient_attention(native_tokens)
+            ),
             "readout": {
                 "method": self.provenance["method"],
                 "adapter_applied": self.provenance["adapter_applied"],
diff --git a/scripts/evaluate_choice_readout_matrix.py b/scripts/evaluate_choice_readout_matrix.py
index 7406c6e..fba7833 100644
--- a/scripts/evaluate_choice_readout_matrix.py
+++ b/scripts/evaluate_choice_readout_matrix.py
@@ -37,6 +37,7 @@
     ENCODING,
     digest,
     load_split,
+    read_json,
     record_digest,
     write_json,
 )
@@ -136,10 +137,14 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
     destination.mkdir(parents=True, exist_ok=False)
     component_rows: dict[str, list[dict]] = defaultdict(list)
     latencies = []
+    efficient_long_context_records = 0
     started = time.time()
     with (destination / "predictions.jsonl").open("w", encoding=ENCODING) as stream:
         for index, record in enumerate(records, 1):
             result = predictor(record)
+            efficient_long_context_records += int(
+                result.get("efficient_long_context_attention_used", False)
+            )
             components = result.get("component_probabilities") or {
                 "choice": result["probabilities"]
             }
@@ -174,6 +179,7 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
         "questions": sum(len(record["questions"]) for record in records),
         "components": reports,
         "components_executed": sorted(component_rows),
+        "efficient_long_context_records": efficient_long_context_records,
         "latency_scope": (
             "end-to-end predictor call; checkpoint calls execute both choice and native "
             "components, so these values are not per-component efficiency measurements"
@@ -188,6 +194,38 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
     return report
 
 
+def reuse_completed(
+    name: str,
+    records: list[dict],
+    predictor,
+    destination: Path,
+) -> dict:
+    """Load and validate one completed dataset from an interrupted matrix run."""
+
+    report_path = destination / "report.json"
+    if not report_path.is_file():
+        raise RuntimeError(
+            f"cannot resume incomplete dataset directory: {destination}; preserve or "
+            "move that directory aside before retrying"
+        )
+    report = read_json(report_path)
+    expected_questions = sum(len(record["questions"]) for record in records)
+    expected_components = {"choice", "native"} if predictor.return_components else {"choice"}
+    actual_components = set(report.get("components", {}))
+    if (
+        report.get("dataset") != name
+        or report.get("records") != len(records)
+        or report.get("questions") != expected_questions
+        or actual_components != expected_components
+    ):
+        raise RuntimeError(f"completed report does not match requested resume dataset: {destination}")
+    for component in expected_components:
+        rows_path = destination / f"{component}-rows.json"
+        if not rows_path.is_file() or len(read_json(rows_path)) != expected_questions:
+            raise RuntimeError(f"completed component rows are missing or incomplete: {rows_path}")
+    return report
+
+
 def _correct(report: dict, component: str) -> int:
     clean = report["components"][component]["clean"]
     return round(clean["n"] * clean["acc"])
@@ -234,6 +272,21 @@ def main() -> None:
     parser.add_argument("--dtype", choices=("fp32", "bf16"), default="bf16")
     parser.add_argument("--attn", choices=("eager", "sdpa"), default="sdpa")
     parser.add_argument("--max-tokens", type=int, default=65_536)
+    parser.add_argument(
+        "--efficient-long-context-tokens", type=int, default=8_192,
+        help=(
+            "use memory-linear fused SDPA at or above this many tokens; short "
+            "parity panels retain exact math SDPA"
+        ),
+    )
+    parser.add_argument(
+        "--resume", action="store_true",
+        help="reuse validated completed dataset directories in an interrupted run",
+    )
+    parser.add_argument(
+        "--preexisting-code-revision",
+        help="code revision that produced reused reports (required when any are reused)",
+    )
     args = parser.parse_args()
     if args.revision and args.checkpoint:
         parser.error("--revision accompanies --base, not --checkpoint")
@@ -241,7 +294,13 @@ def main() -> None:
                       or args.expected_transfer_native_correct is not None):
         parser.error("native parity assertions require --checkpoint")
 
-    args.out.mkdir(parents=True, exist_ok=False)
+    if args.resume:
+        if not args.out.is_dir():
+            parser.error("--resume requires an existing --out directory")
+    else:
+        if args.preexisting_code_revision:
+            parser.error("--preexisting-code-revision requires --resume")
+        args.out.mkdir(parents=True, exist_ok=False)
     options = LoadOptions(
         dtype={"fp32": torch.float32, "bf16": torch.bfloat16}[args.dtype],
         merge=False,
@@ -259,15 +318,31 @@ def main() -> None:
         native_weight=0.5 if args.checkpoint else 0.0,
         return_components=bool(args.checkpoint),
         exact_kernels=True,
+        efficient_long_context_tokens=args.efficient_long_context_tokens,
         max_tokens=args.max_tokens,
     )
 
+    current_code_revision = _code_revision()
+    dataset_code_revisions = {}
+    reused_datasets = []
+
+    def run_dataset(name: str, records: list[dict]) -> dict:
+        destination = args.out / name
+        if args.resume and destination.exists():
+            report = reuse_completed(name, records, predictor, destination)
+            reused_datasets.append(name)
+            dataset_code_revisions[name] = args.preexisting_code_revision
+            return report
+        report = evaluate(name, records, predictor, destination)
+        dataset_code_revisions[name] = current_code_revision
+        return report
+
     parity_datasets = {
         "jevbench_public": load_split(args.jevbench, "development"),
         "transfer_calibration": load_split(args.transfer, "development"),
     }
     reports = {
-        name: evaluate(name, records, predictor, args.out / name)
+        name: run_dataset(name, records)
         for name, records in parity_datasets.items()
     }
     if args.checkpoint:
@@ -291,9 +366,11 @@ def main() -> None:
         ],
     }
     reports.update({
-        name: evaluate(name, records, predictor, args.out / name)
+        name: run_dataset(name, records)
         for name, records in heldout_datasets.items()
     })
+    if reused_datasets and not args.preexisting_code_revision:
+        parser.error("--preexisting-code-revision is required when reports are reused")
 
     typed_manifest = json.loads((args.typed / ".hf-fetch.json").read_text())
     manifest = {
@@ -311,7 +388,7 @@ def main() -> None:
             "numpy": np.__version__,
             "pyarrow": pyarrow.__version__,
             "safetensors": safetensors.__version__,
-            "code_revision": _code_revision(),
+            "code_revision": current_code_revision,
         },
         "protocol": {
             "kernel_policy": "LocalPredictor-compatible exact CUDA policy",
@@ -326,6 +403,17 @@ def main() -> None:
                 "base calls execute choice only; checkpoint calls execute choice and native. "
                 "Do not compare component efficiency from these end-to-end timings"
             ),
+            "long_context_attention": (
+                "exact math SDPA below the configured threshold; memory-linear fused SDPA "
+                "without a math fallback at or above it"
+            ),
+            "efficient_long_context_tokens": args.efficient_long_context_tokens,
+            "resume": {
+                "enabled": args.resume,
+                "reused_datasets": reused_datasets,
+                "preexisting_code_revision": args.preexisting_code_revision,
+                "dataset_code_revisions": dataset_code_revisions,
+            },
         },
         "datasets": {
             "jevbench_development_sha256": digest(args.jevbench / "development.jsonl"),
diff --git a/tests/test_evaluate_choice_readout_matrix.py b/tests/test_evaluate_choice_readout_matrix.py
index a600a21..4dc48c5 100644
--- a/tests/test_evaluate_choice_readout_matrix.py
+++ b/tests/test_evaluate_choice_readout_matrix.py
@@ -8,6 +8,7 @@
     _typed_record,
     _typed_soft_metrics,
     evaluate,
+    reuse_completed,
 )
 
 
@@ -127,3 +128,33 @@ def predictor(_record):
     )
     assert set(prediction["component_probabilities"]) == {"choice"}
 
+
+def test_reuse_completed_validates_report_and_component_rows(tmp_path):
+    destination = tmp_path / "typed_test"
+    record = {
+        "state": "state",
+        "questions": {"q": {
+            "type": "choice", "criteria": {"a": None, "b": None},
+            "label": "a", "target": {"a": .75, "b": .25}, "src": "unit/reuse",
+        }},
+        "_meta": {
+            "id": "reuse", "group_id": "reuse", "source": "unit", "variant": "clean",
+        },
+    }
+
+    def predictor(_record):
+        return {
+            "probabilities": {"q": {"a": .75, "b": .25}},
+            "latency_ms": 1.0,
+            "input_tokens": 4,
+            "efficient_long_context_attention_used": False,
+        }
+
+    expected = evaluate("typed_test", [record], predictor, destination)
+    owner = type("Predictor", (), {"return_components": False})()
+
+    assert reuse_completed("typed_test", [record], owner, destination) == expected
+
+    (destination / "choice-rows.json").unlink()
+    with pytest.raises(RuntimeError, match="missing or incomplete"):
+        reuse_completed("typed_test", [record], owner, destination)
diff --git a/tests/test_letter_predictor.py b/tests/test_letter_predictor.py
index ca33be9..19af8e5 100644
--- a/tests/test_letter_predictor.py
+++ b/tests/test_letter_predictor.py
@@ -39,6 +39,10 @@ def test_letter_predictor_validates_generalized_native_arguments_before_loading(
         LetterReadoutPredictor(checkpoint="unused", device="cpu", exact_kernels=1)
     with pytest.raises(ValueError, match="pointer weight"):
         LetterReadoutPredictor(checkpoint="unused", device="cpu", native_weight=1.01)
+    with pytest.raises(ValueError, match="efficient_long_context_tokens"):
+        LetterReadoutPredictor(
+            checkpoint="unused", device="cpu", efficient_long_context_tokens=1,
+        )
 
 
 def test_one_row_input_ids_normalizes_template_shapes_and_mappings():
@@ -105,6 +109,16 @@ def test_safe_chat_text_escapes_gemma_and_bracket_style_controls():
     assert "［INST]" in escaped
 
 
+def test_long_context_attention_threshold_is_cuda_only():
+    predictor = object.__new__(LetterReadoutPredictor)
+    predictor.efficient_long_context_tokens = 16
+    predictor.device = "cpu"
+    assert not predictor._uses_efficient_attention(16)
+    predictor.device = "cuda"
+    assert not predictor._uses_efficient_attention(15)
+    assert predictor._uses_efficient_attention(16)
+
+
 def test_release_unused_output_head_drops_pointer_adapter_reference():
     head = object()
     model = type("Model", (), {

From 277cab9f3962bfc37366e30c061edabce144ac4d Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 00:49:09 +0000
Subject: [PATCH 03/10] Handle long-context choice evaluation and resume runs

---
 jevany/letter_predictor.py                   | 62 +++++++++++--
 scripts/evaluate_choice_readout_matrix.py    | 96 +++++++++++++++++++-
 tests/test_evaluate_choice_readout_matrix.py | 31 +++++++
 tests/test_letter_predictor.py               | 14 +++
 4 files changed, 191 insertions(+), 12 deletions(-)

diff --git a/jevany/letter_predictor.py b/jevany/letter_predictor.py
index e0856f9..1459d74 100644
--- a/jevany/letter_predictor.py
+++ b/jevany/letter_predictor.py
@@ -9,6 +9,7 @@
 from __future__ import annotations
 
 from collections.abc import Mapping
+from contextlib import nullcontext
 import hashlib
 import json
 import math
@@ -17,6 +18,7 @@
 
 import torch
 import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
 
 from .api import SystemOneRequest, question_keys
 from .checkpoint import Checkpoint, LoadOptions
@@ -244,6 +246,7 @@ def __init__(
         native_weight: float | None = None,
         return_components: bool = False,
         exact_kernels: bool = True,
+        efficient_long_context_tokens: int | None = None,
         max_tokens: int = 16_384,
     ) -> None:
         if (base is None) == (checkpoint is None):
@@ -278,7 +281,16 @@ def __init__(
             raise TypeError("exact_kernels must be a boolean")
         self.return_components = return_components
         self.exact_kernels = exact_kernels
-        self.exact_cuda_kernels_applied = device == "cuda" and exact_kernels
+        self.exact_cuda_kernels_applied = str(device).startswith("cuda") and exact_kernels
+        if (
+            efficient_long_context_tokens is not None
+            and (
+                type(efficient_long_context_tokens) is not int
+                or efficient_long_context_tokens < 2
+            )
+        ):
+            raise ValueError("efficient_long_context_tokens must be an integer >= 2 or None")
+        self.efficient_long_context_tokens = efficient_long_context_tokens
         if type(max_tokens) is not int or max_tokens < 2:
             raise ValueError("max_tokens must be an integer >= 2")
         self.max_tokens = max_tokens
@@ -442,6 +454,13 @@ def __init__(
             "return_components": self.return_components,
             "exact_kernels": self.exact_kernels,
             "exact_cuda_kernels_applied": self.exact_cuda_kernels_applied,
+            "efficient_long_context_tokens": self.efficient_long_context_tokens,
+            "long_context_attention": (
+                "fused SDPA (Flash/Efficient, no math fallback) at or above the "
+                "recorded threshold; exact math SDPA below it"
+                if self.efficient_long_context_tokens is not None
+                else None
+            ),
             "max_tokens": self.max_tokens,
             "backbone_context_window": self.backbone_context_window,
             "effective_max_tokens": self.effective_max_tokens,
@@ -449,6 +468,26 @@ def __init__(
             "device": device,
         }
 
+    def _uses_efficient_attention(self, token_count: int) -> bool:
+        return (
+            str(self.device).startswith("cuda")
+            and getattr(self, "efficient_long_context_tokens", None) is not None
+            and token_count >= self.efficient_long_context_tokens
+        )
+
+    def _attention_context(self, token_count: int):
+        if not self._uses_efficient_attention(token_count):
+            return nullcontext()
+        # Exact math SDPA materializes an O(sequence^2) attention matrix.  A
+        # few public JevJudge text records exceed 30K tokens, which can require
+        # roughly 60 GiB for that matrix alone.  Release cached short-record
+        # workspaces and force a memory-linear fused backend for those records.
+        torch.cuda.empty_cache()
+        return sdpa_kernel(
+            [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION],
+            set_priority=True,
+        )
+
     def _chat_ids(self, prompt: str) -> torch.Tensor:
         messages = [
             {"role": "system", "content": SYSTEM_PROMPT},
@@ -477,11 +516,12 @@ def _letter_question(self, state, question: dict) -> tuple[list[str], list[float
             )
         prompt = build_prompt(state, question.get("instructions") or "", descriptions)
         ids = self._chat_ids(prompt)
-        output = self.language_model(
-            input_ids=ids,
-            attention_mask=torch.ones_like(ids),
-            use_cache=False,
-        )
+        with self._attention_context(int(ids.shape[1])):
+            output = self.language_model(
+                input_ids=ids,
+                attention_mask=torch.ones_like(ids),
+                use_cache=False,
+            )
         hidden = output.last_hidden_state[0, -1]
         projection = self.letter_projection
         selected_logits = F.linear(
@@ -512,8 +552,10 @@ def _native(self, record: dict) -> tuple[list[list[float]], int]:
                 f"native request needs {len(encoded['ids'])} packed tokens; "
                 f"limit {self.effective_max_tokens}"
             )
-        return [F.softmax(logits, -1).float().cpu().tolist()
-                for logits in self.native_model.forward(encoded)], len(encoded["ids"])
+        with self._attention_context(len(encoded["ids"])):
+            rows = [F.softmax(logits, -1).float().cpu().tolist()
+                    for logits in self.native_model.forward(encoded)]
+        return rows, len(encoded["ids"])
 
     def _pointer(self, record: dict) -> tuple[list[list[float]], int]:
         """Compatibility alias for the generalized native checkpoint pass."""
@@ -567,6 +609,10 @@ def __call__(self, record: dict) -> dict:
             "inference_temperature": self.temperature,
             "latency_ms": (time.perf_counter() - started) * 1000,
             "input_tokens": sum(row[3] for row in letter_rows) + native_tokens,
+            "efficient_long_context_attention_used": (
+                any(self._uses_efficient_attention(row[3]) for row in letter_rows)
+                or self._uses_efficient_attention(native_tokens)
+            ),
             "readout": {
                 "method": self.provenance["method"],
                 "adapter_applied": self.provenance["adapter_applied"],
diff --git a/scripts/evaluate_choice_readout_matrix.py b/scripts/evaluate_choice_readout_matrix.py
index 7406c6e..fba7833 100644
--- a/scripts/evaluate_choice_readout_matrix.py
+++ b/scripts/evaluate_choice_readout_matrix.py
@@ -37,6 +37,7 @@
     ENCODING,
     digest,
     load_split,
+    read_json,
     record_digest,
     write_json,
 )
@@ -136,10 +137,14 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
     destination.mkdir(parents=True, exist_ok=False)
     component_rows: dict[str, list[dict]] = defaultdict(list)
     latencies = []
+    efficient_long_context_records = 0
     started = time.time()
     with (destination / "predictions.jsonl").open("w", encoding=ENCODING) as stream:
         for index, record in enumerate(records, 1):
             result = predictor(record)
+            efficient_long_context_records += int(
+                result.get("efficient_long_context_attention_used", False)
+            )
             components = result.get("component_probabilities") or {
                 "choice": result["probabilities"]
             }
@@ -174,6 +179,7 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
         "questions": sum(len(record["questions"]) for record in records),
         "components": reports,
         "components_executed": sorted(component_rows),
+        "efficient_long_context_records": efficient_long_context_records,
         "latency_scope": (
             "end-to-end predictor call; checkpoint calls execute both choice and native "
             "components, so these values are not per-component efficiency measurements"
@@ -188,6 +194,38 @@ def evaluate(name: str, records: list[dict], predictor, destination: Path) -> di
     return report
 
 
+def reuse_completed(
+    name: str,
+    records: list[dict],
+    predictor,
+    destination: Path,
+) -> dict:
+    """Load and validate one completed dataset from an interrupted matrix run."""
+
+    report_path = destination / "report.json"
+    if not report_path.is_file():
+        raise RuntimeError(
+            f"cannot resume incomplete dataset directory: {destination}; preserve or "
+            "move that directory aside before retrying"
+        )
+    report = read_json(report_path)
+    expected_questions = sum(len(record["questions"]) for record in records)
+    expected_components = {"choice", "native"} if predictor.return_components else {"choice"}
+    actual_components = set(report.get("components", {}))
+    if (
+        report.get("dataset") != name
+        or report.get("records") != len(records)
+        or report.get("questions") != expected_questions
+        or actual_components != expected_components
+    ):
+        raise RuntimeError(f"completed report does not match requested resume dataset: {destination}")
+    for component in expected_components:
+        rows_path = destination / f"{component}-rows.json"
+        if not rows_path.is_file() or len(read_json(rows_path)) != expected_questions:
+            raise RuntimeError(f"completed component rows are missing or incomplete: {rows_path}")
+    return report
+
+
 def _correct(report: dict, component: str) -> int:
     clean = report["components"][component]["clean"]
     return round(clean["n"] * clean["acc"])
@@ -234,6 +272,21 @@ def main() -> None:
     parser.add_argument("--dtype", choices=("fp32", "bf16"), default="bf16")
     parser.add_argument("--attn", choices=("eager", "sdpa"), default="sdpa")
     parser.add_argument("--max-tokens", type=int, default=65_536)
+    parser.add_argument(
+        "--efficient-long-context-tokens", type=int, default=8_192,
+        help=(
+            "use memory-linear fused SDPA at or above this many tokens; short "
+            "parity panels retain exact math SDPA"
+        ),
+    )
+    parser.add_argument(
+        "--resume", action="store_true",
+        help="reuse validated completed dataset directories in an interrupted run",
+    )
+    parser.add_argument(
+        "--preexisting-code-revision",
+        help="code revision that produced reused reports (required when any are reused)",
+    )
     args = parser.parse_args()
     if args.revision and args.checkpoint:
         parser.error("--revision accompanies --base, not --checkpoint")
@@ -241,7 +294,13 @@ def main() -> None:
                       or args.expected_transfer_native_correct is not None):
         parser.error("native parity assertions require --checkpoint")
 
-    args.out.mkdir(parents=True, exist_ok=False)
+    if args.resume:
+        if not args.out.is_dir():
+            parser.error("--resume requires an existing --out directory")
+    else:
+        if args.preexisting_code_revision:
+            parser.error("--preexisting-code-revision requires --resume")
+        args.out.mkdir(parents=True, exist_ok=False)
     options = LoadOptions(
         dtype={"fp32": torch.float32, "bf16": torch.bfloat16}[args.dtype],
         merge=False,
@@ -259,15 +318,31 @@ def main() -> None:
         native_weight=0.5 if args.checkpoint else 0.0,
         return_components=bool(args.checkpoint),
         exact_kernels=True,
+        efficient_long_context_tokens=args.efficient_long_context_tokens,
         max_tokens=args.max_tokens,
     )
 
+    current_code_revision = _code_revision()
+    dataset_code_revisions = {}
+    reused_datasets = []
+
+    def run_dataset(name: str, records: list[dict]) -> dict:
+        destination = args.out / name
+        if args.resume and destination.exists():
+            report = reuse_completed(name, records, predictor, destination)
+            reused_datasets.append(name)
+            dataset_code_revisions[name] = args.preexisting_code_revision
+            return report
+        report = evaluate(name, records, predictor, destination)
+        dataset_code_revisions[name] = current_code_revision
+        return report
+
     parity_datasets = {
         "jevbench_public": load_split(args.jevbench, "development"),
         "transfer_calibration": load_split(args.transfer, "development"),
     }
     reports = {
-        name: evaluate(name, records, predictor, args.out / name)
+        name: run_dataset(name, records)
         for name, records in parity_datasets.items()
     }
     if args.checkpoint:
@@ -291,9 +366,11 @@ def main() -> None:
         ],
     }
     reports.update({
-        name: evaluate(name, records, predictor, args.out / name)
+        name: run_dataset(name, records)
         for name, records in heldout_datasets.items()
     })
+    if reused_datasets and not args.preexisting_code_revision:
+        parser.error("--preexisting-code-revision is required when reports are reused")
 
     typed_manifest = json.loads((args.typed / ".hf-fetch.json").read_text())
     manifest = {
@@ -311,7 +388,7 @@ def main() -> None:
             "numpy": np.__version__,
             "pyarrow": pyarrow.__version__,
             "safetensors": safetensors.__version__,
-            "code_revision": _code_revision(),
+            "code_revision": current_code_revision,
         },
         "protocol": {
             "kernel_policy": "LocalPredictor-compatible exact CUDA policy",
@@ -326,6 +403,17 @@ def main() -> None:
                 "base calls execute choice only; checkpoint calls execute choice and native. "
                 "Do not compare component efficiency from these end-to-end timings"
             ),
+            "long_context_attention": (
+                "exact math SDPA below the configured threshold; memory-linear fused SDPA "
+                "without a math fallback at or above it"
+            ),
+            "efficient_long_context_tokens": args.efficient_long_context_tokens,
+            "resume": {
+                "enabled": args.resume,
+                "reused_datasets": reused_datasets,
+                "preexisting_code_revision": args.preexisting_code_revision,
+                "dataset_code_revisions": dataset_code_revisions,
+            },
         },
         "datasets": {
             "jevbench_development_sha256": digest(args.jevbench / "development.jsonl"),
diff --git a/tests/test_evaluate_choice_readout_matrix.py b/tests/test_evaluate_choice_readout_matrix.py
index a600a21..4dc48c5 100644
--- a/tests/test_evaluate_choice_readout_matrix.py
+++ b/tests/test_evaluate_choice_readout_matrix.py
@@ -8,6 +8,7 @@
     _typed_record,
     _typed_soft_metrics,
     evaluate,
+    reuse_completed,
 )
 
 
@@ -127,3 +128,33 @@ def predictor(_record):
     )
     assert set(prediction["component_probabilities"]) == {"choice"}
 
+
+def test_reuse_completed_validates_report_and_component_rows(tmp_path):
+    destination = tmp_path / "typed_test"
+    record = {
+        "state": "state",
+        "questions": {"q": {
+            "type": "choice", "criteria": {"a": None, "b": None},
+            "label": "a", "target": {"a": .75, "b": .25}, "src": "unit/reuse",
+        }},
+        "_meta": {
+            "id": "reuse", "group_id": "reuse", "source": "unit", "variant": "clean",
+        },
+    }
+
+    def predictor(_record):
+        return {
+            "probabilities": {"q": {"a": .75, "b": .25}},
+            "latency_ms": 1.0,
+            "input_tokens": 4,
+            "efficient_long_context_attention_used": False,
+        }
+
+    expected = evaluate("typed_test", [record], predictor, destination)
+    owner = type("Predictor", (), {"return_components": False})()
+
+    assert reuse_completed("typed_test", [record], owner, destination) == expected
+
+    (destination / "choice-rows.json").unlink()
+    with pytest.raises(RuntimeError, match="missing or incomplete"):
+        reuse_completed("typed_test", [record], owner, destination)
diff --git a/tests/test_letter_predictor.py b/tests/test_letter_predictor.py
index ca33be9..19af8e5 100644
--- a/tests/test_letter_predictor.py
+++ b/tests/test_letter_predictor.py
@@ -39,6 +39,10 @@ def test_letter_predictor_validates_generalized_native_arguments_before_loading(
         LetterReadoutPredictor(checkpoint="unused", device="cpu", exact_kernels=1)
     with pytest.raises(ValueError, match="pointer weight"):
         LetterReadoutPredictor(checkpoint="unused", device="cpu", native_weight=1.01)
+    with pytest.raises(ValueError, match="efficient_long_context_tokens"):
+        LetterReadoutPredictor(
+            checkpoint="unused", device="cpu", efficient_long_context_tokens=1,
+        )
 
 
 def test_one_row_input_ids_normalizes_template_shapes_and_mappings():
@@ -105,6 +109,16 @@ def test_safe_chat_text_escapes_gemma_and_bracket_style_controls():
     assert "［INST]" in escaped
 
 
+def test_long_context_attention_threshold_is_cuda_only():
+    predictor = object.__new__(LetterReadoutPredictor)
+    predictor.efficient_long_context_tokens = 16
+    predictor.device = "cpu"
+    assert not predictor._uses_efficient_attention(16)
+    predictor.device = "cuda"
+    assert not predictor._uses_efficient_attention(15)
+    assert predictor._uses_efficient_attention(16)
+
+
 def test_release_unused_output_head_drops_pointer_adapter_reference():
     head = object()
     model = type("Model", (), {

From a88bfa91844127f1b14178e20c5195c0338643e4 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 00:46:33 +0000
Subject: [PATCH 04/10] Expose choice readout as the public API

---
 jevany/__init__.py                |   6 +-
 jevany/benchmark.py               |  42 ++++----
 jevany/cli.py                     |  20 ++--
 jevany/demos/static/app.js        |   3 +-
 jevany/letter_predictor.py        |  13 +--
 jevany/letter_runtime.py          |  40 ++++----
 jevany/readout.py                 | 160 +++++++++++++++++++++++-------
 jevany/runtime.py                 |  46 +++++----
 jevany/serve.py                   |  56 ++++++-----
 tests/test_playground_workflow.py |  13 ++-
 tests/test_readout_integration.py | 130 ++++++++++++++++--------
 11 files changed, 355 insertions(+), 174 deletions(-)

diff --git a/jevany/__init__.py b/jevany/__init__.py
index d999df1..a3ca0e6 100644
--- a/jevany/__init__.py
+++ b/jevany/__init__.py
@@ -5,8 +5,12 @@
 from .api import Choice, Noul, Score, SystemOneRequest
 from .client import JevClient
 from .inference import InferenceOptions
+from .readout import ChoiceReadoutOptions
 
-__all__ = ["Choice", "Noul", "Score", "SystemOneRequest", "JevClient", "JevModel", "InferenceOptions"]
+__all__ = [
+    "Choice", "Noul", "Score", "SystemOneRequest", "JevClient", "JevModel",
+    "InferenceOptions", "ChoiceReadoutOptions",
+]
 
 
 def __getattr__(name: str):
diff --git a/jevany/benchmark.py b/jevany/benchmark.py
index 0a361ca..5ae3a5c 100644
--- a/jevany/benchmark.py
+++ b/jevany/benchmark.py
@@ -26,7 +26,7 @@
 from jevany.metrics import EPSILON, grouped_metrics, metrics, unknowable_report
 from jevany.model import ContextLengthError
 from jevany.predictors import LocalPredictor, RemotePredictor
-from jevany.readout import add_readout_arguments, letter_options_from_args
+from jevany.readout import add_readout_arguments, choice_options_from_args, normalize_readout
 from jevany.suite import ENCODING, digest, load_split, read_manifest, record_digest, write_json
 
 
@@ -170,7 +170,7 @@ def evaluate_records(records, predictor, directory, temperature=1.0, heldout_sou
 EXAMPLES = """examples:
   jevany eval --run runs/my-jev --data data/starter/development.jsonl --out runs/my-jev/eval
   jevany eval --run SimpleJev/JevAny-Qwen3.5-4B-LoRA --suite data/eval-suite --out runs/eval
-  jevany eval --run SimpleJev/JevAny-Qwen3.5-4B-LoRA --readout letter --suite data/eval-suite --out runs/letter
+  jevany eval --run SimpleJev/JevAny-Qwen3.5-4B-LoRA --readout choice --suite data/eval-suite --out runs/choice
   jevany eval --remote http://127.0.0.1:8008 --data my-labelled.jsonl --out runs/remote-eval
 
 --data scores your own labelled JSONL (jevany.data.load_records); --suite scores a
@@ -195,12 +195,12 @@ def main(argv=None, prog=None):
     ap.add_argument("--date_facts", action="store_true", help="apply jevany.api.with_date_facts to every state before scoring (the opt-in serving preprocessor); reported in report.json")
     a = ap.parse_args(argv)
     try:
-        letter_options = letter_options_from_args(a)
+        choice_options = choice_options_from_args(a)
     except ValueError as error:
         ap.error(str(error))
     if bool(a.run) == bool(a.remote): ap.error("give exactly one of --run or --remote")
-    if a.remote and letter_options is not None:
-        ap.error("--readout letter is a local model setting; configure it on the remote server")
+    if a.remote and choice_options is not None:
+        ap.error("--readout choice is a local model setting; configure it on the remote server")
     if bool(a.suite) == bool(a.data): ap.error("give exactly one of --suite or --data")
     if a.data:
         records, heldout, split, source_hash = load_records(a.data), [], "custom", digest(Path(a.data))
@@ -212,34 +212,40 @@ def main(argv=None, prog=None):
         records = [{**r, "state": with_date_facts(r["state"])} for r in records]
     if a.remote:
         predictor = RemotePredictor(a.remote, a.remote_model, os.environ.get("JEVANY_REMOTE_API_KEY", "local"))
-    elif letter_options is not None:
+    elif choice_options is not None:
         from jevany.letter_predictor import LetterReadoutPredictor
         predictor = LetterReadoutPredictor(
             checkpoint=a.run,
             device=a.device,
             options=LoadOptions.from_env(),
-            temperature=letter_options.temperature,
-            pointer_weight=letter_options.pointer_weight,
-            max_tokens=letter_options.max_tokens,
+            temperature=choice_options.temperature,
+            native_weight=choice_options.native_weight,
+            max_tokens=choice_options.max_tokens,
         )
     else:
         predictor = LocalPredictor(a.run, a.device, LoadOptions.from_env())
     report, _ = evaluate_records(records, predictor, a.out, heldout_sources=tuple(heldout), skip_overlong=bool(a.data))
-    pointer_temperature = (getattr(predictor.pointer_model, "temperature", None)
-                           if letter_options is not None and predictor.pointer_weight else None)
+    native_temperature = (getattr(predictor.native_model, "temperature", None)
+                          if choice_options is not None and predictor.native_weight else None)
     calibration_applied = (None if a.remote else predictor.temperature != 1.0
-                           or pointer_temperature not in (None, 1.0))
+                           or native_temperature not in (None, 1.0))
     report.update(suite_sha256=source_hash, data=a.data, date_facts=a.date_facts, run=a.run or a.remote, split=split,
                   calibration_applied=calibration_applied,
                   base_loading=getattr(predictor, "base_loading", None),
                   remote={"base_url": a.remote, "requested_model": a.remote_model, "served_model": predictor.served_model} if a.remote else None)
     if not a.remote:
-        report["readout"] = a.readout
-    if letter_options is not None:
-        report["letter_readout"] = dict(predictor.provenance)
-        if pointer_temperature is not None:
-            report["letter_readout"]["pointer_temperature"] = pointer_temperature
-            report["calibration"]["pointer_temperature"] = pointer_temperature
+        report["readout"] = normalize_readout(a.readout)
+    if choice_options is not None:
+        choice_readout = dict(predictor.provenance)
+        if native_temperature is not None:
+            choice_readout["native_temperature"] = native_temperature
+            choice_readout["pointer_temperature"] = native_temperature
+            report["calibration"]["native_temperature"] = native_temperature
+            report["calibration"]["pointer_temperature"] = native_temperature
+        report["choice_readout"] = choice_readout
+        # Kept as an output alias for consumers of reports written before the
+        # public readout name changed to ``choice``.
+        report["letter_readout"] = dict(choice_readout)
     write_json(Path(a.out) / "report.json", report)
     print(json.dumps({"objective": report["objective"], "clean": report["clean"], "coverage": report["coverage"]}, indent=2))
 
diff --git a/jevany/cli.py b/jevany/cli.py
index 055a321..a04a0d7 100644
--- a/jevany/cli.py
+++ b/jevany/cli.py
@@ -45,7 +45,7 @@ def decide_main(argv: list[str]) -> None:
     from dataclasses import fields
     from .inference import InferenceOptions, add_inference_arguments, inference_options_from_args
     from .placement import add_placement_arguments
-    from .readout import add_readout_arguments, letter_options_from_args
+    from .readout import add_readout_arguments, choice_options_from_args
 
     parser = argparse.ArgumentParser(prog="jevany decide")
     parser.add_argument("request", help="JSON request file; - reads stdin")
@@ -63,7 +63,7 @@ def decide_main(argv: list[str]) -> None:
     add_inference_arguments(parser)
     args = parser.parse_args(argv)
     try:
-        letter_options = letter_options_from_args(args)
+        choice_options = choice_options_from_args(args)
     except ValueError as error:
         parser.error(str(error))
     content = sys.stdin.read() if args.request == "-" else Path(args.request).read_text(encoding="utf-8")
@@ -73,12 +73,12 @@ def decide_main(argv: list[str]) -> None:
         from dataclasses import replace
         from .checkpoint import LoadOptions, load_options_from_args
         from .runtime import JevModel
-        if letter_options is not None and (
+        if choice_options is not None and (
             args.cuda_graphs or args.cuda_graph_max_tokens is not None
             or any(getattr(args, item.name) is not None for item in fields(InferenceOptions))
         ):
-            parser.error("CUDA graph and native inference-limit flags cannot be used with --readout letter; "
-                         "use --letter-max-tokens")
+            parser.error("CUDA graph and native inference-limit flags cannot be used with --readout choice; "
+                         "use --choice-max-tokens")
         options = load_options_from_args(args)
         if args.cuda_graphs or args.cuda_graph_max_tokens is not None:
             options = options or LoadOptions.from_env()
@@ -87,11 +87,11 @@ def decide_main(argv: list[str]) -> None:
                                                      if args.cuda_graph_max_tokens is not None
                                                      else options.cuda_graph_max_tokens))
         settings = ({
-            "readout": "letter",
-            "letter_temperature": letter_options.temperature,
-            "letter_pointer_weight": letter_options.pointer_weight,
-            "letter_max_tokens": letter_options.max_tokens,
-        } if letter_options is not None else {
+            "readout": "choice",
+            "choice_temperature": choice_options.temperature,
+            "choice_native_weight": choice_options.native_weight,
+            "choice_max_tokens": choice_options.max_tokens,
+        } if choice_options is not None else {
             "inference_options": inference_options_from_args(args),
         })
         client = JevModel.from_pretrained(
diff --git a/jevany/demos/static/app.js b/jevany/demos/static/app.js
index b7a51d1..2f43c48 100644
--- a/jevany/demos/static/app.js
+++ b/jevany/demos/static/app.js
@@ -217,7 +217,8 @@ async function liveStep(action) {
 }
 function renderConnection() {
   const media = link.media || {}, served = link.served;
-  const readout = served && served.readout === "letter" ? "letter" : served && served.decision_mode;
+  const readout = served && (served.readout === "choice" || served.readout === "letter")
+    ? "choice" : served && served.decision_mode;
   $("connect-url").disabled = $("connect-model").disabled = !link.editable;
   if (!$("connect-url").value) $("connect-url").value = link.base_url || link.default_base_url || "";
   if (!$("connect-model").value && link.model && link.model !== "jevany-latest") $("connect-model").value = link.model;
diff --git a/jevany/letter_predictor.py b/jevany/letter_predictor.py
index 1459d74..078bba4 100644
--- a/jevany/letter_predictor.py
+++ b/jevany/letter_predictor.py
@@ -1,4 +1,4 @@
-"""Training-free option-letter inference over a frozen base or JevAny adapter.
+"""Training-free choice-token inference over a frozen base or JevAny adapter.
 
 This is the in-process backend for :mod:`jevany.letter_readout`.  It keeps the
 existing pointer path unchanged: a JevAny checkpoint can instead be applied to
@@ -297,7 +297,7 @@ def __init__(
         self.device = device
         self.options = options or LoadOptions()
         if self.options.temperature is not None:
-            raise ValueError("native-head temperature is not used by letter readout; use temperature")
+            raise ValueError("native-head temperature is not used by choice readout; use temperature")
         if self.options.cuda_graphs:
             raise ValueError("CUDA graph capture is available only for native readout")
         if self.exact_cuda_kernels_applied:
@@ -323,7 +323,7 @@ def __init__(
                 raise ValueError("revision accompanies a base; pin checkpoint revisions in owner/repo@revision")
             loaded = self.checkpoint = Checkpoint(checkpoint)
             if loaded.meta.special_embeddings:
-                raise ValueError("letter readout is not validated for checkpoints with trained special embeddings")
+                raise ValueError("choice readout is not validated for checkpoints with trained special embeddings")
             self.preprocessor, decision_model = loaded.load(device, self.options)
             self.native_model = decision_model
             self.pointer_model = decision_model
@@ -406,7 +406,7 @@ def __init__(
         if tied:
             embeddings = self.language_model.get_input_embeddings().weight
             if max(flat_token_ids) >= embeddings.shape[0]:
-                raise ValueError("letter choice token ID exceeds the tied vocabulary projection")
+                raise ValueError("choice token ID exceeds the tied vocabulary projection")
             index = torch.tensor(flat_token_ids, dtype=torch.long, device=embeddings.device)
             projection = embeddings.index_select(0, index).detach().clone()
             projection_bias = None
@@ -438,6 +438,7 @@ def __init__(
             "adapter_scale": adapter_scale,
             "tied_output_embeddings": tied,
             "unused_full_output_head_released": released_output_head,
+            "choice_token_rows": len(flat_token_ids),
             "letter_choice_token_rows": len(flat_token_ids),
             "output_softcap": self.output_softcap,
             "temperature": self.temperature,
@@ -503,7 +504,7 @@ def _chat_ids(self, prompt: str) -> torch.Tensor:
         ids = _one_row_input_ids(ids)
         if ids.shape[1] + 1 > self.effective_max_tokens:
             raise ContextLengthError(
-                f"letter prompt needs {ids.shape[1] + 1} tokens including the answer; "
+                f"choice prompt needs {ids.shape[1] + 1} tokens including the answer; "
                 f"limit {self.effective_max_tokens}"
             )
         return ids.to(self.device)
@@ -567,7 +568,7 @@ def __call__(self, record: dict) -> dict:
         request = api_request(record)
         SystemOneRequest.model_validate(request)
         if request.get("media"):
-            raise ValueError("letter readout is text-only and does not accept media")
+            raise ValueError("choice readout is text-only and does not accept media")
         sync(self.device)
         started = time.perf_counter()
         letter_rows = [
diff --git a/jevany/letter_runtime.py b/jevany/letter_runtime.py
index 2f41022..1ea0637 100644
--- a/jevany/letter_runtime.py
+++ b/jevany/letter_runtime.py
@@ -1,4 +1,4 @@
-"""System One runtime adapter for the training-free option-letter predictor."""
+"""System One runtime adapter for the training-free choice-token predictor."""
 
 from __future__ import annotations
 
@@ -7,6 +7,7 @@
 from typing import Any
 
 from .api import SystemOneRequest, output_tokens, to_answers, to_record, validate_response
+from .letter_readout import CHOICE_SYMBOLS
 
 
 def _unlabelled_record(request: SystemOneRequest) -> dict[str, Any]:
@@ -41,7 +42,7 @@ def __post_init__(self) -> None:
         if not isinstance(self.model_id, str) or not self.model_id.strip():
             raise ValueError("model_name must be a nonempty string")
         if self.predictor.checkpoint is None:
-            raise ValueError("serving letter readout requires a JevAny checkpoint")
+            raise ValueError("serving choice readout requires a JevAny checkpoint")
 
     @property
     def checkpoint(self):
@@ -52,25 +53,26 @@ def aliases(self) -> list[str]:
         return [] if self.model_id == "jevany-latest" else ["jevany-latest"]
 
     def clear_cache(self) -> None:
-        """Letter readout currently keeps no mutable prefix cache."""
+        """Choice readout currently keeps no mutable prefix cache."""
 
     def describe(self) -> dict[str, Any]:
-        """Describe the effective letter readout and its checkpoint overlay."""
+        """Describe the effective choice readout and its checkpoint overlay."""
 
         checkpoint = self.checkpoint
-        pointer_model = self.predictor.pointer_model
-        capabilities = pointer_model.inference_capabilities
+        native_model = self.predictor.native_model
+        capabilities = native_model.inference_capabilities
         context_window = capabilities.context_window
         effective_window = self.predictor.effective_max_tokens
-        acceleration = getattr(pointer_model, "inference_acceleration", {
+        acceleration = getattr(native_model, "inference_acceleration", {
             "compile_mode": None,
             "lora_merged": False,
             "approximate_bf16_merge": False,
             "cuda_graphs": None,
         })
-        letter_readout = dict(self.predictor.provenance)
-        if self.predictor.pointer_weight:
-            letter_readout["pointer_temperature"] = pointer_model.temperature
+        choice_readout = dict(self.predictor.provenance)
+        if self.predictor.native_weight:
+            choice_readout["native_temperature"] = native_model.temperature
+            choice_readout["pointer_temperature"] = native_model.temperature
         with self.lock:
             return {
                 "id": self.model_id,
@@ -79,13 +81,15 @@ def describe(self) -> dict[str, Any]:
                 "base": checkpoint.meta.base,
                 "lora": checkpoint.meta.lora,
                 "device": self.predictor.device,
-                "device_map": getattr(pointer_model, "device_map", None),
-                "devices": getattr(pointer_model, "devices", [self.predictor.device]),
+                "device_map": getattr(native_model, "device_map", None),
+                "devices": getattr(native_model, "devices", [self.predictor.device]),
                 "temperature": self.predictor.temperature,
                 "decision_mode": checkpoint.meta.decision_mode,
-                "readout": "letter",
-                "letter_readout": letter_readout,
-                "backbone_adapter": pointer_model.backbone_adapter,
+                "readout": "choice",
+                "choice_readout": choice_readout,
+                # Legacy descriptor alias retained for older clients.
+                "letter_readout": dict(choice_readout),
+                "backbone_adapter": native_model.backbone_adapter,
                 "branch_mode": "chat",
                 "acceleration": acceleration,
                 "capabilities": {
@@ -98,7 +102,7 @@ def describe(self) -> dict[str, Any]:
                     "state_tokens": effective_window,
                     "branch_tokens": effective_window,
                     "packed_tokens": effective_window,
-                    "choices": 26,
+                    "choices": len(CHOICE_SYMBOLS),
                 },
                 "prefix_cache": {
                     "enabled": False,
@@ -111,12 +115,12 @@ def describe(self) -> dict[str, Any]:
             }
 
     def answer(self, request: SystemOneRequest) -> dict[str, Any]:
-        """Run letter inference and return a validated System One response."""
+        """Run choice-token inference and return a validated System One response."""
 
         if request.model not in (self.model_id, *self.aliases):
             raise ValueError(f"unknown model {request.model!r}; this deployment serves {self.model_id!r}")
         if request.media:
-            raise ValueError("letter readout does not support media requests")
+            raise ValueError("choice readout does not support media requests")
         _, metadata = to_record(request)
         with self.lock:
             prediction = self.predictor(_unlabelled_record(request))
diff --git a/jevany/readout.py b/jevany/readout.py
index 99f8b45..387a1a1 100644
--- a/jevany/readout.py
+++ b/jevany/readout.py
@@ -11,69 +11,159 @@
 import math
 
 
-READOUTS = ("native", "letter")
+# ``letter`` is accepted indefinitely as the legacy spelling, but is omitted
+# from generated usage/help so new integrations consistently say ``choice``.
+READOUTS = ("native", "choice")
+_ACCEPTED_READOUTS = (*READOUTS, "letter")
+
+
+def normalize_readout(readout: str) -> str:
+    """Return the public readout name while accepting the legacy alias."""
+
+    if readout == "letter":
+        return "choice"
+    if readout not in READOUTS:
+        raise ValueError("readout must be native or choice")
+    return readout
 
 
 @dataclass(frozen=True)
-class LetterReadoutOptions:
-    """Runtime settings for the training-free option-letter readout."""
+class ChoiceReadoutOptions:
+    """Runtime settings for the training-free choice-token readout."""
 
     temperature: float = 1.0
-    pointer_weight: float = 0.0
+    native_weight: float = 0.0
     max_tokens: int = 16_384
 
     def __post_init__(self) -> None:
-        for name in ("temperature", "pointer_weight"):
+        for name in ("temperature", "native_weight"):
             value = getattr(self, name)
             if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value):
-                raise ValueError(f"letter {name.replace('_', ' ')} must be finite and numeric")
+                raise ValueError(f"choice {name.replace('_', ' ')} must be finite and numeric")
         if self.temperature <= 0:
-            raise ValueError("letter temperature must be positive")
-        if not 0 <= self.pointer_weight <= 1:
-            raise ValueError("letter pointer weight must be in [0, 1]")
+            raise ValueError("choice temperature must be positive")
+        if not 0 <= self.native_weight <= 1:
+            raise ValueError("choice native weight must be in [0, 1]")
         if type(self.max_tokens) is not int or self.max_tokens < 2:
-            raise ValueError("letter max tokens must be an integer >= 2")
+            raise ValueError("choice max tokens must be an integer >= 2")
+
+    @property
+    def pointer_weight(self) -> float:
+        """Legacy name for :attr:`native_weight`."""
+
+        return self.native_weight
+
+
+@dataclass(frozen=True)
+class LetterReadoutOptions:
+    """Legacy spelling of :class:`ChoiceReadoutOptions`."""
+
+    temperature: float = 1.0
+    pointer_weight: float = 0.0
+    max_tokens: int = 16_384
+
+    def __post_init__(self) -> None:
+        # Validate through the public representation so both APIs have exactly
+        # the same accepted values.
+        ChoiceReadoutOptions(self.temperature, self.pointer_weight, self.max_tokens)
+
+    @property
+    def native_weight(self) -> float:
+        return self.pointer_weight
+
+    def as_choice(self) -> ChoiceReadoutOptions:
+        return ChoiceReadoutOptions(self.temperature, self.pointer_weight, self.max_tokens)
 
 
 def add_readout_arguments(parser: argparse.ArgumentParser) -> None:
-    """Add the same readout selector and letter-only knobs to a CLI parser."""
+    """Add shared readout arguments, with legacy flags accepted but hidden."""
 
     parser.add_argument(
-        "--readout", choices=READOUTS, default="native",
-        help="native checkpoint head (default) or training-free option-letter readout",
+        "--readout", choices=_ACCEPTED_READOUTS, metavar="{native,choice}", default="native",
+        help="native checkpoint head (default) or training-free choice-token readout",
     )
     parser.add_argument(
-        "--letter-temperature", type=float,
-        help="temperature for letter probability calibration (letter readout only; default 1.0)",
+        "--choice-temperature", type=float,
+        help="probability temperature for choice readout (default 1.0)",
     )
     parser.add_argument(
-        "--letter-pointer-weight", type=float,
-        help="log-linear weight on the native pointer distribution (letter readout only; default 0)",
+        "--choice-native-weight", type=float,
+        help="log-linear weight on the checkpoint's native distribution (default 0)",
     )
     parser.add_argument(
-        "--letter-max-tokens", type=int,
-        help="maximum chat-prompt tokens including the one-letter answer (letter readout only; default 16384)",
+        "--choice-max-tokens", type=int,
+        help="maximum chat-prompt tokens including the one-token answer (default 16384)",
+    )
+    # Backward-compatible spellings. argparse.SUPPRESS keeps the preferred
+    # surface compact without removing existing scripts.
+    parser.add_argument("--letter-temperature", type=float, help=argparse.SUPPRESS)
+    parser.add_argument("--letter-pointer-weight", type=float, help=argparse.SUPPRESS)
+    parser.add_argument("--letter-max-tokens", type=int, help=argparse.SUPPRESS)
+
+
+def resolve_readout_options(
+    readout: str = "native", *,
+    choice_temperature: float | None = None,
+    choice_native_weight: float | None = None,
+    choice_max_tokens: int | None = None,
+    letter_temperature: float | None = None,
+    letter_pointer_weight: float | None = None,
+    letter_max_tokens: int | None = None,
+) -> tuple[str, ChoiceReadoutOptions | None]:
+    """Normalize aliases and resolve preferred/legacy setting spellings."""
+
+    normalized = normalize_readout(readout)
+    pairs = (
+        ("temperature", "--choice-temperature", choice_temperature,
+         "--letter-temperature", letter_temperature),
+        ("native_weight", "--choice-native-weight", choice_native_weight,
+         "--letter-pointer-weight", letter_pointer_weight),
+        ("max_tokens", "--choice-max-tokens", choice_max_tokens,
+         "--letter-max-tokens", letter_max_tokens),
+    )
+    values, used = {}, []
+    for name, preferred_flag, preferred, legacy_flag, legacy in pairs:
+        if preferred is not None and legacy is not None:
+            raise ValueError(f"give at most one of {preferred_flag} and legacy {legacy_flag}")
+        if preferred is not None:
+            values[name] = preferred
+            used.append(preferred_flag)
+        elif legacy is not None:
+            values[name] = legacy
+            used.append(legacy_flag)
+    if normalized == "native":
+        if used:
+            raise ValueError(f"{', '.join(used)} require --readout choice")
+        return normalized, None
+    return normalized, ChoiceReadoutOptions(**values)
+
+
+def choice_options_from_args(args: argparse.Namespace) -> ChoiceReadoutOptions | None:
+    """Return preferred choice settings from a shared CLI namespace."""
+
+    _, options = resolve_readout_options(
+        getattr(args, "readout", "native"),
+        choice_temperature=getattr(args, "choice_temperature", None),
+        choice_native_weight=getattr(args, "choice_native_weight", None),
+        choice_max_tokens=getattr(args, "choice_max_tokens", None),
+        letter_temperature=getattr(args, "letter_temperature", None),
+        letter_pointer_weight=getattr(args, "letter_pointer_weight", None),
+        letter_max_tokens=getattr(args, "letter_max_tokens", None),
     )
+    return options
 
 
 def letter_options_from_args(args: argparse.Namespace) -> LetterReadoutOptions | None:
-    """Return letter settings, rejecting letter-only flags with native readout."""
-
-    values = {
-        "temperature": getattr(args, "letter_temperature", None),
-        "pointer_weight": getattr(args, "letter_pointer_weight", None),
-        "max_tokens": getattr(args, "letter_max_tokens", None),
-    }
-    if getattr(args, "readout", "native") == "native":
-        used = ["--letter-" + name.replace("_", "-") for name, value in values.items() if value is not None]
-        if used:
-            raise ValueError(f"{', '.join(used)} require --readout letter")
-        return None
-    return LetterReadoutOptions(**{
-        name: value for name, value in values.items() if value is not None
-    })
+    """Legacy wrapper returning the historical options type."""
+
+    options = choice_options_from_args(args)
+    return (None if options is None else LetterReadoutOptions(
+        options.temperature, options.native_weight, options.max_tokens,
+    ))
 
 
 __all__ = [
-    "READOUTS", "LetterReadoutOptions", "add_readout_arguments", "letter_options_from_args",
+    "READOUTS", "ChoiceReadoutOptions", "LetterReadoutOptions",
+    "add_readout_arguments", "choice_options_from_args", "letter_options_from_args",
+    "normalize_readout", "resolve_readout_options",
 ]
diff --git a/jevany/runtime.py b/jevany/runtime.py
index 728f43b..1d1205e 100644
--- a/jevany/runtime.py
+++ b/jevany/runtime.py
@@ -14,6 +14,7 @@
 from .device import default_device, sync
 from .inference import InferenceOptions
 from .model import DecisionModel
+from .readout import resolve_readout_options
 
 DEFAULT_CHECKPOINT = "SimpleJev/JevAny-Qwen3.8-27B-LoRA"
 
@@ -165,7 +166,9 @@ def from_pretrained(
         device: str | None = None, dtype: str | None = None,
         model_name: str | None = None, options: LoadOptions | None = None,
         inference_options: InferenceOptions | None = None,
-        readout: str = "native", letter_temperature: float | None = None,
+        readout: str = "native", choice_temperature: float | None = None,
+        choice_native_weight: float | None = None, choice_max_tokens: int | None = None,
+        letter_temperature: float | None = None,
         letter_pointer_weight: float | None = None, letter_max_tokens: int | None = None,
     ) -> "JevModel":
         """Load a local run or Hugging Face adapter ID (optionally ``owner/repo@revision``).
@@ -173,20 +176,25 @@ def from_pretrained(
         The full backbone must fit on the selected device unless ``options.device_map``
         (or JEVANY_DEVICE_MAP) splits it over the visible GPUs. ``dtype`` accepts
         fp32, fp16 or bf16; omission uses the checkpoint/environment settings.
-        ``readout='letter'`` replaces the checkpoint head with a training-free
-        option-letter projection; its temperature, pointer blend, and prompt
-        limit are deployment settings, not checkpoint metadata. Files used by
-        native media requests are trusted local paths.
+        ``readout='choice'`` replaces the checkpoint head with a training-free
+        choice-token projection; its temperature, native blend, and prompt
+        limit are deployment settings, not checkpoint metadata. ``letter`` and
+        ``letter_*`` remain accepted legacy aliases. Files used by native media
+        requests are trusted local paths.
         """
         import torch
 
-        if readout not in ("native", "letter"):
-            raise ValueError("readout must be native or letter")
-        letter_settings = (letter_temperature, letter_pointer_weight, letter_max_tokens)
-        if readout == "native" and any(value is not None for value in letter_settings):
-            raise ValueError("letter readout options require readout='letter'")
-        if readout == "letter" and inference_options is not None:
-            raise ValueError("inference_options apply only to native readout; use letter_max_tokens")
+        readout, choice_options = resolve_readout_options(
+            readout,
+            choice_temperature=choice_temperature,
+            choice_native_weight=choice_native_weight,
+            choice_max_tokens=choice_max_tokens,
+            letter_temperature=letter_temperature,
+            letter_pointer_weight=letter_pointer_weight,
+            letter_max_tokens=letter_max_tokens,
+        )
+        if readout == "choice" and inference_options is not None:
+            raise ValueError("inference_options apply only to native readout; use choice_max_tokens")
 
         device = default_device() if device is None else device
         if device not in ("cpu", "mps", "cuda"):
@@ -203,21 +211,21 @@ def from_pretrained(
             options = replace(options, dtype=dtypes[dtype])
         if device == "mps" and options.attn is None:
             options = replace(options, attn="sdpa")
-        if readout == "letter" and options.cuda_graphs:
+        if readout == "choice" and options.cuda_graphs:
             raise ValueError("CUDA graph capture is available only for native readout")
-        if readout == "letter" and options.temperature is not None:
-            raise ValueError("JEVANY_TEMPERATURE applies to the native head; use letter_temperature")
+        if readout == "choice" and options.temperature is not None:
+            raise ValueError("JEVANY_TEMPERATURE applies to the native head; use choice_temperature")
         predictor = None
-        if readout == "letter":
+        if readout == "choice":
             from .letter_predictor import LetterReadoutPredictor
 
             predictor = LetterReadoutPredictor(
                 checkpoint=checkpoint,
                 device=device,
                 options=options,
-                temperature=1.0 if letter_temperature is None else letter_temperature,
-                pointer_weight=0.0 if letter_pointer_weight is None else letter_pointer_weight,
-                max_tokens=16_384 if letter_max_tokens is None else letter_max_tokens,
+                temperature=choice_options.temperature,
+                native_weight=choice_options.native_weight,
+                max_tokens=choice_options.max_tokens,
             )
             loaded = predictor.checkpoint
         else:
diff --git a/jevany/serve.py b/jevany/serve.py
index a93d798..49944ff 100644
--- a/jevany/serve.py
+++ b/jevany/serve.py
@@ -3,7 +3,7 @@
 """FastAPI server for prefill-only decisions.
 
 Run: uv run --extra serve python -m jevany.serve --run runs/rlcr --port 8008
-Use ``--readout letter`` for the training-free option-letter deployment path.
+Use ``--readout choice`` for the training-free choice-token deployment path.
 
 TypeSafe-compatible: POST /v1/systemone and GET /v1/models (no auth). JEVANY_PREFIX_CACHE /
 JEVANY_PREFIX_MIN_TOKENS size the state-prefix cache; JEVANY_DATE_FACTS=1 enables deterministic date preprocessing.
@@ -18,7 +18,13 @@
 from .api import SystemOneRequest, with_date_facts
 from .checkpoint import LoadOptions, add_placement_arguments, load_options_from_args
 from .inference import InferenceOptions, add_inference_arguments, inference_options_from_args
-from .readout import LetterReadoutOptions, add_readout_arguments, letter_options_from_args
+from .readout import (
+    ChoiceReadoutOptions,
+    LetterReadoutOptions,
+    add_readout_arguments,
+    choice_options_from_args,
+    normalize_readout,
+)
 from .runtime import DEFAULT_CHECKPOINT, DecisionRuntime, JevModel
 DATE_FACTS = os.environ.get("JEVANY_DATE_FACTS", "0") == "1"
 MEDIA_ROOT = os.environ.get("JEVANY_MEDIA_ROOT")
@@ -148,7 +154,8 @@ def create_app(
     model: JevModel | None = None, device: str | None = None,
     dtype: str | None = None, model_name: str | None = None,
     options: LoadOptions | None = None, inference_options: InferenceOptions | None = None,
-    readout: str = "native", letter_options: LetterReadoutOptions | None = None,
+    readout: str = "native", choice_options: ChoiceReadoutOptions | None = None,
+    letter_options: LetterReadoutOptions | None = None,
 ) -> FastAPI:
     """Build an isolated app, loading one checkpoint during ASGI startup.
 
@@ -156,18 +163,21 @@ def create_app(
     and cache with Python callers. Loading options cannot accompany an injected
     model. Each worker loads its own full model; use one worker per device.
     """
-    if model is not None and (readout != "native" or letter_options is not None or any(
+    readout = normalize_readout(readout)
+    if choice_options is not None and letter_options is not None:
+        raise ValueError("give at most one of choice_options and legacy letter_options")
+    if letter_options is not None:
+        choice_options = letter_options.as_choice()
+    if model is not None and (readout != "native" or choice_options is not None or any(
         value is not None for value in (checkpoint, device, dtype, model_name, options, inference_options)
     )):
         raise ValueError("pass either a loaded model or checkpoint loading options")
-    if readout not in ("native", "letter"):
-        raise ValueError("readout must be native or letter")
-    if readout == "native" and letter_options is not None:
-        raise ValueError("letter_options require readout='letter'")
-    if readout == "letter" and inference_options is not None:
-        raise ValueError("inference_options apply only to native readout; use letter_options.max_tokens")
-    if readout == "letter" and letter_options is None:
-        letter_options = LetterReadoutOptions()
+    if readout == "native" and choice_options is not None:
+        raise ValueError("choice_options require readout='choice'")
+    if readout == "choice" and inference_options is not None:
+        raise ValueError("inference_options apply only to native readout; use choice_options.max_tokens")
+    if readout == "choice" and choice_options is None:
+        choice_options = ChoiceReadoutOptions()
 
     @asynccontextmanager
     async def lifespan(application: FastAPI):
@@ -175,11 +185,11 @@ async def lifespan(application: FastAPI):
             local = model
         else:
             settings = ({
-                "readout": "letter",
-                "letter_temperature": letter_options.temperature,
-                "letter_pointer_weight": letter_options.pointer_weight,
-                "letter_max_tokens": letter_options.max_tokens,
-            } if letter_options is not None else {
+                "readout": "choice",
+                "choice_temperature": choice_options.temperature,
+                "choice_native_weight": choice_options.native_weight,
+                "choice_max_tokens": choice_options.max_tokens,
+            } if choice_options is not None else {
                 "inference_options": inference_options,
             })
             local = JevModel.from_pretrained(
@@ -221,15 +231,15 @@ def main(argv=None):
     ap.add_argument("--port", type=int, default=8008)
     a = ap.parse_args(argv)
     try:
-        letter_options = letter_options_from_args(a)
+        choice_options = choice_options_from_args(a)
     except ValueError as error:
         ap.error(str(error))
-    if letter_options is not None and (
+    if choice_options is not None and (
         a.cuda_graphs or a.cuda_graph_max_tokens is not None
         or any(getattr(a, item.name) is not None for item in fields(InferenceOptions))
     ):
-        ap.error("CUDA graph and native inference-limit flags cannot be used with --readout letter; "
-                 "use --letter-max-tokens")
+        ap.error("CUDA graph and native inference-limit flags cannot be used with --readout choice; "
+                 "use --choice-max-tokens")
     options = load_options_from_args(a)
     if a.cuda_graphs or a.cuda_graph_max_tokens is not None:
         options = options or LoadOptions.from_env()
@@ -239,8 +249,8 @@ def main(argv=None):
     application = create_app(a.run, device=a.device, dtype=a.dtype, model_name=a.model_name,
                              options=options,
                              inference_options=(inference_options_from_args(a)
-                                                if letter_options is None else None),
-                             readout=a.readout, letter_options=letter_options)
+                                                if choice_options is None else None),
+                             readout=normalize_readout(a.readout), choice_options=choice_options)
     import uvicorn
     uvicorn.run(application, host=a.host, port=a.port)
 
diff --git a/tests/test_playground_workflow.py b/tests/test_playground_workflow.py
index 46cbf36..9058463 100644
--- a/tests/test_playground_workflow.py
+++ b/tests/test_playground_workflow.py
@@ -140,12 +140,19 @@ def test_connecting_reports_identity_and_capabilities_from_v1_models(app, endpoi
         app.set_images(True)
 
 
-def test_playground_displays_letter_deployment_readout(app, endpoint):
+def test_playground_displays_choice_deployment_readout(app, endpoint):
+    endpoint.models[0]["readout"] = "choice"
+    assert app.connect(endpoint.url)["served"]["readout"] == "choice"
+    source = (Path(__file__).parents[1] / "jevany" / "demos" / "static" / "app.js").read_text()
+    assert 'served.readout === "choice" || served.readout === "letter"' in source
+    assert 'readout ? `${readout} readout`' in source
+
+
+def test_playground_labels_legacy_letter_descriptor_as_choice(app, endpoint):
     endpoint.models[0]["readout"] = "letter"
     assert app.connect(endpoint.url)["served"]["readout"] == "letter"
     source = (Path(__file__).parents[1] / "jevany" / "demos" / "static" / "app.js").read_text()
-    assert 'served.readout === "letter" ? "letter"' in source
-    assert 'readout ? `${readout} readout`' in source
+    assert 'served.readout === "choice" || served.readout === "letter"' in source
 
 
 def test_playground_accepts_older_model_descriptors_without_readout(app, endpoint):
diff --git a/tests/test_readout_integration.py b/tests/test_readout_integration.py
index 6f8efcf..dee9c36 100644
--- a/tests/test_readout_integration.py
+++ b/tests/test_readout_integration.py
@@ -5,12 +5,14 @@
 
 import pytest
 
+from jevany import ChoiceReadoutOptions
 from jevany.api import Choice, Noul, Score, SystemOneRequest
 from jevany.inference import InferenceOptions
 from jevany.letter_runtime import LetterDecisionRuntime, _unlabelled_record
 from jevany.readout import (
     LetterReadoutOptions,
     add_readout_arguments,
+    choice_options_from_args,
     letter_options_from_args,
 )
 
@@ -34,15 +36,18 @@ def __init__(self):
             },
             device_map=None, devices=["cpu"], backbone_adapter="test", temperature=1.2,
         )
+        self.native_model = self.pointer_model
         self.device = "cpu"
         self.temperature = 1.0
         self.pointer_weight = 0.25
+        self.native_weight = 0.25
         self.max_tokens = 2048
         self.effective_max_tokens = 2048
         self.tokenizer = FakeTokenizer()
         self.provenance = {
             "method": "exact option-letter alias projection",
             "adapter_applied": True,
+            "native_weight": 0.25,
             "pointer_weight": 0.25,
         }
         self.last_record = None
@@ -72,25 +77,38 @@ def decision_request():
     )
 
 
-def test_letter_cli_options_are_shared_and_native_rejects_letter_flags():
+def test_choice_cli_is_public_and_legacy_letter_flags_remain_compatible():
     parser = argparse.ArgumentParser()
     add_readout_arguments(parser)
-    assert letter_options_from_args(parser.parse_args([])) is None
-    actual = letter_options_from_args(parser.parse_args([
+    help_text = parser.format_help()
+    assert "--readout {native,choice}" in help_text
+    assert "--choice-native-weight" in help_text
+    assert "--letter-temperature" not in help_text
+    assert choice_options_from_args(parser.parse_args([])) is None
+    actual = choice_options_from_args(parser.parse_args([
+        "--readout", "choice", "--choice-temperature", "1.5",
+        "--choice-native-weight", "0.25", "--choice-max-tokens", "2048",
+    ]))
+    assert actual == ChoiceReadoutOptions(temperature=1.5, native_weight=0.25, max_tokens=2048)
+    legacy = letter_options_from_args(parser.parse_args([
         "--readout", "letter", "--letter-temperature", "1.5",
         "--letter-pointer-weight", "0.25", "--letter-max-tokens", "2048",
     ]))
-    assert actual == LetterReadoutOptions(temperature=1.5, pointer_weight=0.25, max_tokens=2048)
-    with pytest.raises(ValueError, match="require --readout letter"):
-        letter_options_from_args(parser.parse_args(["--letter-temperature", "2"]))
+    assert legacy == LetterReadoutOptions(temperature=1.5, pointer_weight=0.25, max_tokens=2048)
+    with pytest.raises(ValueError, match="require --readout choice"):
+        choice_options_from_args(parser.parse_args(["--letter-temperature", "2"]))
+    with pytest.raises(ValueError, match="at most one"):
+        choice_options_from_args(parser.parse_args([
+            "--readout", "choice", "--choice-temperature", "1", "--letter-temperature", "1",
+        ]))
     for arguments, message in [
-        (["--readout", "letter", "--letter-temperature", "nan"], "finite"),
-        (["--readout", "letter", "--letter-temperature", "0"], "positive"),
-        (["--readout", "letter", "--letter-pointer-weight", "1.1"], r"\[0, 1\]"),
-        (["--readout", "letter", "--letter-max-tokens", "1"], ">= 2"),
+        (["--readout", "choice", "--choice-temperature", "nan"], "finite"),
+        (["--readout", "choice", "--choice-temperature", "0"], "positive"),
+        (["--readout", "choice", "--choice-native-weight", "1.1"], r"\[0, 1\]"),
+        (["--readout", "choice", "--choice-max-tokens", "1"], ">= 2"),
     ]:
         with pytest.raises(ValueError, match=message):
-            letter_options_from_args(parser.parse_args(arguments))
+            choice_options_from_args(parser.parse_args(arguments))
 
 
 def test_letter_runtime_maps_requests_and_reports_effective_readout(decision_request):
@@ -107,12 +125,14 @@ def test_letter_runtime_maps_requests_and_reports_effective_readout(decision_req
     assert predictor.last_record["state"] == {"signal": "green"}
     assert [q["label"] for q in predictor.last_record["questions"].values()] == ["left", False, 0]
     description = runtime.describe()
-    assert description["readout"] == "letter"
-    assert description["letter_readout"]["pointer_weight"] == 0.25
-    assert description["letter_readout"]["pointer_temperature"] == 1.2
+    assert description["readout"] == "choice"
+    assert description["choice_readout"]["native_weight"] == 0.25
+    assert description["choice_readout"]["pointer_weight"] == 0.25
+    assert description["choice_readout"]["native_temperature"] == 1.2
+    assert description["letter_readout"] == description["choice_readout"]
     assert description["limits"] == {
         "state_tokens": 2048, "branch_tokens": 2048,
-        "packed_tokens": 2048, "choices": 26,
+        "packed_tokens": 2048, "choices": 52,
     }
     assert description["capabilities"]["media_types"] == []
     assert not description["prefix_cache"]["enabled"]
@@ -137,7 +157,7 @@ def test_unlabelled_record_adds_only_encoder_placeholders(decision_request):
     assert record["questions"]["score"]["label"] == 0
 
 
-def test_public_loader_dispatches_to_letter_predictor(monkeypatch):
+def test_public_loader_dispatches_choice_settings_to_predictor(monkeypatch):
     from jevany import letter_predictor
     from jevany.runtime import JevModel
 
@@ -152,34 +172,61 @@ def load(**kwargs):
     monkeypatch.setattr(letter_predictor, "LetterReadoutPredictor", load)
     local = JevModel.from_pretrained(
         "owner/checkpoint", device="cpu", model_name="letter-model",
-        readout="letter", letter_temperature=1.5,
-        letter_pointer_weight=0.25, letter_max_tokens=2048,
+        readout="choice", choice_temperature=1.5,
+        choice_native_weight=0.25, choice_max_tokens=2048,
     )
     assert isinstance(local.runtime, LetterDecisionRuntime)
     assert seen["checkpoint"] == "owner/checkpoint"
     assert seen["temperature"] == 1.5
-    assert seen["pointer_weight"] == 0.25
+    assert seen["native_weight"] == 0.25
     assert seen["max_tokens"] == 2048
-    with pytest.raises(ValueError, match="require readout='letter'"):
-        JevModel.from_pretrained("unused", device="cpu", letter_temperature=2)
+    with pytest.raises(ValueError, match="require --readout choice"):
+        JevModel.from_pretrained("unused", device="cpu", choice_temperature=2)
     with pytest.raises(ValueError, match="only to native readout"):
         JevModel.from_pretrained(
-            "unused", device="cpu", readout="letter",
+            "unused", device="cpu", readout="choice",
             inference_options=InferenceOptions(),
         )
 
 
+def test_public_loader_accepts_legacy_letter_api(monkeypatch):
+    from jevany import letter_predictor
+    from jevany.runtime import JevModel
+
+    predictor = FakeLetterPredictor()
+    predictor.checkpoint.path = "/tmp/checkpoint"
+    seen = {}
+
+    def load(**kwargs):
+        seen.update(kwargs)
+        return predictor
+
+    monkeypatch.setattr(letter_predictor, "LetterReadoutPredictor", load)
+    local = JevModel.from_pretrained(
+        "owner/checkpoint", device="cpu", model_name="letter-model",
+        readout="letter", letter_temperature=1.5,
+        letter_pointer_weight=0.25, letter_max_tokens=2048,
+    )
+
+    assert isinstance(local.runtime, LetterDecisionRuntime)
+    assert seen["temperature"] == 1.5
+    assert seen["native_weight"] == 0.25
+    assert seen["max_tokens"] == 2048
+
+
 def test_create_app_rejects_cross_readout_settings():
     pytest.importorskip("fastapi")
     from jevany.serve import create_app
 
-    with pytest.raises(ValueError, match="require readout='letter'"):
-        create_app(readout="native", letter_options=LetterReadoutOptions())
+    with pytest.raises(ValueError, match="require readout='choice'"):
+        create_app(readout="native", choice_options=ChoiceReadoutOptions())
     with pytest.raises(ValueError, match="only to native readout"):
-        create_app(readout="letter", inference_options=InferenceOptions())
+        create_app(readout="choice", inference_options=InferenceOptions())
+    # The old programmatic spelling still builds the same application path.
+    assert create_app(readout="letter", letter_options=LetterReadoutOptions())
 
 
-def test_decide_cli_forwards_letter_settings(tmp_path, monkeypatch, capsys):
+def test_decide_cli_forwards_choice_settings(tmp_path, monkeypatch, capsys):
     from jevany.cli import decide_main
     from jevany.runtime import JevModel
 
@@ -207,21 +254,21 @@ def load(*args, **kwargs):
     monkeypatch.setattr(JevModel, "from_pretrained", load)
     decide_main([
         str(source), "--checkpoint", "owner/checkpoint", "--device", "cpu",
-        "--readout", "letter", "--letter-temperature", "1.5",
-        "--letter-pointer-weight", "0.25", "--letter-max-tokens", "2048",
+        "--readout", "choice", "--choice-temperature", "1.5",
+        "--choice-native-weight", "0.25", "--choice-max-tokens", "2048",
     ])
     assert json.loads(capsys.readouterr().out)["answers"]["choice"]["choice"] == "right"
     assert seen["args"] == ("owner/checkpoint",)
-    assert seen["kwargs"]["readout"] == "letter"
-    assert seen["kwargs"]["letter_temperature"] == 1.5
-    assert seen["kwargs"]["letter_pointer_weight"] == 0.25
-    assert seen["kwargs"]["letter_max_tokens"] == 2048
+    assert seen["kwargs"]["readout"] == "choice"
+    assert seen["kwargs"]["choice_temperature"] == 1.5
+    assert seen["kwargs"]["choice_native_weight"] == 0.25
+    assert seen["kwargs"]["choice_max_tokens"] == 2048
     assert "inference_options" not in seen["kwargs"]
     with pytest.raises(SystemExit):
         decide_main([str(source), "--letter-temperature", "2"])
 
 
-def test_eval_cli_selects_letter_predictor_and_records_provenance(tmp_path, monkeypatch, capsys):
+def test_eval_cli_selects_choice_predictor_and_records_provenance(tmp_path, monkeypatch, capsys):
     from jevany import benchmark, letter_predictor
 
     data = tmp_path / "data.jsonl"
@@ -237,8 +284,9 @@ def test_eval_cli_selects_letter_predictor_and_records_provenance(tmp_path, monk
 
     class Predictor:
         temperature = 1.5
+        native_weight = 0.25
         pointer_weight = 0.25
-        pointer_model = SimpleNamespace(temperature=1.2)
+        native_model = SimpleNamespace(temperature=1.2)
         provenance = {"method": "exact option-letter alias projection"}
 
         def __init__(self, **kwargs):
@@ -255,18 +303,20 @@ def evaluate(records, predictor, directory, **kwargs):
     monkeypatch.setattr(letter_predictor, "LetterReadoutPredictor", Predictor)
     benchmark.main([
         "--run", "owner/checkpoint", "--data", str(data), "--out", str(output),
-        "--device", "cpu", "--readout", "letter", "--letter-temperature", "1.5",
-        "--letter-pointer-weight", "0.25", "--letter-max-tokens", "2048",
+        "--device", "cpu", "--readout", "choice", "--choice-temperature", "1.5",
+        "--choice-native-weight", "0.25", "--choice-max-tokens", "2048",
     ])
     assert json.loads(capsys.readouterr().out)["objective"] == 0.0
     assert seen["checkpoint"] == "owner/checkpoint"
     assert seen["temperature"] == 1.5
-    assert seen["pointer_weight"] == 0.25
+    assert seen["native_weight"] == 0.25
     assert seen["max_tokens"] == 2048
     report = json.loads((output / "report.json").read_text())
-    assert report["readout"] == "letter"
-    assert report["letter_readout"] == {
-        **Predictor.provenance, "pointer_temperature": 1.2,
+    assert report["readout"] == "choice"
+    assert report["choice_readout"] == {
+        **Predictor.provenance, "native_temperature": 1.2, "pointer_temperature": 1.2,
     }
+    assert report["letter_readout"] == report["choice_readout"]
     assert report["calibration_applied"] is True
+    assert report["calibration"]["native_temperature"] == 1.2
     assert report["calibration"]["pointer_temperature"] == 1.2

From 97e242fe7e5a4b18f017296eb2e12c52d9bf6c48 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 00:44:36 +0000
Subject: [PATCH 05/10] Add reproducible choice readout result builder

---
 scripts/build_choice_readout_results.py    | 852 +++++++++++++++++++++
 tests/test_build_choice_readout_results.py | 304 ++++++++
 2 files changed, 1156 insertions(+)
 create mode 100644 scripts/build_choice_readout_results.py
 create mode 100644 tests/test_build_choice_readout_results.py

diff --git a/scripts/build_choice_readout_results.py b/scripts/build_choice_readout_results.py
new file mode 100644
index 0000000..8eb6fcc
--- /dev/null
+++ b/scripts/build_choice_readout_results.py
@@ -0,0 +1,852 @@
+#!/usr/bin/env python3
+"""Validate, summarize, and plot the five-run choice-readout matrix.
+
+The input directory must contain ``base4``, ``pointer4``, ``direct4``,
+``base27``, and ``pointer27`` subdirectories.  Each subdirectory contains the
+``manifest.json`` emitted by ``evaluate_choice_readout_matrix.py`` and the
+``ensemble.json`` emitted by ``fit_choice_ensemble.py``.
+
+The builder fails closed on incomplete panels, component/count drift, or
+cross-run dataset-hash drift.  It never substitutes partial coverage or a
+media-stripped score for JevJudge full.
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import hashlib
+import json
+import math
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_EXTERNAL = ROOT / "results/external-zero-shot-v1.json"
+DEFAULT_JSON_OUT = ROOT / "results/choice-readout-v2.json"
+DEFAULT_SVG_OUT = ROOT / "docs/choice-readout-results.svg"
+
+INK = "#213248"
+MUTED = "#64748B"
+RULE = "#E4E9EF"
+CHOICE = "#278577"
+NATIVE = "#8493A6"
+TUNED = "#165F55"
+EXTERNAL = "#A17BB7"
+TYPESAFE = "#C08A42"
+
+RUN_SPECS = (
+    {
+        "key": "base4",
+        "family": "Qwen3.5-4B",
+        "weights": "frozen_base",
+        "native_readout": None,
+        "native_decision_mode": None,
+    },
+    {
+        "key": "pointer4",
+        "family": "Qwen3.5-4B",
+        "weights": "jevany_sft",
+        "native_readout": "pointer",
+        "native_decision_mode": "pointer",
+    },
+    {
+        "key": "direct4",
+        "family": "Qwen3.5-4B",
+        "weights": "jevany_sft",
+        "native_readout": "direct-token",
+        "native_decision_mode": "lm_token",
+    },
+    {
+        "key": "base27",
+        "family": "Qwen3.8-27B",
+        "weights": "frozen_base",
+        "native_readout": None,
+        "native_decision_mode": None,
+    },
+    {
+        "key": "pointer27",
+        "family": "Qwen3.8-27B",
+        "weights": "jevany_sft",
+        "native_readout": "pointer",
+        "native_decision_mode": "pointer",
+    },
+)
+
+DATASETS = {
+    "transfer_calibration": {
+        "label": "Transfer-v9 development",
+        "role": "selection_only",
+        "records": 1_264,
+        "questions": 1_264,
+        "headline_n": 1_046,
+    },
+    "transfer_test": {
+        "label": "Transfer-v9 test",
+        "role": "held_out_evaluation",
+        "records": 1_264,
+        "questions": 1_264,
+        "headline_n": 1_046,
+    },
+    "typed_test": {
+        "label": "Typed Decisions test",
+        "role": "held_out_external_evaluation",
+        "records": 400,
+        "questions": 2_000,
+        "headline_n": 2_000,
+    },
+    "jevbench_public": {
+        "label": "JevBench public development",
+        "role": "public_diagnostic_not_used_for_selection",
+        "records": 231,
+        "questions": 231,
+        "headline_n": 231,
+    },
+    "jevjudge_text": {
+        "label": "JevJudge text",
+        "role": "held_out_external_evaluation",
+        "records": 724,
+        "questions": 724,
+        "headline_n": 724,
+    },
+}
+
+ZERO_SHOT_CONFIGS = (
+    "choice_t1",
+    "native_shipped",
+    "fixed_blend_0_5",
+)
+TUNED_CONFIGS = (
+    "transfer_dev_calibrated_choice",
+    "transfer_dev_tuned_blend",
+)
+BASE_CONFIGS = {"choice_t1", "transfer_dev_calibrated_choice"}
+CHECKPOINT_CONFIGS = BASE_CONFIGS | {
+    "native_shipped",
+    "fixed_blend_0_5",
+    "transfer_dev_tuned_blend",
+}
+
+
+def _read_object(path: Path) -> dict:
+    try:
+        value = json.loads(path.read_text(encoding="utf-8"))
+    except FileNotFoundError as error:
+        raise ValueError(f"missing input artifact: {path}") from error
+    except json.JSONDecodeError as error:
+        raise ValueError(f"invalid JSON artifact: {path}: {error}") from error
+    if not isinstance(value, dict):
+        raise ValueError(f"JSON artifact must be an object: {path}")
+    return value
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def _display_path(path: Path) -> str:
+    resolved = path.resolve()
+    try:
+        return str(resolved.relative_to(ROOT.resolve()))
+    except ValueError:
+        return str(resolved)
+
+
+def _finite(value: Any) -> bool:
+    return (
+        not isinstance(value, bool)
+        and isinstance(value, (int, float))
+        and math.isfinite(value)
+    )
+
+
+def _probability(value: Any, context: str) -> float:
+    if not _finite(value) or not 0 <= value <= 1:
+        raise ValueError(f"{context} must be a finite number in [0, 1]")
+    return float(value)
+
+
+def _positive(value: Any, context: str) -> float:
+    if not _finite(value) or value <= 0:
+        raise ValueError(f"{context} must be a finite positive number")
+    return float(value)
+
+
+def _metric(metric: Any, expected_n: int, context: str) -> dict:
+    if not isinstance(metric, dict):
+        raise ValueError(f"{context} must be an object")
+    if metric.get("n") != expected_n:
+        raise ValueError(
+            f"{context} expected n={expected_n}, got {metric.get('n')!r}"
+        )
+    correct = metric.get("correct")
+    if isinstance(correct, bool) or not isinstance(correct, int) or not 0 <= correct <= expected_n:
+        raise ValueError(f"{context}.correct must be an integer in [0, {expected_n}]")
+    accuracy = _probability(metric.get("accuracy"), f"{context}.accuracy")
+    if not math.isclose(accuracy, correct / expected_n, rel_tol=0, abs_tol=1e-12):
+        raise ValueError(f"{context}.accuracy does not equal correct / n")
+    for key in (
+        "cross_entropy_from_gold",
+        "gold_entropy",
+        "kl_from_gold",
+        "brier",
+        "ece",
+        "mean_confidence",
+    ):
+        if key in metric and not _finite(metric[key]):
+            raise ValueError(f"{context}.{key} must be finite")
+    return copy.deepcopy(metric)
+
+
+def _manifest_component_accuracy(report: dict, component: str, expected_n: int, context: str) -> float:
+    try:
+        clean = report["components"][component]["clean"]
+    except (KeyError, TypeError) as error:
+        raise ValueError(f"{context}: missing {component} clean report") from error
+    if clean.get("n") != expected_n:
+        raise ValueError(
+            f"{context}: {component} expected clean n={expected_n}, "
+            f"got {clean.get('n')!r}"
+        )
+    return _probability(clean.get("acc"), f"{context}.{component}.clean.acc")
+
+
+def _validate_source(spec: dict, manifest: dict, context: str) -> None:
+    source = manifest.get("source")
+    predictor = manifest.get("predictor")
+    if not isinstance(source, dict) or not isinstance(predictor, dict):
+        raise ValueError(f"{context}: missing source or predictor provenance")
+    is_base = spec["weights"] == "frozen_base"
+    if is_base:
+        if not source.get("base") or source.get("checkpoint") is not None:
+            raise ValueError(f"{context}: frozen-base run must use source.base only")
+        if predictor.get("adapter_applied") is not False:
+            raise ValueError(f"{context}: frozen-base run unexpectedly applied an adapter")
+        if manifest.get("checkpoint_artifacts") is not None:
+            raise ValueError(f"{context}: frozen-base run has checkpoint artifacts")
+    else:
+        if not source.get("checkpoint") or source.get("base") is not None:
+            raise ValueError(f"{context}: checkpoint run must use source.checkpoint only")
+        if predictor.get("adapter_applied") is not True:
+            raise ValueError(f"{context}: checkpoint run did not apply its adapter")
+        artifacts = manifest.get("checkpoint_artifacts")
+        if not isinstance(artifacts, dict) or not isinstance(artifacts.get("files"), dict):
+            raise ValueError(f"{context}: checkpoint artifact hashes are missing")
+    if predictor.get("native_decision_mode") != spec["native_decision_mode"]:
+        raise ValueError(
+            f"{context}: expected native_decision_mode={spec['native_decision_mode']!r}, "
+            f"got {predictor.get('native_decision_mode')!r}"
+        )
+
+
+def _load_run(run_root: Path, spec: dict) -> dict:
+    directory = run_root / spec["key"]
+    manifest_path = directory / "manifest.json"
+    ensemble_path = directory / "ensemble.json"
+    manifest = _read_object(manifest_path)
+    ensemble = _read_object(ensemble_path)
+    context = spec["key"]
+
+    model = manifest.get("model")
+    if not isinstance(model, str) or not model.strip():
+        raise ValueError(f"{context}: manifest model must be a non-empty string")
+    if ensemble.get("model") != model:
+        raise ValueError(f"{context}: manifest and ensemble model names differ")
+    _validate_source(spec, manifest, context)
+
+    expected_components = {"choice"}
+    if spec["native_readout"] is not None:
+        expected_components.add("native")
+    reports = manifest.get("reports")
+    datasets = ensemble.get("datasets")
+    if not isinstance(reports, dict) or not isinstance(datasets, dict):
+        raise ValueError(f"{context}: missing report or ensemble datasets")
+    if set(DATASETS) - set(reports) or set(DATASETS) - set(datasets):
+        raise ValueError(f"{context}: one or more required datasets are missing")
+
+    expected_configs = BASE_CONFIGS if spec["native_readout"] is None else CHECKPOINT_CONFIGS
+    summarized = {}
+    for dataset, expected in DATASETS.items():
+        report = reports[dataset]
+        if report.get("records") != expected["records"]:
+            raise ValueError(
+                f"{context}/{dataset}: expected {expected['records']} records, "
+                f"got {report.get('records')!r}"
+            )
+        if report.get("questions") != expected["questions"]:
+            raise ValueError(
+                f"{context}/{dataset}: expected {expected['questions']} questions, "
+                f"got {report.get('questions')!r}"
+            )
+        components = set(report.get("components", {}))
+        executed = set(report.get("components_executed", components))
+        if components != expected_components or executed != expected_components:
+            raise ValueError(
+                f"{context}/{dataset}: expected components {sorted(expected_components)}, "
+                f"got reports={sorted(components)}, executed={sorted(executed)}"
+            )
+        component_accuracy = {
+            component: _manifest_component_accuracy(
+                report, component, expected["headline_n"], f"{context}/{dataset}"
+            )
+            for component in sorted(expected_components)
+        }
+
+        configurations = datasets[dataset]
+        if not isinstance(configurations, dict) or set(configurations) != expected_configs:
+            raise ValueError(
+                f"{context}/{dataset}: expected ensemble configs "
+                f"{sorted(expected_configs)}, got "
+                f"{sorted(configurations) if isinstance(configurations, dict) else configurations!r}"
+            )
+        checked = {
+            name: _metric(
+                values,
+                expected["headline_n"],
+                f"{context}/{dataset}/{name}",
+            )
+            for name, values in configurations.items()
+        }
+        expected_excluded = expected["questions"] - expected["headline_n"]
+        for name, values in checked.items():
+            if values.get("excluded_non_headline_rows") != expected_excluded:
+                raise ValueError(
+                    f"{context}/{dataset}/{name}: expected "
+                    f"excluded_non_headline_rows={expected_excluded}"
+                )
+        if not math.isclose(
+            checked["choice_t1"]["accuracy"], component_accuracy["choice"],
+            rel_tol=0, abs_tol=1e-12,
+        ):
+            raise ValueError(f"{context}/{dataset}: choice component and ensemble are not aligned")
+        if checked["choice_t1"]["correct"] != checked["transfer_dev_calibrated_choice"]["correct"]:
+            raise ValueError(
+                f"{context}/{dataset}: temperature-only calibration changed hard decisions"
+            )
+        if "native" in expected_components and not math.isclose(
+            checked["native_shipped"]["accuracy"], component_accuracy["native"],
+            rel_tol=0, abs_tol=1e-12,
+        ):
+            raise ValueError(f"{context}/{dataset}: native component and ensemble are not aligned")
+
+        summarized[dataset] = {
+            "zero_shot": {
+                key: checked[key] for key in ZERO_SHOT_CONFIGS if key in checked
+            },
+            "transfer_dev_tuned": {
+                key: checked[key] for key in TUNED_CONFIGS if key in checked
+            },
+        }
+
+    protocol = ensemble.get("protocol")
+    selected = ensemble.get("selected")
+    if not isinstance(protocol, dict) or protocol.get("selection_rows") != 1_046:
+        raise ValueError(f"{context}: ensemble must select on 1,046 Transfer-dev rows")
+    if not isinstance(selected, dict):
+        raise ValueError(f"{context}: missing selected ensemble settings")
+    choice_temperature = _positive(
+        selected.get("choice_temperature"), f"{context}.choice_temperature"
+    )
+    blend_temperature = _positive(
+        selected.get("blend_temperature"), f"{context}.blend_temperature"
+    )
+    native_weight = _probability(
+        selected.get("native_weight"), f"{context}.native_weight"
+    )
+    if spec["native_readout"] is None and native_weight != 0:
+        raise ValueError(f"{context}: frozen base selected a native weight")
+
+    dataset_hashes = manifest.get("datasets")
+    runtime = manifest.get("runtime")
+    if not isinstance(dataset_hashes, dict) or not dataset_hashes:
+        raise ValueError(f"{context}: dataset hashes are missing")
+    if not isinstance(runtime, dict) or not runtime.get("code_revision"):
+        raise ValueError(f"{context}: runtime/code revision is missing")
+
+    return {
+        "key": spec["key"],
+        "model": model,
+        "family": spec["family"],
+        "weights": spec["weights"],
+        "native_readout": spec["native_readout"],
+        "artifacts": {
+            "manifest": {
+                "path": _display_path(manifest_path),
+                "sha256": _sha256(manifest_path),
+            },
+            "ensemble": {
+                "path": _display_path(ensemble_path),
+                "sha256": _sha256(ensemble_path),
+            },
+        },
+        "source": copy.deepcopy(manifest.get("source")),
+        "checkpoint_artifacts": copy.deepcopy(manifest.get("checkpoint_artifacts")),
+        "base_loading": copy.deepcopy(manifest.get("base_loading")),
+        "predictor": copy.deepcopy(manifest.get("predictor")),
+        "runtime": copy.deepcopy(runtime),
+        "dataset_hashes": copy.deepcopy(dataset_hashes),
+        "evaluation_protocol": copy.deepcopy(manifest.get("protocol")),
+        "ensemble_protocol": copy.deepcopy(protocol),
+        "selected": {
+            **copy.deepcopy(selected),
+            "choice_temperature": choice_temperature,
+            "blend_temperature": blend_temperature,
+            "native_weight": native_weight,
+        },
+        "datasets": summarized,
+    }
+
+
+def _find_model(rows: Any, name: str, context: str) -> dict:
+    if not isinstance(rows, list):
+        raise ValueError(f"{context}: models must be a list")
+    matches = [row for row in rows if isinstance(row, dict) and row.get("model") == name]
+    if len(matches) != 1:
+        raise ValueError(f"{context}: expected exactly one {name!r} row")
+    _probability(matches[0].get("accuracy"), f"{context}/{name}.accuracy")
+    return copy.deepcopy(matches[0])
+
+
+def _load_external(path: Path) -> dict:
+    external = _read_object(path)
+    typed = external.get("typed_decisions")
+    text = external.get("jevjudge_text")
+    if not isinstance(typed, dict) or not isinstance(text, dict):
+        raise ValueError("external artifact is missing Typed Decisions or JevJudge text")
+
+    typed_rows = typed.get("models")
+    if not isinstance(typed_rows, list):
+        raise ValueError("external Typed Decisions models must be a list")
+    published = [
+        row for row in typed_rows
+        if isinstance(row, dict)
+        and row.get("kind") == "published_only"
+        and _finite(row.get("accuracy"))
+    ]
+    if not published:
+        raise ValueError("external artifact has no published Typed Decisions baseline")
+    typed_top = copy.deepcopy(max(published, key=lambda row: (row["accuracy"], row["model"])))
+    _probability(typed_top.get("accuracy"), "Typed published top accuracy")
+    typed_typesafe = _find_model(
+        typed_rows, "Jev 1.13 (OpenRouter)", "external/typed_decisions"
+    )
+
+    text_rows = text.get("models")
+    text_typesafe = _find_model(
+        text_rows, "Jev 1.13 (OpenRouter)", "external/jevjudge_text"
+    )
+    kev_rows = [
+        row for row in text_rows
+        if isinstance(row, dict)
+        and row.get("kind") == "open_kev"
+        and _finite(row.get("accuracy"))
+    ] if isinstance(text_rows, list) else []
+    if not kev_rows:
+        raise ValueError("external artifact has no scored JevJudge-text Kev baseline")
+    text_kev = copy.deepcopy(max(kev_rows, key=lambda row: (row["accuracy"], row["model"])))
+    for row in (text_typesafe, text_kev):
+        if (row.get("answered"), row.get("requested")) != (724, 724):
+            raise ValueError(
+                f"external/jevjudge_text/{row['model']}: expected 724/724 coverage"
+            )
+
+    return {
+        "artifact": {
+            "path": _display_path(path),
+            "sha256": _sha256(path),
+            "artifact_version": external.get("artifact_version"),
+            "provenance": copy.deepcopy(external.get("provenance")),
+        },
+        "typed_decisions": [typed_top, typed_typesafe],
+        "jevjudge_text": [text_typesafe, text_kev],
+    }
+
+
+def build_results(run_root: Path, external_path: Path = DEFAULT_EXTERNAL) -> dict:
+    """Build a validated, JSON-serializable result artifact without writing it."""
+
+    run_root = Path(run_root)
+    runs = [_load_run(run_root, spec) for spec in RUN_SPECS]
+    if len({run["model"] for run in runs}) != len(runs):
+        raise ValueError("the five run manifests must have unique model labels")
+
+    reference_hashes = runs[0]["dataset_hashes"]
+    for run in runs[1:]:
+        if run["dataset_hashes"] != reference_hashes:
+            raise ValueError(
+                f"{run['key']}: dataset hashes differ from {runs[0]['key']}"
+            )
+    revisions = {run["runtime"].get("code_revision") for run in runs}
+    if len(revisions) != 1:
+        raise ValueError("all five runs must use one code revision")
+
+    external = _load_external(Path(external_path))
+    result_datasets = {}
+    for dataset, expected in DATASETS.items():
+        result_datasets[dataset] = {
+            **copy.deepcopy(expected),
+            "runs": [
+                {
+                    "run": run["key"],
+                    "model": run["model"],
+                    "family": run["family"],
+                    "weights": run["weights"],
+                    "native_readout": run["native_readout"],
+                    **copy.deepcopy(run["datasets"][dataset]),
+                }
+                for run in runs
+            ],
+        }
+    result_datasets["typed_test"]["external_baselines"] = copy.deepcopy(
+        external["typed_decisions"]
+    )
+    result_datasets["jevjudge_text"]["external_baselines"] = copy.deepcopy(
+        external["jevjudge_text"]
+    )
+    result_datasets["jevjudge_full"] = {
+        "label": "JevJudge full",
+        "records": 3_220,
+        "status": "unsupported",
+        "accuracy": None,
+        "reason": (
+            "Training-free choice-token readout is text-only; image/video records "
+            "are not stripped or relabeled as a full-suite result."
+        ),
+        "native_checkpoint_results": "results/external-zero-shot-v1.json#jevjudge_full",
+    }
+
+    public_runs = []
+    for run in runs:
+        public_runs.append({key: copy.deepcopy(value) for key, value in run.items() if key != "datasets"})
+
+    return {
+        "schema_version": 2,
+        "artifact": "choice-readout-v2",
+        "title": "Training-free choice-token readout evaluation",
+        "generated_by": "scripts/build_choice_readout_results.py",
+        "method": {
+            "readout": (
+                "Constrain the next token to exact one-character option IDs and "
+                "renormalize their probability mass; this can change argmax decisions."
+            ),
+            "training": "No additional training for the choice-token readout.",
+            "option_ids": "A-Z followed by a-z; at most 52 options.",
+            "calibration": (
+                "Scalar temperature changes probabilities but not argmax accuracy."
+            ),
+            "ensemble": (
+                "Native + choice configurations use log-linear/geometric pooling."
+            ),
+        },
+        "protocol_groups": {
+            "zero_shot": {
+                "meaning": (
+                    "No Typed, JevJudge, JevBench, or Transfer-test labels tune the "
+                    "readout configuration."
+                ),
+                "configurations": list(ZERO_SHOT_CONFIGS),
+            },
+            "transfer_dev_tuned": {
+                "meaning": (
+                    "Weight and additional temperature are selected only on Transfer-v9 "
+                    "development, then frozen for every displayed evaluation panel."
+                ),
+                "configurations": list(TUNED_CONFIGS),
+                "accuracy_note": (
+                    "Temperature-only calibrated choice has the same hard accuracy as "
+                    "choice_t1; only a changed blend weight can change its argmax."
+                ),
+            },
+        },
+        "expected_counts": copy.deepcopy(DATASETS),
+        "dataset_hashes": copy.deepcopy(reference_hashes),
+        "code_revision": next(iter(revisions)),
+        "source_runs": public_runs,
+        "external_source": external["artifact"],
+        "datasets": result_datasets,
+        "legacy_results": {
+            "status": "retained unchanged",
+            "artifact": "results/letter-readout-v1.json",
+        },
+    }
+
+
+def write_results(path: Path, artifact: dict) -> None:
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    payload = json.dumps(
+        artifact, indent=2, ensure_ascii=False, allow_nan=False
+    ) + "\n"
+    path.write_text(payload, encoding="utf-8")
+
+
+def _run_result(artifact: dict, dataset: str, run_key: str) -> dict:
+    matches = [
+        row for row in artifact["datasets"][dataset]["runs"]
+        if row["run"] == run_key
+    ]
+    if len(matches) != 1:
+        raise ValueError(f"{dataset}: expected exactly one {run_key} result")
+    return matches[0]
+
+
+def _plot_rows(artifact: dict, dataset: str) -> list[dict]:
+    def accuracy(run: str, group: str, config: str) -> float:
+        return _run_result(artifact, dataset, run)[group][config]["accuracy"]
+
+    rows = [
+        {
+            "label": "Frozen Qwen3.5 4B · Choice",
+            "group": "zero_shot",
+            "kind": "choice",
+            "accuracy": accuracy("base4", "zero_shot", "choice_t1"),
+        },
+        {
+            "label": "JevAny 4B Pointer · Native",
+            "group": "zero_shot",
+            "kind": "native",
+            "accuracy": accuracy("pointer4", "zero_shot", "native_shipped"),
+        },
+        {
+            "label": "JevAny 4B Pointer · Choice",
+            "group": "zero_shot",
+            "kind": "choice",
+            "accuracy": accuracy("pointer4", "zero_shot", "choice_t1"),
+        },
+        {
+            "label": "JevAny 4B Direct-Token · Native",
+            "group": "zero_shot",
+            "kind": "native",
+            "accuracy": accuracy("direct4", "zero_shot", "native_shipped"),
+        },
+        {
+            "label": "JevAny 4B Direct-Token · Choice",
+            "group": "zero_shot",
+            "kind": "choice",
+            "accuracy": accuracy("direct4", "zero_shot", "choice_t1"),
+        },
+        {
+            "label": "Frozen Qwen3.8 27B · Choice",
+            "group": "zero_shot",
+            "kind": "choice",
+            "accuracy": accuracy("base27", "zero_shot", "choice_t1"),
+        },
+        {
+            "label": "JevAny 27B Pointer · Native",
+            "group": "zero_shot",
+            "kind": "native",
+            "accuracy": accuracy("pointer27", "zero_shot", "native_shipped"),
+        },
+        {
+            "label": "JevAny 27B Pointer · Choice",
+            "group": "zero_shot",
+            "kind": "choice",
+            "accuracy": accuracy("pointer27", "zero_shot", "choice_t1"),
+        },
+        {
+            "label": "JevAny 4B Pointer · Tuned blend",
+            "group": "transfer_dev_tuned",
+            "kind": "tuned",
+            "accuracy": accuracy(
+                "pointer4", "transfer_dev_tuned", "transfer_dev_tuned_blend"
+            ),
+        },
+        {
+            "label": "JevAny 4B Direct-Token · Tuned blend",
+            "group": "transfer_dev_tuned",
+            "kind": "tuned",
+            "accuracy": accuracy(
+                "direct4", "transfer_dev_tuned", "transfer_dev_tuned_blend"
+            ),
+        },
+        {
+            "label": "JevAny 27B Pointer · Tuned blend",
+            "group": "transfer_dev_tuned",
+            "kind": "tuned",
+            "accuracy": accuracy(
+                "pointer27", "transfer_dev_tuned", "transfer_dev_tuned_blend"
+            ),
+        },
+    ]
+    baselines = artifact["datasets"][dataset]["external_baselines"]
+    for baseline in baselines:
+        is_typesafe = baseline["model"] == "Jev 1.13 (OpenRouter)"
+        label = (
+            "TypeSafe Jev 1.13"
+            if is_typesafe
+            else f"{baseline['model']} · "
+            + ("published" if dataset == "typed_test" else "open")
+        )
+        rows.append({
+            "label": label,
+            "group": "external",
+            "kind": "typesafe" if is_typesafe else "external",
+            "accuracy": baseline["accuracy"],
+            "published": dataset == "typed_test" and not is_typesafe,
+        })
+    return rows
+
+
+def render_svg(artifact: dict, output: Path) -> None:
+    """Render the compact two-panel README figure from a validated artifact."""
+
+    try:
+        import matplotlib
+    except ImportError as error:
+        raise RuntimeError("matplotlib is required to render the SVG") from error
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    from matplotlib.patches import Patch
+
+    plt.rcParams.update({
+        "font.family": ["DejaVu Sans", "sans-serif"],
+        "svg.fonttype": "none",
+        "svg.hashsalt": "jevany-choice-readout-v2",
+        "text.color": INK,
+        "hatch.linewidth": 0.7,
+    })
+    panels = (
+        ("typed_test", "Typed Decisions", "2,000 decisions · accuracy (%)"),
+        ("jevjudge_text", "JevJudge text", "724 records · accuracy (%)"),
+    )
+    fig, axes = plt.subplots(1, 2, figsize=(18, 9.3), dpi=110, facecolor="white")
+    fig.subplots_adjust(left=0.19, right=0.988, bottom=0.12, top=0.78, wspace=0.30)
+    fig.text(0.035, 0.955, "Training-free choice readout", fontsize=24, weight="bold")
+    fig.text(
+        0.035,
+        0.914,
+        "Zero-shot readouts and Transfer-dev-tuned blends · no Typed or JevJudge labels used for tuning",
+        fontsize=12.2,
+        color=MUTED,
+    )
+    fig.legend(
+        handles=[
+            Patch(facecolor=CHOICE, label="Choice T=1 · zero-shot"),
+            Patch(facecolor=NATIVE, label="Native shipped · zero-shot"),
+            Patch(facecolor=TUNED, hatch="///", label="Transfer-dev-tuned blend"),
+            Patch(facecolor=TYPESAFE, label="TypeSafe Jev"),
+            Patch(facecolor=EXTERNAL, hatch="////", label="Published / open baseline"),
+        ],
+        loc="upper right",
+        bbox_to_anchor=(0.988, 0.982),
+        ncol=3,
+        frameon=False,
+        fontsize=10.1,
+        handlelength=1.2,
+        columnspacing=1.25,
+    )
+
+    description_parts = []
+    colors = {
+        "choice": CHOICE,
+        "native": NATIVE,
+        "tuned": TUNED,
+        "external": EXTERNAL,
+        "typesafe": TYPESAFE,
+    }
+    positions = list(range(8)) + list(range(9, 12)) + list(range(13, 15))
+    for ax, (dataset, title, scope) in zip(axes, panels, strict=True):
+        rows = _plot_rows(artifact, dataset)
+        if len(rows) != len(positions):
+            raise ValueError(f"{dataset}: plot requires exactly 15 rows")
+        values = [row["accuracy"] * 100 for row in rows]
+        xmax = min(100, max(70, int(math.ceil((max(values) + 4) / 10) * 10)))
+        ax.axhspan(-0.7, 7.65, color="#F7FAFC", zorder=0)
+        ax.axhspan(8.35, 11.65, color="#EDF7F4", zorder=0)
+        ax.axhspan(12.35, 14.65, color="#FAF7FC", zorder=0)
+        for y, row, value in zip(positions, rows, values, strict=True):
+            bars = ax.barh(
+                y,
+                value,
+                height=0.64,
+                color=colors[row["kind"]],
+                edgecolor="#805A2B" if row.get("published") else "none",
+                linewidth=0.7 if row.get("published") else 0,
+                zorder=3,
+            )
+            if row["kind"] == "tuned":
+                bars[0].set_hatch("///")
+            elif row.get("published"):
+                bars[0].set_hatch("////")
+            ax.text(
+                min(value + xmax * 0.015, xmax * 0.985),
+                y,
+                f"{value:.1f}",
+                ha="right" if value > xmax * 0.91 else "left",
+                va="center",
+                fontsize=9.2,
+                color=INK,
+                weight="bold" if row["group"] != "external" else "normal",
+            )
+        ax.text(0, -0.78, "ZERO-SHOT", fontsize=9.0, color=MUTED, weight="bold")
+        ax.text(0, 8.22, "TRANSFER-DEV-TUNED", fontsize=9.0, color=TUNED, weight="bold")
+        ax.text(0, 12.22, "EXTERNAL REFERENCE", fontsize=9.0, color=MUTED, weight="bold")
+        ax.set_title(title, loc="left", fontsize=17, color=INK, weight="bold", pad=28)
+        ax.text(0, 1.015, scope, transform=ax.transAxes, fontsize=10.2, color=MUTED)
+        ax.set_xlim(0, xmax)
+        ticks = list(range(0, xmax + 1, 20))
+        ax.set_xticks(ticks)
+        ax.set_xticklabels([str(value) for value in ticks], fontsize=9.2, color=MUTED)
+        ax.set_yticks(positions)
+        ax.set_yticklabels([row["label"] for row in rows], fontsize=9.0, color=INK)
+        for tick, row in zip(ax.get_yticklabels(), rows, strict=True):
+            if row["group"] != "external":
+                tick.set_weight("bold" if row["kind"] == "tuned" else "normal")
+        ax.set_ylim(15.0, -1.1)
+        ax.tick_params(axis="x", length=0, pad=7)
+        ax.tick_params(axis="y", length=0, pad=7)
+        ax.set_axisbelow(True)
+        ax.grid(axis="x", color=RULE, linewidth=0.8)
+        for spine in ax.spines.values():
+            spine.set_visible(False)
+        ax.axvline(0, color=RULE, linewidth=1)
+        description_parts.append(
+            f"{title}: " + ", ".join(
+                f"{row['label']} {row['accuracy'] * 100:.2f}%" for row in rows
+            )
+        )
+
+    fig.text(
+        0.035,
+        0.044,
+        "JevJudge full is unsupported for this text-only readout. Choice temperature calibration is omitted here because it cannot change accuracy.",
+        fontsize=10.1,
+        color=MUTED,
+    )
+    output = Path(output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    metadata = {
+        "Date": None,
+        "Title": "Training-free choice readout accuracy",
+        "Description": " ".join(description_parts),
+    }
+    fig.savefig(output, metadata=metadata)
+    plt.close(fig)
+    output.write_text(
+        "\n".join(line.rstrip() for line in output.read_text().splitlines()) + "\n",
+        encoding="utf-8",
+    )
+
+
+def main(argv: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--run-root", type=Path, required=True)
+    parser.add_argument("--external", type=Path, default=DEFAULT_EXTERNAL)
+    parser.add_argument("--json-out", type=Path, default=DEFAULT_JSON_OUT)
+    parser.add_argument("--svg-out", type=Path, default=DEFAULT_SVG_OUT)
+    args = parser.parse_args(argv)
+
+    artifact = build_results(args.run_root, args.external)
+    write_results(args.json_out, artifact)
+    render_svg(artifact, args.svg_out)
+    print(f"Wrote {_display_path(args.json_out)} and {_display_path(args.svg_out)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_build_choice_readout_results.py b/tests/test_build_choice_readout_results.py
new file mode 100644
index 0000000..7cf760c
--- /dev/null
+++ b/tests/test_build_choice_readout_results.py
@@ -0,0 +1,304 @@
+import copy
+import json
+from pathlib import Path
+
+import pytest
+
+from scripts.build_choice_readout_results import (
+    DATASETS,
+    RUN_SPECS,
+    build_results,
+    render_svg,
+    write_results,
+)
+
+
+def _metric(n, correct):
+    accuracy = correct / n
+    return {
+        "n": n,
+        "correct": correct,
+        "accuracy": accuracy,
+        "cross_entropy_from_gold": 0.5,
+        "gold_entropy": 0.1,
+        "kl_from_gold": 0.4,
+        "brier": 0.2,
+        "ece": 0.03,
+        "mean_confidence": 0.7,
+        "cohort": "clean and knowable",
+        "excluded_non_headline_rows": 0,
+        "ece_bins": 15,
+    }
+
+
+def _write_json(path, value):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(value, indent=2) + "\n")
+
+
+def _external_fixture(path):
+    value = {
+        "artifact_version": 5,
+        "provenance": {"fixture": {"sha256": "e" * 64}},
+        "typed_decisions": {
+            "models": [
+                {
+                    "model": "Published runner-up",
+                    "kind": "published_only",
+                    "accuracy": 0.74,
+                },
+                {
+                    "model": "Published top",
+                    "kind": "published_only",
+                    "accuracy": 0.77,
+                },
+                {
+                    "model": "Jev 1.13 (OpenRouter)",
+                    "kind": "external_api",
+                    "accuracy": 0.73,
+                },
+            ]
+        },
+        "jevjudge_text": {
+            "models": [
+                {
+                    "model": "Jev 1.13 (OpenRouter)",
+                    "kind": "external_api",
+                    "accuracy": 0.65,
+                    "answered": 724,
+                    "requested": 724,
+                },
+                {
+                    "model": "Kev-4B",
+                    "kind": "open_kev",
+                    "accuracy": 0.54,
+                    "answered": 724,
+                    "requested": 724,
+                },
+                {
+                    "model": "Kev-27B",
+                    "kind": "open_kev",
+                    "accuracy": 0.64,
+                    "answered": 724,
+                    "requested": 724,
+                },
+            ]
+        },
+    }
+    _write_json(path, value)
+
+
+def _run_fixture(root, spec, ordinal):
+    is_base = spec["weights"] == "frozen_base"
+    components = ["choice"] if is_base else ["choice", "native"]
+    model = f"fixture-{spec['key']}"
+    reports = {}
+    ensemble_datasets = {}
+    for dataset_index, (dataset, expected) in enumerate(DATASETS.items()):
+        n = expected["headline_n"]
+        choice_correct = n // 2 + ordinal + dataset_index
+        native_correct = choice_correct + 1
+        choice_accuracy = choice_correct / n
+        native_accuracy = native_correct / n
+        reports[dataset] = {
+            "dataset": dataset,
+            "records": expected["records"],
+            "questions": expected["questions"],
+            "components_executed": components,
+            "components": {
+                "choice": {"clean": {"n": n, "acc": choice_accuracy}},
+            },
+        }
+        if not is_base:
+            reports[dataset]["components"]["native"] = {
+                "clean": {"n": n, "acc": native_accuracy}
+            }
+        choice = _metric(n, choice_correct)
+        choice["excluded_non_headline_rows"] = expected["questions"] - n
+        calibrated = copy.deepcopy(choice)
+        calibrated["cross_entropy_from_gold"] = 0.45
+        configurations = {
+            "choice_t1": choice,
+            "transfer_dev_calibrated_choice": calibrated,
+        }
+        if not is_base:
+            native = _metric(n, native_correct)
+            fixed = _metric(n, min(n, native_correct + 1))
+            tuned = _metric(n, min(n, native_correct + 2))
+            for value in (native, fixed, tuned):
+                value["excluded_non_headline_rows"] = expected["questions"] - n
+            configurations.update({
+                "native_shipped": native,
+                "fixed_blend_0_5": fixed,
+                "transfer_dev_tuned_blend": tuned,
+            })
+        ensemble_datasets[dataset] = configurations
+
+    hashes = {
+        "jevbench_development_sha256": "1" * 64,
+        "jevbench_manifest_sha256": "2" * 64,
+        "transfer_manifest_sha256": "3" * 64,
+        "transfer_development_sha256": "4" * 64,
+        "transfer_test_sha256": "5" * 64,
+        "typed_revision": "6" * 40,
+        "typed_test_sha256": "7" * 64,
+        "jevjudge_test_sha256": "8" * 64,
+        "jevjudge_manifest_sha256": "9" * 64,
+    }
+    manifest = {
+        "model": model,
+        "source": {
+            "base": f"fixture/{spec['family']}" if is_base else None,
+            "checkpoint": None if is_base else f"fixture/{spec['key']}",
+        },
+        "checkpoint_artifacts": None if is_base else {
+            "requested": f"fixture/{spec['key']}",
+            "resolved": f"/fixture/{spec['key']}",
+            "files": {
+                "adapter_model.safetensors": {
+                    "sha256": f"{ordinal + 1}" * 64,
+                    "bytes": 123,
+                }
+            },
+        },
+        "base_loading": {"requested": spec["family"], "resolved": "/fixture/base"},
+        "predictor": {
+            "method": "training-free exact choice-token projection",
+            "adapter_applied": not is_base,
+            "native_decision_mode": spec["native_decision_mode"],
+            "canonical_base": spec["family"],
+            "canonical_revision": "a" * 40,
+        },
+        "runtime": {
+            "python": "3.12.0",
+            "torch": "2.fixture",
+            "code_revision": "b" * 40,
+        },
+        "protocol": {"temperature": 1.0, "kernel_policy": "fixture exact"},
+        "datasets": hashes,
+        "reports": reports,
+    }
+    ensemble = {
+        "model": model,
+        "protocol": {
+            "selection_rows": 1046,
+            "selection_split": "Transfer-v9 development",
+        },
+        "selected": {
+            "choice_temperature": 1.2,
+            "native_weight": 0.0 if is_base else 0.55,
+            "blend_temperature": 1.1,
+            "selection_objective": "fixture",
+        },
+        "datasets": ensemble_datasets,
+    }
+    directory = root / spec["key"]
+    _write_json(directory / "manifest.json", manifest)
+    _write_json(directory / "ensemble.json", ensemble)
+
+
+@pytest.fixture
+def matrix(tmp_path):
+    root = tmp_path / "runs"
+    for ordinal, spec in enumerate(RUN_SPECS):
+        _run_fixture(root, spec, ordinal)
+    external = tmp_path / "external.json"
+    _external_fixture(external)
+    return root, external
+
+
+def test_build_results_preserves_all_five_run_types_and_protocol_groups(matrix):
+    root, external = matrix
+
+    artifact = build_results(root, external)
+
+    assert artifact["artifact"] == "choice-readout-v2"
+    assert [run["key"] for run in artifact["source_runs"]] == [
+        "base4", "pointer4", "direct4", "base27", "pointer27"
+    ]
+    assert {run["weights"] for run in artifact["source_runs"]} == {
+        "frozen_base", "jevany_sft"
+    }
+    assert {run["native_readout"] for run in artifact["source_runs"]} == {
+        None, "pointer", "direct-token"
+    }
+    typed = artifact["datasets"]["typed_test"]
+    assert typed["questions"] == typed["headline_n"] == 2000
+    assert typed["external_baselines"][0]["model"] == "Published top"
+    assert artifact["datasets"]["jevjudge_text"]["external_baselines"][1]["model"] == "Kev-27B"
+    assert artifact["datasets"]["jevjudge_full"] == {
+        "label": "JevJudge full",
+        "records": 3220,
+        "status": "unsupported",
+        "accuracy": None,
+        "reason": (
+            "Training-free choice-token readout is text-only; image/video records "
+            "are not stripped or relabeled as a full-suite result."
+        ),
+        "native_checkpoint_results": "results/external-zero-shot-v1.json#jevjudge_full",
+    }
+    base = next(row for row in typed["runs"] if row["run"] == "base4")
+    direct = next(row for row in typed["runs"] if row["run"] == "direct4")
+    assert set(base["zero_shot"]) == {"choice_t1"}
+    assert set(base["transfer_dev_tuned"]) == {"transfer_dev_calibrated_choice"}
+    assert set(direct["zero_shot"]) == {
+        "choice_t1", "native_shipped", "fixed_blend_0_5"
+    }
+    assert set(direct["transfer_dev_tuned"]) == {
+        "transfer_dev_calibrated_choice", "transfer_dev_tuned_blend"
+    }
+    assert len(artifact["source_runs"][0]["artifacts"]["manifest"]["sha256"]) == 64
+    assert artifact["source_runs"][0]["runtime"]["torch"] == "2.fixture"
+
+
+def test_build_results_rejects_component_count_drift(matrix):
+    root, external = matrix
+    path = root / "pointer4" / "manifest.json"
+    manifest = json.loads(path.read_text())
+    manifest["reports"]["typed_test"]["components"]["native"]["clean"]["n"] = 1999
+    _write_json(path, manifest)
+
+    with pytest.raises(ValueError, match="native expected clean n=2000"):
+        build_results(root, external)
+
+
+def test_build_results_rejects_native_component_metric_misalignment(matrix):
+    root, external = matrix
+    path = root / "direct4" / "ensemble.json"
+    ensemble = json.loads(path.read_text())
+    native = ensemble["datasets"]["jevjudge_text"]["native_shipped"]
+    native["correct"] += 1
+    native["accuracy"] = native["correct"] / native["n"]
+    _write_json(path, ensemble)
+
+    with pytest.raises(ValueError, match="native component and ensemble are not aligned"):
+        build_results(root, external)
+
+
+def test_build_results_rejects_cross_run_dataset_hash_drift(matrix):
+    root, external = matrix
+    path = root / "direct4" / "manifest.json"
+    manifest = json.loads(path.read_text())
+    manifest["datasets"]["typed_test_sha256"] = "0" * 64
+    _write_json(path, manifest)
+
+    with pytest.raises(ValueError, match="dataset hashes differ"):
+        build_results(root, external)
+
+
+def test_synthetic_artifact_and_svg_are_written_only_to_requested_paths(matrix, tmp_path):
+    pytest.importorskip("matplotlib")
+    root, external = matrix
+    artifact = build_results(root, external)
+    json_out = tmp_path / "out" / "choice.json"
+    svg_out = tmp_path / "out" / "choice.svg"
+
+    write_results(json_out, artifact)
+    render_svg(artifact, svg_out)
+
+    assert json.loads(json_out.read_text())["schema_version"] == 2
+    svg = svg_out.read_text()
+    assert "Training-free choice readout" in svg
+    assert "Typed Decisions" in svg
+    assert "JevJudge text" in svg
+    assert "TRANSFER-DEV-TUNED" in svg

From d46eb271ae19d3a23c77f72c7f37d56c79ca01c2 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 01:04:54 +0000
Subject: [PATCH 06/10] Add choice readout report appendix builder

---
 .github/workflows/ci.yml                     |  18 +
 docs/EXTERNAL_EVALUATION.md                  |  29 +-
 scripts/build_external_report_appendix.py    | 467 +++++++++++++++++--
 tests/test_build_external_report_appendix.py | 217 +++++++++
 4 files changed, 694 insertions(+), 37 deletions(-)
 create mode 100644 tests/test_build_external_report_appendix.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ecdcd3e..366628c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -64,3 +64,21 @@ jobs:
           path: |
             ci-results.xml
             ci-dependencies.txt
+
+  report-test:
+    name: report appendix
+    runs-on: ubuntu-24.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.13'
+          cache: pip
+          cache-dependency-path: reports/requirements.txt
+      - name: Install report dependencies
+        run: python -m pip install --disable-pip-version-check -r reports/requirements.txt pytest==9.1.1
+      - name: Test report artifact and PDF builders
+        run: >-
+          python -m pytest -q
+          tests/test_build_choice_readout_results.py
+          tests/test_build_external_report_appendix.py
diff --git a/docs/EXTERNAL_EVALUATION.md b/docs/EXTERNAL_EVALUATION.md
index 731db1d..0366469 100644
--- a/docs/EXTERNAL_EVALUATION.md
+++ b/docs/EXTERNAL_EVALUATION.md
@@ -152,9 +152,12 @@ and are not used in the figure.
   [`external-zero-shot-v1.json`](../results/external-zero-shot-v1.json), and the
   deterministic renderer is
   [`plot_external_zero_shot.py`](../scripts/plot_external_zero_shot.py).
-- The two external-evaluation pages appended to the single merged technical
-  report are generated by
-  [`build_external_report_appendix.py`](../scripts/build_external_report_appendix.py).
+- Four evaluation pages are appended to the single merged technical report by
+  [`build_external_report_appendix.py`](../scripts/build_external_report_appendix.py):
+  two external-evaluation pages in Appendix L and two training-free
+  choice-token pages in Appendix M. Appendix M reads
+  `results/choice-readout-v2.json` directly; it does not rasterize or convert
+  the README SVG.
 - The tracked artifact contains the aggregate rows needed to regenerate the
   figure. Raw local run locations and SHA-256 hashes are recorded for audit but
   are not included in the repository. JevAny full results come from
@@ -190,20 +193,24 @@ build/report-repro-env/bin/python scripts/plot_external_zero_shot.py
 build/report-repro-env/bin/python scripts/build_external_report_appendix.py \
   --data results/external-zero-shot-v1.json \
   --chart docs/external-zero-shot.png \
-  --output build/JevAny_Tech_Report_External_Evaluation_Appendix.pdf \
+  --choice-data results/choice-readout-v2.json \
+  --output build/JevAny_Tech_Report_Evaluation_Appendices.pdf \
   --base-report reports/JevAny_Tech_Report.pdf \
   --merged-output reports/JevAny_Tech_Report.pdf \
   --base-pages 19
+cp reports/JevAny_Tech_Report.pdf site/files/reports/JevAny_Tech_Report.pdf
 ```
 
 `--base-pages 19` always takes the original report and agent-harness pages and
-discards any previously appended external pages before adding the newly built
-two-page appendix. The base and merged paths may therefore be identical without
-growing the report on repeated runs. Both outputs are written to temporary
-files in their destination directories and installed with `os.replace`; an
-interrupted build cannot leave a partial tracked PDF. Appendix and merged PDF
-metadata use the fixed creation and modification timestamp
-`D:20261001000000Z`. With the pinned inputs and dependencies, repeated commands
+discards any previously appended evaluation pages before rebuilding Appendices
+L and M. The result is exactly 23 pages: 19 preserved pages plus four generated
+pages. The base and merged paths may therefore be identical without growing the
+report on repeated runs. Both outputs are written to temporary files in their
+destination directories and installed with `os.replace`; an interrupted build
+cannot leave a partial tracked PDF. `reports/` contains only one PDF—the merged
+report; the `site/files/` copy is its byte-identical deployment mirror. Appendix
+and merged PDF metadata use the fixed creation and modification timestamp
+`D:20261002000000Z`. With the pinned inputs and dependencies, repeated commands
 produce byte-identical appendix and merged files.
 
 ### JevBench v1.5.4
diff --git a/scripts/build_external_report_appendix.py b/scripts/build_external_report_appendix.py
index 435746f..5885e4c 100644
--- a/scripts/build_external_report_appendix.py
+++ b/scripts/build_external_report_appendix.py
@@ -1,10 +1,11 @@
 #!/usr/bin/env python3
-"""Build and reproducibly merge the two-page external-evaluation appendix."""
+"""Build and reproducibly merge the external and choice-readout appendices."""
 
 import argparse
 from contextlib import contextmanager
 from datetime import datetime, timezone
 import json
+import math
 import os
 from pathlib import Path
 import tempfile
@@ -25,21 +26,29 @@
 RULE = "#DCE4EC"
 TEAL = "#278577"
 PALE = "#F3F7FA"
-PDF_TIMESTAMP = datetime(2026, 10, 1, tzinfo=timezone.utc)
-PDF_DATE_LITERAL = "D:20261001000000Z"
+PDF_TIMESTAMP = datetime(2026, 10, 2, tzinfo=timezone.utc)
+PDF_DATE_LITERAL = "D:20261002000000Z"
+EXTERNAL_APPENDIX_PAGES = 2
+CHOICE_APPENDIX_PAGES = 2
+APPENDIX_PAGES = EXTERNAL_APPENDIX_PAGES + CHOICE_APPENDIX_PAGES
+REPORT_BASE_PAGES = 19
+MERGED_REPORT_PAGES = REPORT_BASE_PAGES + APPENDIX_PAGES
 APPENDIX_METADATA = {
-    "Title": "JevAny Technical Report — External Evaluation Appendix",
+    "Title": "JevAny Technical Report — Evaluation Appendices",
     "Author": "SimpleJev",
-    "Subject": "Typed Decisions and JevJudge-Public v0.3 evaluation",
+    "Subject": "External evaluation and training-free choice-token readout",
     "Creator": "scripts/build_external_report_appendix.py",
     "Producer": "Matplotlib PDF backend",
     "CreationDate": PDF_TIMESTAMP,
     "ModDate": PDF_TIMESTAMP,
 }
 MERGED_METADATA = {
-    "/Title": "JevAny: Toward General Decision Intelligence — with Agent-Harness and External-Evaluation Appendices",
+    "/Title": "JevAny: Toward General Decision Intelligence — merged technical report",
     "/Author": "SimpleJev",
-    "/Subject": "JevAny model, bounded decision harness, and external decision-suite evaluation",
+    "/Subject": (
+        "JevAny model, bounded decision harness, external evaluation, and "
+        "training-free choice-token readout"
+    ),
     "/Creator": "scripts/build_external_report_appendix.py",
     "/Producer": "pypdf",
     "/CreationDate": PDF_DATE_LITERAL,
@@ -67,15 +76,24 @@ def atomic_destination(destination: Path):
         temporary.unlink(missing_ok=True)
 
 
-def header(fig, title: str, subtitle: str, page: int) -> None:
-    fig.text(0.065, 0.955, "SimpleJev  ·  TECHNICAL REPORT  ·  APPENDIX L", color=TEAL,
+def header(
+    fig,
+    title: str,
+    subtitle: str,
+    page: int,
+    *,
+    appendix: str = "L",
+    section: str = "External evaluation",
+    pages: int = EXTERNAL_APPENDIX_PAGES,
+) -> None:
+    fig.text(0.065, 0.955, f"SimpleJev  ·  TECHNICAL REPORT  ·  APPENDIX {appendix}", color=TEAL,
              fontsize=8.5, weight="bold")
     fig.text(0.065, 0.905, title, color=INK, fontsize=22, weight="bold")
     fig.text(0.065, 0.872, subtitle, color=MUTED, fontsize=9.5)
     fig.lines.append(plt.Line2D([0.065, 0.935], [0.845, 0.845], transform=fig.transFigure,
                                 color=RULE, linewidth=1))
     fig.text(0.065, 0.035, "JevAny · Toward General Decision Intelligence", color=MUTED, fontsize=7.5)
-    fig.text(0.935, 0.035, f"External evaluation · {page}/2", color=MUTED,
+    fig.text(0.935, 0.035, f"{section} · {page}/{pages}", color=MUTED,
              fontsize=7.5, ha="right")
 
 
@@ -173,15 +191,407 @@ def results_page(pdf: PdfPages, data: dict) -> None:
     plt.close(fig)
 
 
-def build_appendix(data_path: Path, chart_path: Path, output: Path) -> None:
-    data = json.loads(data_path.read_text())
+CHOICE_RUN_SPECS = {
+    "base4": ("Qwen3.5-4B", "frozen_base", None),
+    "pointer4": ("Qwen3.5-4B", "jevany_sft", "pointer"),
+    "direct4": ("Qwen3.5-4B", "jevany_sft", "direct-token"),
+    "base27": ("Qwen3.8-27B", "frozen_base", None),
+    "pointer27": ("Qwen3.8-27B", "jevany_sft", "pointer"),
+}
+CHOICE_DATASET_SPECS = (
+    ("jevbench_public", "JevBench\npublic", 231, 231, 231),
+    ("transfer_test", "Transfer-v9\ntest", 1_264, 1_264, 1_046),
+    ("typed_test", "Typed\ntest", 400, 2_000, 2_000),
+    ("jevjudge_text", "JevJudge\ntext", 724, 724, 724),
+)
+
+
+def _read_json_object(path: Path, name: str) -> dict:
+    try:
+        value = json.loads(path.read_text(encoding="utf-8"))
+    except FileNotFoundError as error:
+        raise ValueError(f"missing {name}: {path}") from error
+    except json.JSONDecodeError as error:
+        raise ValueError(f"invalid {name}: {path}: {error}") from error
+    if not isinstance(value, dict):
+        raise ValueError(f"{name} must be a JSON object: {path}")
+    return value
+
+
+def _choice_metric(row: dict, group: str, config: str, n: int, context: str) -> dict:
+    try:
+        metric = row[group][config]
+    except (KeyError, TypeError) as error:
+        raise ValueError(f"{context}: missing {group}/{config}") from error
+    if not isinstance(metric, dict) or metric.get("n") != n:
+        raise ValueError(f"{context}/{group}/{config}: expected n={n}")
+    correct = metric.get("correct")
+    accuracy = metric.get("accuracy")
+    if isinstance(correct, bool) or not isinstance(correct, int) or not 0 <= correct <= n:
+        raise ValueError(f"{context}/{group}/{config}: invalid correct count")
+    if (
+        isinstance(accuracy, bool)
+        or not isinstance(accuracy, (int, float))
+        or not math.isfinite(accuracy)
+        or not math.isclose(float(accuracy), correct / n, rel_tol=0, abs_tol=1e-12)
+    ):
+        raise ValueError(f"{context}/{group}/{config}: accuracy does not equal correct / n")
+    return metric
+
+
+def load_choice_artifact(path: Path) -> dict:
+    """Load the validated v2 matrix consumed by Appendix M."""
+
+    artifact = _read_json_object(path, "choice-readout artifact")
+    if artifact.get("schema_version") != 2 or artifact.get("artifact") != "choice-readout-v2":
+        raise ValueError("choice-readout artifact must use schema_version=2")
+    method = artifact.get("method")
+    if not isinstance(method, dict) or method.get("option_ids") != (
+        "A-Z followed by a-z; at most 52 options."
+    ):
+        raise ValueError("choice-readout artifact does not declare the v2 52-ID method")
+
+    source_rows = artifact.get("source_runs")
+    if not isinstance(source_rows, list):
+        raise ValueError("choice-readout artifact has no source_runs list")
+    source_by_key = {
+        row.get("key"): row for row in source_rows if isinstance(row, dict)
+    }
+    if set(source_by_key) != set(CHOICE_RUN_SPECS) or len(source_rows) != len(source_by_key):
+        raise ValueError("choice-readout artifact must contain the five required source runs")
+    for key, (family, weights, native_readout) in CHOICE_RUN_SPECS.items():
+        row = source_by_key[key]
+        if (row.get("family"), row.get("weights"), row.get("native_readout")) != (
+            family, weights, native_readout
+        ):
+            raise ValueError(f"choice-readout source run {key!r} has inconsistent identity")
+        selected = row.get("selected")
+        if not isinstance(selected, dict):
+            raise ValueError(f"choice-readout source run {key!r} has no selected settings")
+        for field in ("native_weight", "choice_temperature", "blend_temperature"):
+            value = selected.get(field)
+            if (
+                isinstance(value, bool)
+                or not isinstance(value, (int, float))
+                or not math.isfinite(value)
+            ):
+                raise ValueError(f"choice-readout source run {key!r} has invalid {field}")
+        if not 0 <= selected["native_weight"] <= 1:
+            raise ValueError(f"choice-readout source run {key!r} has invalid native_weight")
+        if selected["choice_temperature"] <= 0 or selected["blend_temperature"] <= 0:
+            raise ValueError(f"choice-readout source run {key!r} has invalid temperature")
+        if weights == "frozen_base" and selected["native_weight"] != 0:
+            raise ValueError(f"frozen-base run {key!r} selected a native weight")
+        ensemble_protocol = row.get("ensemble_protocol")
+        if not isinstance(ensemble_protocol, dict) or (
+            ensemble_protocol.get("selection_rows") != 1_046
+        ):
+            raise ValueError(f"choice-readout source run {key!r} has invalid selection rows")
+
+    datasets = artifact.get("datasets")
+    if not isinstance(datasets, dict):
+        raise ValueError("choice-readout artifact has no datasets object")
+    for dataset, _label, records, questions, n in CHOICE_DATASET_SPECS:
+        panel = datasets.get(dataset)
+        if not isinstance(panel, dict) or (
+            panel.get("records"), panel.get("questions"), panel.get("headline_n")
+        ) != (records, questions, n):
+            raise ValueError(
+                f"choice-readout dataset {dataset!r} must declare "
+                f"records/questions/headline_n={records}/{questions}/{n}"
+            )
+        rows = panel.get("runs")
+        if not isinstance(rows, list):
+            raise ValueError(f"choice-readout dataset {dataset!r} has no runs list")
+        by_key = {row.get("run"): row for row in rows if isinstance(row, dict)}
+        if set(by_key) != set(CHOICE_RUN_SPECS) or len(rows) != len(by_key):
+            raise ValueError(f"choice-readout dataset {dataset!r} must contain five runs")
+        for key, (family, weights, native_readout) in CHOICE_RUN_SPECS.items():
+            row = by_key[key]
+            if (row.get("family"), row.get("weights"), row.get("native_readout")) != (
+                family, weights, native_readout
+            ):
+                raise ValueError(f"{dataset}/{key}: inconsistent run identity")
+            _choice_metric(row, "zero_shot", "choice_t1", n, f"{dataset}/{key}")
+            if native_readout is not None:
+                _choice_metric(row, "zero_shot", "native_shipped", n, f"{dataset}/{key}")
+                _choice_metric(
+                    row,
+                    "transfer_dev_tuned",
+                    "transfer_dev_tuned_blend",
+                    n,
+                    f"{dataset}/{key}",
+                )
+
+    full = datasets.get("jevjudge_full")
+    if not isinstance(full, dict) or (
+        full.get("records"), full.get("status"), full.get("accuracy")
+    ) != (3_220, "unsupported", None):
+        raise ValueError("choice-token JevJudge full must be explicitly unsupported")
+    return artifact
+
+
+def _rounded_box(fig, x, y, width, height, *, facecolor=PALE, edgecolor=RULE):
+    patch = FancyBboxPatch(
+        (x, y), width, height, transform=fig.transFigure,
+        boxstyle="round,pad=0.010,rounding_size=0.010",
+        facecolor=facecolor, edgecolor=edgecolor, linewidth=0.8,
+    )
+    fig.patches.append(patch)
+
+
+def choice_method_page(pdf: PdfPages, data: dict) -> None:
+    fig = plt.figure(figsize=(8.5, 11), facecolor="white")
+    header(
+        fig,
+        "Training-free choice-token readout",
+        "A constrained readout, probability calibration, and stacking are different operations",
+        1,
+        appendix="M",
+        section="Choice-token readout",
+        pages=CHOICE_APPENDIX_PAGES,
+    )
+    fig.text(
+        0.065, 0.815,
+        "The frozen language model scores one exact option ID at the answer position. "
+        "No explanation is generated and no additional training is required for this readout.",
+        color=INK, fontsize=10.0, wrap=True, linespacing=1.45,
+    )
+
+    pipeline = (
+        ("1", "Ordered options", "Preserve candidate order"),
+        ("2", "52 exact IDs", "A–Z followed by a–z"),
+        ("3", "One-token projection", "Read next-token mass"),
+        ("4", "Decision distribution", "Renormalize over IDs"),
+    )
+    for index, (number, title, body) in enumerate(pipeline):
+        x = 0.065 + index * 0.222
+        _rounded_box(fig, x, 0.685, 0.185, 0.095, facecolor="#F5F9F8")
+        fig.text(x + 0.014, 0.750, number, color=TEAL, fontsize=9, weight="bold")
+        fig.text(x + 0.042, 0.750, title, color=INK, fontsize=9.2, weight="bold")
+        fig.text(x + 0.014, 0.712, body, color=MUTED, fontsize=7.8)
+        if index < len(pipeline) - 1:
+            fig.text(x + 0.198, 0.730, "→", color=TEAL, fontsize=14, weight="bold")
+
+    semantic_cards = (
+        (
+            "Choice-token · T=1",
+            "Training-free constrained\nprojection. This is a readout,\n"
+            "not temperature calibration;\nit can disagree with native.",
+        ),
+        (
+            "Temperature calibration",
+            "Fit scalar T on separate\ndevelopment rows. T changes\n"
+            "probabilities; it cannot change\nargmax or accuracy.",
+        ),
+        (
+            "Native + choice stack",
+            "Log-linear pooling can change\nrankings. Select native weight\n"
+            "and additional temperature on\nTransfer-v9 development only.",
+        ),
+    )
+    for index, (title, body) in enumerate(semantic_cards):
+        x = 0.065 + index * 0.299
+        _rounded_box(fig, x, 0.475, 0.270, 0.150)
+        fig.text(x + 0.016, 0.590, title, color=INK, fontsize=9.6, weight="bold")
+        fig.text(
+            x + 0.016, 0.558, body, color=MUTED, fontsize=7.8,
+            va="top", linespacing=1.35,
+        )
+
+    sources = {row["key"]: row for row in data["source_runs"]}
+    table = (
+        ("Frozen base", "—", "Choice-token", f"{sources['base4']['family']}, {sources['base27']['family']}"),
+        ("JevAny SFT", "Pointer", "Choice-token", f"{sources['pointer4']['family']}, {sources['pointer27']['family']}"),
+        ("JevAny SFT", "Direct-Token", "Choice-token", sources["direct4"]["family"]),
+    )
+    fig.text(0.065, 0.405, "Five-run comparison", color=INK, fontsize=12, weight="bold")
+    columns = (0.065, 0.245, 0.420, 0.610)
+    for x, label in zip(columns, ("Weights", "Native reference", "Training-free path", "Backbone family")):
+        fig.text(x, 0.370, label, color=MUTED, fontsize=8.0, weight="bold")
+    fig.lines.append(plt.Line2D([0.065, 0.935], [0.357, 0.357], transform=fig.transFigure,
+                                color=RULE, linewidth=1))
+    for index, values in enumerate(table):
+        y = 0.326 - index * 0.055
+        for x, value in zip(columns, values):
+            fig.text(x, y, value, color=INK, fontsize=8.4)
+        fig.lines.append(plt.Line2D([0.065, 0.935], [y - 0.019, y - 0.019],
+                                    transform=fig.transFigure, color="#EEF2F6", linewidth=0.7))
+
+    _rounded_box(fig, 0.065, 0.095, 0.870, 0.105, facecolor="#F7FAFC")
+    fig.text(0.083, 0.169, "Scope", color=INK, fontsize=9.5, weight="bold")
+    fig.text(
+        0.083, 0.142,
+        "Text only · up to 52 option IDs · JevJudge text 724 is supported.\n"
+        "The full 3,220-record multimodal suite is unsupported; media is never stripped into a score.",
+        color=MUTED, fontsize=8.2, linespacing=1.35,
+    )
+    fig.text(
+        0.083, 0.112,
+        "The earlier agent-harness appendix is unchanged; these readout experiments do not re-score agent tasks.",
+        color=MUTED, fontsize=8.2,
+    )
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+def _dataset_run(data: dict, dataset: str, run: str) -> dict:
+    matches = [row for row in data["datasets"][dataset]["runs"] if row["run"] == run]
+    if len(matches) != 1:
+        raise ValueError(f"{dataset}: expected one {run} row")
+    return matches[0]
+
+
+def _choice_matrix_rows(data: dict) -> list[dict]:
+    sources = {row["key"]: row for row in data["source_runs"]}
+    definitions = (
+        ("Frozen Qwen3.5-4B", "Choice T=1", "base4", "zero_shot", "choice_t1", "—"),
+        ("JevAny 4B Pointer", "Native", "pointer4", "zero_shot", "native_shipped", "shipped"),
+        ("", "Choice T=1", "pointer4", "zero_shot", "choice_t1", "—"),
+        ("", "Tuned stack", "pointer4", "transfer_dev_tuned", "transfer_dev_tuned_blend", None),
+        ("JevAny 4B Direct-Token", "Native", "direct4", "zero_shot", "native_shipped", "shipped"),
+        ("", "Choice T=1", "direct4", "zero_shot", "choice_t1", "—"),
+        ("", "Tuned stack", "direct4", "transfer_dev_tuned", "transfer_dev_tuned_blend", None),
+        ("Frozen Qwen3.8-27B", "Choice T=1", "base27", "zero_shot", "choice_t1", "—"),
+        ("JevAny 27B Pointer", "Native", "pointer27", "zero_shot", "native_shipped", "shipped"),
+        ("", "Choice T=1", "pointer27", "zero_shot", "choice_t1", "—"),
+        ("", "Tuned stack", "pointer27", "transfer_dev_tuned", "transfer_dev_tuned_blend", None),
+    )
+    rows = []
+    for model, readout, run, group, config, weight in definitions:
+        if weight is None:
+            weight = f"{sources[run]['selected']['native_weight']:.2f}"
+        metrics = []
+        for dataset, _label, _records, _questions, n in CHOICE_DATASET_SPECS:
+            metric = _choice_metric(
+                _dataset_run(data, dataset, run), group, config, n, f"{dataset}/{run}"
+            )
+            metrics.append(metric)
+        rows.append({
+            "model": model,
+            "readout": readout,
+            "run": run,
+            "weight": weight,
+            "metrics": metrics,
+            "tuned": readout == "Tuned stack",
+            "native": readout == "Native",
+        })
+    return rows
+
+
+def choice_results_page(pdf: PdfPages, data: dict) -> None:
+    fig = plt.figure(figsize=(8.5, 11), facecolor="white")
+    header(
+        fig,
+        "Choice-token accuracy matrix",
+        "Matched runs · Transfer-dev settings frozen before every displayed evaluation panel",
+        2,
+        appendix="M",
+        section="Choice-token readout",
+        pages=CHOICE_APPENDIX_PAGES,
+    )
+    rows = _choice_matrix_rows(data)
+    ax = fig.add_axes([0.055, 0.285, 0.89, 0.530])
+    ax.axis("off")
+    x_model, x_readout, x_weight = 0.00, 0.315, 0.495
+    x_metrics = (0.635, 0.755, 0.875, 0.995)
+    ax.text(x_model, 1.035, "Model", transform=ax.transAxes, fontsize=8.0,
+            color=MUTED, weight="bold")
+    ax.text(x_readout, 1.035, "Readout", transform=ax.transAxes, fontsize=8.0,
+            color=MUTED, weight="bold")
+    ax.text(x_weight, 1.035, "w_native", transform=ax.transAxes, fontsize=8.0,
+            color=MUTED, weight="bold", ha="right")
+    for x, (_dataset, label, _records, _questions, n) in zip(
+        x_metrics, CHOICE_DATASET_SPECS, strict=True
+    ):
+        ax.text(x, 1.035, f"{label}\n(n={n:,})", transform=ax.transAxes,
+                fontsize=7.7, color=MUTED, weight="bold", ha="right", va="bottom")
+    ax.plot([0, 1], [0.995, 0.995], transform=ax.transAxes, color=RULE, linewidth=1)
+
+    group_spans = ((0, 0), (1, 3), (4, 6), (7, 7), (8, 10))
+    step, first_y = 0.082, 0.930
+    for group_index, (start, end) in enumerate(group_spans):
+        top = first_y - start * step + 0.034
+        bottom = first_y - end * step - 0.034
+        if group_index % 2:
+            patch = FancyBboxPatch(
+                (-0.012, bottom), 1.022, top - bottom,
+                transform=ax.transAxes, boxstyle="round,pad=0.004,rounding_size=0.005",
+                facecolor="#F7FAFC", edgecolor="none", zorder=0,
+            )
+            ax.add_patch(patch)
+
+    maxima = [max(row["metrics"][index]["accuracy"] for row in rows) for index in range(4)]
+    for row_index, row in enumerate(rows):
+        y = first_y - row_index * step
+        color = TEAL if row["tuned"] else INK
+        ax.text(x_model, y, row["model"], transform=ax.transAxes, fontsize=8.0,
+                color=INK, va="center", weight="bold" if row["model"] else "normal")
+        ax.text(x_readout, y, row["readout"], transform=ax.transAxes, fontsize=8.0,
+                color=color, va="center", weight="bold" if row["tuned"] else "normal")
+        ax.text(x_weight, y, row["weight"], transform=ax.transAxes, fontsize=8.0,
+                color=MUTED, va="center", ha="right")
+        for metric_index, (x, metric) in enumerate(zip(x_metrics, row["metrics"], strict=True)):
+            best = math.isclose(metric["accuracy"], maxima[metric_index], rel_tol=0, abs_tol=1e-12)
+            ax.text(
+                x, y, f"{metric['accuracy'] * 100:.2f}", transform=ax.transAxes,
+                fontsize=8.1, color=TEAL if best else color, va="center", ha="right",
+                weight="bold" if best or row["tuned"] else "normal",
+            )
+        ax.plot([0, 1], [y - 0.040, y - 0.040], transform=ax.transAxes,
+                color="#EEF2F6", linewidth=0.6, zorder=1)
+
+    sources = {row["key"]: row for row in data["source_runs"]}
+    tuned = []
+    for key, label in (("pointer4", "4B Pointer"), ("direct4", "4B Direct-Token"),
+                       ("pointer27", "27B Pointer")):
+        selected = sources[key]["selected"]
+        tuned.append(
+            f"{label}: w={selected['native_weight']:.2f}, T={selected['blend_temperature']:.2f}"
+        )
+    fig.text(0.065, 0.235, "Transfer-dev selection", color=INK, fontsize=10.5, weight="bold")
+    fig.text(0.065, 0.205, " · ".join(tuned), color=TEAL, fontsize=8.4, weight="bold")
+    fig.text(
+        0.065, 0.169,
+        "Weights and additional temperatures use only 1,046 clean/knowable Transfer-v9 development rows.\n"
+        "Scalar T changes probabilities, not the accuracy values above.",
+        color=MUTED, fontsize=8.2, linespacing=1.35,
+    )
+    fig.text(
+        0.065, 0.125,
+        "Native = matched native rerun at the checkpoint's shipped inference temperature.\n"
+        "JevBench is a public development diagnostic; Transfer, Typed, and JevJudge text are held-out panels.",
+        color=MUTED, fontsize=8.2, linespacing=1.35,
+    )
+    fig.text(
+        0.065, 0.082,
+        "Choice-token is text-only: JevJudge text 724 is reported; full JevJudge 3,220 is unsupported.\n"
+        "Source and complete calibration metrics: results/choice-readout-v2.json.",
+        color=MUTED, fontsize=8.2, linespacing=1.35,
+    )
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+def build_appendix(
+    data_path: Path,
+    chart_path: Path,
+    choice_data_path: Path,
+    output: Path,
+) -> None:
+    data = _read_json_object(data_path, "external-evaluation artifact")
+    choice_data = load_choice_artifact(choice_data_path)
     with atomic_destination(output) as temporary:
         with PdfPages(temporary, metadata=APPENDIX_METADATA) as pdf:
             overview_page(pdf, data, chart_path)
             results_page(pdf, data)
+            choice_method_page(pdf, choice_data)
+            choice_results_page(pdf, choice_data)
         appendix = PdfReader(temporary)
-        if len(appendix.pages) != 2:
-            raise RuntimeError(f"expected a two-page appendix, got {len(appendix.pages)} pages")
+        if len(appendix.pages) != APPENDIX_PAGES:
+            raise RuntimeError(
+                f"expected a {APPENDIX_PAGES}-page appendix, got {len(appendix.pages)} pages"
+            )
 
 
 def page_invariants(page) -> tuple:
@@ -199,16 +609,18 @@ def page_invariants(page) -> tuple:
 
 
 def merge_report(base_report: Path, appendix_report: Path, merged_output: Path, base_pages: int) -> None:
-    if base_pages < 1:
-        raise ValueError("--base-pages must be positive")
+    if base_pages != REPORT_BASE_PAGES:
+        raise ValueError(f"--base-pages must be exactly {REPORT_BASE_PAGES}")
     base = PdfReader(base_report)
     appendix = PdfReader(appendix_report)
     if len(base.pages) < base_pages:
         raise ValueError(
             f"base report has {len(base.pages)} pages, fewer than --base-pages={base_pages}"
         )
-    if len(appendix.pages) != 2:
-        raise ValueError(f"appendix must have exactly two pages, got {len(appendix.pages)}")
+    if len(appendix.pages) != APPENDIX_PAGES:
+        raise ValueError(
+            f"appendix must have exactly {APPENDIX_PAGES} pages, got {len(appendix.pages)}"
+        )
 
     writer = PdfWriter()
     writer.pdf_header = "%PDF-1.7"
@@ -222,7 +634,7 @@ def merge_report(base_report: Path, appendix_report: Path, merged_output: Path,
         with temporary.open("wb") as stream:
             writer.write(stream)
         merged = PdfReader(temporary)
-        expected_pages = base_pages + len(appendix.pages)
+        expected_pages = MERGED_REPORT_PAGES
         if len(merged.pages) != expected_pages:
             raise RuntimeError(f"expected {expected_pages} merged pages, got {len(merged.pages)}")
         for index in range(base_pages):
@@ -230,10 +642,13 @@ def merge_report(base_report: Path, appendix_report: Path, merged_output: Path,
                 raise RuntimeError(f"base page {index + 1} changed during merge")
 
 
-def main() -> None:
+def main(argv: list[str] | None = None) -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--data", type=Path, default=ROOT / "results/external-zero-shot-v1.json")
     parser.add_argument("--chart", type=Path, default=ROOT / "docs/external-zero-shot.png")
+    parser.add_argument(
+        "--choice-data", type=Path, default=ROOT / "results/choice-readout-v2.json"
+    )
     parser.add_argument("--output", type=Path, required=True)
     parser.add_argument(
         "--base-report",
@@ -245,12 +660,12 @@ def main() -> None:
         type=Path,
         help="merged report; may be the same path as --base-report",
     )
-    parser.add_argument("--base-pages", type=int, default=19)
-    args = parser.parse_args()
+    parser.add_argument("--base-pages", type=int, default=REPORT_BASE_PAGES)
+    args = parser.parse_args(argv)
     if (args.base_report is None) != (args.merged_output is None):
         parser.error("--base-report and --merged-output must be provided together")
-    if args.base_pages < 1:
-        parser.error("--base-pages must be positive")
+    if args.base_pages != REPORT_BASE_PAGES:
+        parser.error(f"--base-pages must be exactly {REPORT_BASE_PAGES}")
     if args.base_report is not None:
         appendix_path = args.output.expanduser().absolute()
         base_path = args.base_report.expanduser().absolute()
@@ -258,12 +673,12 @@ def main() -> None:
         if appendix_path in {base_path, merged_path}:
             parser.error("--output must differ from --base-report and --merged-output")
 
-    build_appendix(args.data, args.chart, args.output)
+    build_appendix(args.data, args.chart, args.choice_data, args.output)
     print(f"Wrote reproducible appendix: {args.output}")
     if args.base_report is not None:
         merge_report(args.base_report, args.output, args.merged_output, args.base_pages)
         print(
-            f"Wrote reproducible {args.base_pages + 2}-page merged report: "
+            f"Wrote reproducible {MERGED_REPORT_PAGES}-page merged report: "
             f"{args.merged_output}"
         )
 
diff --git a/tests/test_build_external_report_appendix.py b/tests/test_build_external_report_appendix.py
new file mode 100644
index 0000000..6b6a62a
--- /dev/null
+++ b/tests/test_build_external_report_appendix.py
@@ -0,0 +1,217 @@
+import json
+
+import pytest
+
+pytest.importorskip("matplotlib")
+pytest.importorskip("pypdf")
+
+import matplotlib.pyplot as plt
+from pypdf import PdfReader, PdfWriter
+
+from scripts.build_external_report_appendix import (
+    CHOICE_DATASET_SPECS,
+    CHOICE_RUN_SPECS,
+    build_appendix,
+    load_choice_artifact,
+    merge_report,
+)
+
+
+def _write_json(path, value):
+    path.write_text(json.dumps(value, indent=2) + "\n", encoding="utf-8")
+
+
+def _external_artifact(path):
+    value = {
+        "typed_decisions": {
+            "models": [
+                {"model": "JevAny-Qwen3.8-27B", "accuracy": 0.728},
+                {"model": "Jev 1.13 (OpenRouter)", "accuracy": 0.727},
+            ],
+        },
+        "jevjudge_text": {
+            "models": [
+                {"model": "JevAny-Qwen3.8-27B", "accuracy": 0.6644},
+                {"model": "Jev 1.13 (OpenRouter)", "accuracy": 0.6506},
+                {"model": "Kev-27B", "accuracy": 0.6423},
+            ],
+        },
+        "jevjudge_full": {
+            "models": [
+                {
+                    "model": "JevAny-Qwen3.8-27B",
+                    "kind": "ours",
+                    "accuracy": 0.6227,
+                    "skill_role": 0.3555,
+                    "skill_role_ci_95": [0.3284, 0.3818],
+                    "nll": 0.878,
+                    "ece": 0.094,
+                },
+                {
+                    "model": "Jeff-Qwen3.5-2B",
+                    "kind": "open_external",
+                    "accuracy": 0.4823,
+                    "skill_role": 0.1357,
+                    "skill_role_ci_95": [0.1080, 0.1599],
+                    "nll": 1.105,
+                    "ece": 0.137,
+                },
+            ],
+        },
+    }
+    _write_json(path, value)
+
+
+def _metric(n, correct):
+    return {"n": n, "correct": correct, "accuracy": correct / n}
+
+
+def _choice_artifact(path):
+    source_runs = []
+    for index, (key, (family, weights, native)) in enumerate(CHOICE_RUN_SPECS.items()):
+        source_runs.append({
+            "key": key,
+            "model": f"fixture-{key}",
+            "family": family,
+            "weights": weights,
+            "native_readout": native,
+            "selected": {
+                "native_weight": 0.0 if native is None else 0.40 + index / 100,
+                "choice_temperature": 1.20 + index / 100,
+                "blend_temperature": 1.10 + index / 100,
+            },
+            "ensemble_protocol": {"selection_rows": 1_046},
+        })
+
+    datasets = {}
+    for dataset_index, (dataset, _label, records, questions, n) in enumerate(
+        CHOICE_DATASET_SPECS
+    ):
+        runs = []
+        for run_index, (key, (family, weights, native)) in enumerate(
+            CHOICE_RUN_SPECS.items()
+        ):
+            choice_correct = n // 2 + dataset_index * 2 + run_index
+            row = {
+                "run": key,
+                "model": f"fixture-{key}",
+                "family": family,
+                "weights": weights,
+                "native_readout": native,
+                "zero_shot": {"choice_t1": _metric(n, choice_correct)},
+                "transfer_dev_tuned": {
+                    "transfer_dev_calibrated_choice": _metric(n, choice_correct),
+                },
+            }
+            if native is not None:
+                row["zero_shot"].update({
+                    "native_shipped": _metric(n, choice_correct + 1),
+                    "fixed_blend_0_5": _metric(n, choice_correct + 2),
+                })
+                row["transfer_dev_tuned"]["transfer_dev_tuned_blend"] = _metric(
+                    n, choice_correct + 3
+                )
+            runs.append(row)
+        datasets[dataset] = {
+            "records": records,
+            "questions": questions,
+            "headline_n": n,
+            "runs": runs,
+        }
+    datasets["jevjudge_full"] = {
+        "label": "JevJudge full",
+        "records": 3_220,
+        "status": "unsupported",
+        "accuracy": None,
+        "reason": "text-only fixture",
+    }
+    value = {
+        "schema_version": 2,
+        "artifact": "choice-readout-v2",
+        "method": {
+            "option_ids": "A-Z followed by a-z; at most 52 options.",
+            "calibration": "Scalar temperature changes probabilities but not argmax accuracy.",
+        },
+        "source_runs": source_runs,
+        "datasets": datasets,
+    }
+    _write_json(path, value)
+    return value
+
+
+@pytest.fixture
+def appendix_inputs(tmp_path):
+    external = tmp_path / "external.json"
+    choice = tmp_path / "choice.json"
+    chart = tmp_path / "chart.png"
+    _external_artifact(external)
+    _choice_artifact(choice)
+    plt.imsave(chart, [[0.0, 0.5], [0.75, 1.0]], cmap="viridis")
+    return external, chart, choice
+
+
+def test_build_appendix_renders_four_pages_directly_from_choice_json(
+    appendix_inputs, tmp_path
+):
+    external, chart, choice = appendix_inputs
+    output = tmp_path / "appendices.pdf"
+
+    build_appendix(external, chart, choice, output)
+
+    pdf = PdfReader(output)
+    assert len(pdf.pages) == 4
+    method_text = pdf.pages[2].extract_text()
+    results_text = pdf.pages[3].extract_text()
+    assert "APPENDIX M" in method_text
+    assert "52 exact IDs" in method_text
+    assert "not temperature calibration" in method_text
+    assert "Choice-token accuracy matrix" in results_text
+    assert "Matched runs" in results_text
+    assert "JevJudge text 724" in results_text
+    # base4 JevBench fixture: floor(231 / 2) = 115 => 49.78%.
+    assert "49.78" in results_text
+
+
+def test_merge_report_preserves_first_19_pages_and_writes_exactly_23(
+    appendix_inputs, tmp_path
+):
+    external, chart, choice = appendix_inputs
+    appendix = tmp_path / "appendices.pdf"
+    base = tmp_path / "base.pdf"
+    merged = tmp_path / "merged.pdf"
+    build_appendix(external, chart, choice, appendix)
+    writer = PdfWriter()
+    for _ in range(21):
+        writer.add_blank_page(width=612, height=792)
+    with base.open("wb") as stream:
+        writer.write(stream)
+
+    merge_report(base, appendix, merged, base_pages=19)
+
+    assert len(PdfReader(merged).pages) == 23
+
+
+def test_merge_report_rejects_any_base_page_count_other_than_19(
+    appendix_inputs, tmp_path
+):
+    external, chart, choice = appendix_inputs
+    appendix = tmp_path / "appendices.pdf"
+    base = tmp_path / "base.pdf"
+    build_appendix(external, chart, choice, appendix)
+    writer = PdfWriter()
+    writer.add_blank_page(width=612, height=792)
+    with base.open("wb") as stream:
+        writer.write(stream)
+
+    with pytest.raises(ValueError, match="exactly 19"):
+        merge_report(base, appendix, tmp_path / "merged.pdf", base_pages=18)
+
+
+def test_choice_artifact_requires_full_3220_to_be_unsupported(tmp_path):
+    choice = tmp_path / "choice.json"
+    value = _choice_artifact(choice)
+    value["datasets"]["jevjudge_full"]["accuracy"] = 0.5
+    _write_json(choice, value)
+
+    with pytest.raises(ValueError, match="must be explicitly unsupported"):
+        load_choice_artifact(choice)

From 01e5081e8a8293efb7d6b1555ae4853e3ae31393 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 01:14:33 +0000
Subject: [PATCH 07/10] Strengthen report appendix regression tests

---
 .github/workflows/ci.yml                     |   6 +
 scripts/build_external_report_appendix.py    |  49 ++-
 tests/test_build_external_report_appendix.py | 328 ++++++++++++++++++-
 3 files changed, 366 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 366628c..985bb4d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -77,7 +77,13 @@ jobs:
           cache-dependency-path: reports/requirements.txt
       - name: Install report dependencies
         run: python -m pip install --disable-pip-version-check -r reports/requirements.txt pytest==9.1.1
+      - name: Verify report environment
+        run: |
+          python -m pip check
+          python -c "import matplotlib, pypdf, pytest; print(matplotlib.__version__, pypdf.__version__, pytest.__version__)"
       - name: Test report artifact and PDF builders
+        env:
+          JEVANY_REPORT_TESTS_REQUIRED: '1'
         run: >-
           python -m pytest -q
           tests/test_build_choice_readout_results.py
diff --git a/scripts/build_external_report_appendix.py b/scripts/build_external_report_appendix.py
index 5885e4c..a5dddd6 100644
--- a/scripts/build_external_report_appendix.py
+++ b/scripts/build_external_report_appendix.py
@@ -4,6 +4,7 @@
 import argparse
 from contextlib import contextmanager
 from datetime import datetime, timezone
+import hashlib
 import json
 import math
 import os
@@ -18,6 +19,7 @@
 from matplotlib.backends.backend_pdf import PdfPages
 from matplotlib.patches import FancyBboxPatch
 from pypdf import PdfReader, PdfWriter
+from pypdf.generic import IndirectObject
 
 
 ROOT = Path(__file__).resolve().parents[1]
@@ -594,17 +596,62 @@ def build_appendix(
             )
 
 
+def _canonical_pdf_object(value, active: set[int] | None = None):
+    """Resolve PDF references into a stable, object-number-independent value."""
+
+    if isinstance(value, IndirectObject):
+        return _canonical_pdf_object(value.get_object(), active)
+    if active is None:
+        active = set()
+    if isinstance(value, dict):
+        marker = id(value)
+        if marker in active:
+            return ("cycle",)
+        active.add(marker)
+        try:
+            items = tuple(sorted(
+                (
+                    str(key),
+                    _canonical_pdf_object(item, active),
+                )
+                for key, item in value.items()
+                if str(key) != "/Length"
+            ))
+            stream = None
+            if hasattr(value, "get_data"):
+                stream = hashlib.sha256(value.get_data()).hexdigest()
+            return ("dict", items, stream)
+        finally:
+            active.remove(marker)
+    if isinstance(value, (list, tuple)):
+        return tuple(_canonical_pdf_object(item, active) for item in value)
+    if isinstance(value, bytes):
+        return ("bytes", hashlib.sha256(value).hexdigest())
+    if value is None or isinstance(value, (bool, int, float, str)):
+        return value
+    return str(value)
+
+
 def page_invariants(page) -> tuple:
     """Return page properties that must survive the merge unchanged."""
     contents = page.get_contents()
     content_bytes = contents.get_data() if contents is not None else b""
+    annotations = []
+    for reference in page.get("/Annots", []):
+        annotation = reference.get_object()
+        # /P is a back-reference to the owning page and would make the
+        # otherwise stable annotation target depend on PDF object numbers.
+        annotations.append(_canonical_pdf_object({
+            key: value for key, value in annotation.items() if str(key) != "/P"
+        }))
     return (
         tuple(float(value) for value in page.mediabox),
         tuple(float(value) for value in page.cropbox),
         page.rotation,
         content_bytes,
         page.extract_text() or "",
-        len(page.get("/Annots", [])),
+        _canonical_pdf_object(page.get("/Resources")),
+        tuple(annotations),
     )
 
 
diff --git a/tests/test_build_external_report_appendix.py b/tests/test_build_external_report_appendix.py
index 6b6a62a..43fffe1 100644
--- a/tests/test_build_external_report_appendix.py
+++ b/tests/test_build_external_report_appendix.py
@@ -1,19 +1,36 @@
 import json
+import os
+import shutil
+from datetime import datetime, timezone
 
 import pytest
 
-pytest.importorskip("matplotlib")
-pytest.importorskip("pypdf")
+try:
+    import matplotlib
+    import pypdf
+except ImportError:
+    if os.environ.get("JEVANY_REPORT_TESTS_REQUIRED") == "1":
+        raise
+    pytest.skip("report dependencies are installed in the report-test job", allow_module_level=True)
 
 import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
 from pypdf import PdfReader, PdfWriter
 
 from scripts.build_external_report_appendix import (
     CHOICE_DATASET_SPECS,
     CHOICE_RUN_SPECS,
+    _choice_matrix_rows,
     build_appendix,
     load_choice_artifact,
     merge_report,
+    page_invariants,
+)
+from scripts.build_choice_readout_results import (
+    DATASETS as PRODUCER_DATASETS,
+    RUN_SPECS as PRODUCER_RUN_SPECS,
+    build_results,
+    write_results,
 )
 
 
@@ -23,17 +40,40 @@ def _write_json(path, value):
 
 def _external_artifact(path):
     value = {
+        "artifact_version": 5,
+        "provenance": {"fixture": {"sha256": "e" * 64}},
         "typed_decisions": {
             "models": [
                 {"model": "JevAny-Qwen3.8-27B", "accuracy": 0.728},
-                {"model": "Jev 1.13 (OpenRouter)", "accuracy": 0.727},
+                {
+                    "model": "Jev 1.13 (OpenRouter)",
+                    "kind": "external_api",
+                    "accuracy": 0.727,
+                },
+                {
+                    "model": "Published top",
+                    "kind": "published_only",
+                    "accuracy": 0.770,
+                },
             ],
         },
         "jevjudge_text": {
             "models": [
                 {"model": "JevAny-Qwen3.8-27B", "accuracy": 0.6644},
-                {"model": "Jev 1.13 (OpenRouter)", "accuracy": 0.6506},
-                {"model": "Kev-27B", "accuracy": 0.6423},
+                {
+                    "model": "Jev 1.13 (OpenRouter)",
+                    "kind": "external_api",
+                    "accuracy": 0.6506,
+                    "answered": 724,
+                    "requested": 724,
+                },
+                {
+                    "model": "Kev-27B",
+                    "kind": "open_kev",
+                    "accuracy": 0.6423,
+                    "answered": 724,
+                    "requested": 724,
+                },
             ],
         },
         "jevjudge_full": {
@@ -139,6 +179,161 @@ def _choice_artifact(path):
     return value
 
 
+def _producer_metric(n, correct, excluded):
+    return {
+        "n": n,
+        "correct": correct,
+        "accuracy": correct / n,
+        "cross_entropy_from_gold": 0.5,
+        "gold_entropy": 0.1,
+        "kl_from_gold": 0.4,
+        "brier": 0.2,
+        "ece": 0.03,
+        "mean_confidence": 0.7,
+        "cohort": "clean and knowable",
+        "excluded_non_headline_rows": excluded,
+        "ece_bins": 15,
+    }
+
+
+def _producer_run(root, spec, ordinal):
+    is_base = spec["weights"] == "frozen_base"
+    components = ["choice"] if is_base else ["choice", "native"]
+    model = f"producer-{spec['key']}"
+    reports = {}
+    ensemble_datasets = {}
+    for dataset_index, (dataset, expected) in enumerate(PRODUCER_DATASETS.items()):
+        n = expected["headline_n"]
+        excluded = expected["questions"] - n
+        choice_correct = n // 2 + ordinal + dataset_index
+        native_correct = choice_correct + 1
+        reports[dataset] = {
+            "dataset": dataset,
+            "records": expected["records"],
+            "questions": expected["questions"],
+            "components_executed": components,
+            "components": {
+                "choice": {"clean": {"n": n, "acc": choice_correct / n}},
+            },
+        }
+        if not is_base:
+            reports[dataset]["components"]["native"] = {
+                "clean": {"n": n, "acc": native_correct / n}
+            }
+        choice = _producer_metric(n, choice_correct, excluded)
+        calibrated = dict(choice)
+        calibrated["cross_entropy_from_gold"] = 0.45
+        configurations = {
+            "choice_t1": choice,
+            "transfer_dev_calibrated_choice": calibrated,
+        }
+        if not is_base:
+            configurations.update({
+                "native_shipped": _producer_metric(n, native_correct, excluded),
+                "fixed_blend_0_5": _producer_metric(n, native_correct + 1, excluded),
+                "transfer_dev_tuned_blend": _producer_metric(
+                    n, native_correct + 2, excluded
+                ),
+            })
+        ensemble_datasets[dataset] = configurations
+
+    hashes = {
+        "jevbench_development_sha256": "1" * 64,
+        "jevbench_manifest_sha256": "2" * 64,
+        "transfer_manifest_sha256": "3" * 64,
+        "transfer_development_sha256": "4" * 64,
+        "transfer_test_sha256": "5" * 64,
+        "typed_revision": "6" * 40,
+        "typed_test_sha256": "7" * 64,
+        "jevjudge_test_sha256": "8" * 64,
+        "jevjudge_manifest_sha256": "9" * 64,
+    }
+    manifest = {
+        "model": model,
+        "source": {
+            "base": f"fixture/{spec['family']}" if is_base else None,
+            "checkpoint": None if is_base else f"fixture/{spec['key']}",
+        },
+        "checkpoint_artifacts": None if is_base else {
+            "requested": f"fixture/{spec['key']}",
+            "resolved": f"/fixture/{spec['key']}",
+            "files": {
+                "adapter_model.safetensors": {
+                    "sha256": f"{ordinal + 1}" * 64,
+                    "bytes": 123,
+                },
+            },
+        },
+        "base_loading": {"requested": spec["family"], "resolved": "/fixture/base"},
+        "predictor": {
+            "method": "training-free exact choice-token projection",
+            "adapter_applied": not is_base,
+            "native_decision_mode": spec["native_decision_mode"],
+            "canonical_base": spec["family"],
+            "canonical_revision": "a" * 40,
+        },
+        "runtime": {
+            "python": "3.13.5",
+            "torch": "2.fixture",
+            "code_revision": "b" * 40,
+        },
+        "protocol": {"temperature": 1.0, "kernel_policy": "fixture exact"},
+        "datasets": hashes,
+        "reports": reports,
+    }
+    ensemble = {
+        "model": model,
+        "protocol": {
+            "selection_rows": 1_046,
+            "selection_split": "Transfer-v9 development",
+        },
+        "selected": {
+            "choice_temperature": 1.2,
+            "native_weight": 0.0 if is_base else 0.50 + ordinal / 100,
+            "blend_temperature": 1.0 + ordinal / 10,
+            "selection_objective": "fixture",
+        },
+        "datasets": ensemble_datasets,
+    }
+    directory = root / spec["key"]
+    directory.mkdir(parents=True)
+    _write_json(directory / "manifest.json", manifest)
+    _write_json(directory / "ensemble.json", ensemble)
+
+
+def _base_report(path, pages=21):
+    metadata = {
+        "Title": "Synthetic uniquely identified base report",
+        "CreationDate": datetime(2026, 10, 2, tzinfo=timezone.utc),
+        "ModDate": datetime(2026, 10, 2, tzinfo=timezone.utc),
+    }
+    with PdfPages(path, metadata=metadata) as pdf:
+        for page_number in range(1, pages + 1):
+            fig = plt.figure(figsize=(8.5, 11), facecolor="white")
+            fig.text(
+                0.1,
+                0.8,
+                f"UNIQUE BASE PAGE {page_number:02d}",
+                fontsize=16,
+                url=f"https://example.test/base/{page_number}",
+            )
+            fig.text(0.1, 0.7, f"resource marker {page_number * 17}", fontsize=9)
+            pdf.savefig(fig)
+            plt.close(fig)
+
+
+def _annotation_uris(page):
+    uris = []
+    for reference in page.get("/Annots", []):
+        annotation = reference.get_object()
+        action = annotation.get("/A")
+        if action is not None:
+            action = action.get_object()
+            if action.get("/URI") is not None:
+                uris.append(str(action["/URI"]))
+    return uris
+
+
 @pytest.fixture
 def appendix_inputs(tmp_path):
     external = tmp_path / "external.json"
@@ -150,6 +345,19 @@ def appendix_inputs(tmp_path):
     return external, chart, choice
 
 
+@pytest.fixture
+def producer_artifact(tmp_path):
+    root = tmp_path / "producer-runs"
+    for ordinal, spec in enumerate(PRODUCER_RUN_SPECS):
+        _producer_run(root, spec, ordinal)
+    external = tmp_path / "producer-external.json"
+    _external_artifact(external)
+    artifact = build_results(root, external)
+    choice = tmp_path / "producer-choice.json"
+    write_results(choice, artifact)
+    return external, choice
+
+
 def test_build_appendix_renders_four_pages_directly_from_choice_json(
     appendix_inputs, tmp_path
 ):
@@ -172,23 +380,111 @@ def test_build_appendix_renders_four_pages_directly_from_choice_json(
     assert "49.78" in results_text
 
 
-def test_merge_report_preserves_first_19_pages_and_writes_exactly_23(
+def test_producer_artifact_flows_into_pointer_direct_and_tuned_pdf_rows(
+    producer_artifact, tmp_path
+):
+    external, choice = producer_artifact
+    chart = tmp_path / "producer-chart.png"
+    output = tmp_path / "producer-appendices.pdf"
+    plt.imsave(chart, [[0.0, 0.5], [0.75, 1.0]], cmap="viridis")
+
+    loaded = load_choice_artifact(choice)
+    source_by_key = {row["key"]: row for row in loaded["source_runs"]}
+    assert source_by_key["pointer4"]["native_readout"] == "pointer"
+    assert source_by_key["direct4"]["native_readout"] == "direct-token"
+    assert source_by_key["pointer4"]["selected"]["native_weight"] == 0.51
+    assert source_by_key["direct4"]["selected"]["native_weight"] == 0.52
+    rows = _choice_matrix_rows(loaded)
+    assert [(row["model"], row["readout"], row["run"]) for row in rows] == [
+        ("Frozen Qwen3.5-4B", "Choice T=1", "base4"),
+        ("JevAny 4B Pointer", "Native", "pointer4"),
+        ("", "Choice T=1", "pointer4"),
+        ("", "Tuned stack", "pointer4"),
+        ("JevAny 4B Direct-Token", "Native", "direct4"),
+        ("", "Choice T=1", "direct4"),
+        ("", "Tuned stack", "direct4"),
+        ("Frozen Qwen3.8-27B", "Choice T=1", "base27"),
+        ("JevAny 27B Pointer", "Native", "pointer27"),
+        ("", "Choice T=1", "pointer27"),
+        ("", "Tuned stack", "pointer27"),
+    ]
+    matrix = {(row["run"], row["readout"]): row for row in rows}
+    assert matrix[("pointer4", "Native")]["metrics"][0]["correct"] == 120
+    assert matrix[("pointer4", "Tuned stack")]["metrics"][0]["correct"] == 122
+    assert matrix[("direct4", "Native")]["metrics"][0]["correct"] == 121
+    assert matrix[("direct4", "Tuned stack")]["metrics"][0]["correct"] == 123
+    build_appendix(external, chart, choice, output)
+
+    text = PdfReader(output).pages[3].extract_text()
+    assert "JevAny 4B Pointer" in text
+    assert "JevAny 4B Direct-Token" in text
+    assert text.count("Tuned stack") == 3
+    assert "4B Pointer: w=0.51, T=1.10" in text
+    assert "4B Direct-Token: w=0.52, T=1.20" in text
+    assert "27B Pointer: w=0.54, T=1.40" in text
+    # JevBench producer values: Pointer native/tuned and Direct native/tuned.
+    for accuracy in ("51.95", "52.81", "52.38", "53.25"):
+        assert accuracy in text
+
+
+def test_appendix_and_in_place_merge_are_deterministic_and_replace_old_pages(
     appendix_inputs, tmp_path
 ):
     external, chart, choice = appendix_inputs
-    appendix = tmp_path / "appendices.pdf"
+    appendix_a = tmp_path / "appendices-a.pdf"
+    appendix_b = tmp_path / "appendices-b.pdf"
     base = tmp_path / "base.pdf"
-    merged = tmp_path / "merged.pdf"
-    build_appendix(external, chart, choice, appendix)
-    writer = PdfWriter()
-    for _ in range(21):
-        writer.add_blank_page(width=612, height=792)
-    with base.open("wb") as stream:
-        writer.write(stream)
+    merged_a = tmp_path / "merged-a.pdf"
+    merged_b = tmp_path / "merged-b.pdf"
+    inplace = tmp_path / "inplace.pdf"
+    build_appendix(external, chart, choice, appendix_a)
+    build_appendix(external, chart, choice, appendix_b)
+    assert appendix_a.read_bytes() == appendix_b.read_bytes()
+    appendix_metadata = PdfReader(appendix_a).metadata
+    assert appendix_metadata["/CreationDate"] == "D:20261002000000Z"
+    assert appendix_metadata["/ModDate"] == "D:20261002000000Z"
+    assert appendix_metadata["/Title"] == "JevAny Technical Report — Evaluation Appendices"
+
+    _base_report(base)
+    original = PdfReader(base)
+    original_invariants = [page_invariants(original.pages[index]) for index in range(19)]
+    assert "UNIQUE BASE PAGE 20" in original.pages[19].extract_text()
+    assert "UNIQUE BASE PAGE 21" in original.pages[20].extract_text()
+    merge_report(base, appendix_a, merged_a, base_pages=19)
+    merge_report(base, appendix_b, merged_b, base_pages=19)
+    assert merged_a.read_bytes() == merged_b.read_bytes()
+
+    merged = PdfReader(merged_a)
+    assert len(merged.pages) == 23
+    for index in range(19):
+        assert f"UNIQUE BASE PAGE {index + 1:02d}" in merged.pages[index].extract_text()
+        assert page_invariants(merged.pages[index]) == original_invariants[index]
+        assert _annotation_uris(merged.pages[index]) == [
+            f"https://example.test/base/{index + 1}"
+        ]
+    generated_text = [merged.pages[index].extract_text() for index in range(19, 23)]
+    assert all("UNIQUE BASE PAGE 20" not in text for text in generated_text)
+    assert all("UNIQUE BASE PAGE 21" not in text for text in generated_text)
+    assert "APPENDIX L" in generated_text[0]
+    assert "APPENDIX L" in generated_text[1]
+    assert "APPENDIX M" in generated_text[2]
+    assert "APPENDIX M" in generated_text[3]
+    assert "Training-free choice-token readout" in generated_text[2]
+    assert "Choice-token accuracy matrix" in generated_text[3]
+    merged_metadata = merged.metadata
+    assert merged_metadata["/CreationDate"] == "D:20261002000000Z"
+    assert merged_metadata["/ModDate"] == "D:20261002000000Z"
+    assert merged_metadata["/Title"] == (
+        "JevAny: Toward General Decision Intelligence — merged technical report"
+    )
 
-    merge_report(base, appendix, merged, base_pages=19)
+    shutil.copyfile(base, inplace)
+    merge_report(inplace, appendix_a, inplace, base_pages=19)
+    first_inplace = inplace.read_bytes()
+    assert len(PdfReader(inplace).pages) == 23
+    merge_report(inplace, appendix_a, inplace, base_pages=19)
 
-    assert len(PdfReader(merged).pages) == 23
+    assert inplace.read_bytes() == first_inplace == merged_a.read_bytes()
 
 
 def test_merge_report_rejects_any_base_page_count_other_than_19(

From 57a832aaf742803d15dcb8beba3389d5dd9f25c7 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 01:32:10 +0000
Subject: [PATCH 08/10] Publish choice readout results and report

---
 README.md                                 |   59 +-
 docs/CHOICE_READOUT.md                    |  198 ++
 docs/EVALUATION.md                        |   35 +-
 docs/LETTER_READOUT.md                    |  173 +-
 docs/choice-readout-results.svg           |  928 ++++++++
 jevany/letter_predictor.py                |    8 +-
 reports/JevAny_Tech_Report.pdf            |  Bin 289359 -> 303110 bytes
 results/choice-readout-v2.json            | 2558 +++++++++++++++++++++
 site/files/reports/JevAny_Tech_Report.pdf |  Bin 289359 -> 303110 bytes
 9 files changed, 3762 insertions(+), 197 deletions(-)
 create mode 100644 docs/CHOICE_READOUT.md
 create mode 100644 docs/choice-readout-results.svg
 create mode 100644 results/choice-readout-v2.json

diff --git a/README.md b/README.md
index 4ca72da..c298c71 100644
--- a/README.md
+++ b/README.md
@@ -140,7 +140,7 @@ where Jev helps and when to return control to the LLM.
   - [🛠️ 1.2 JevAny Training](#training)
   - [🚀 1.3 JevAny Deployment](#deployment)
 - [🤗 2. Pretrained Models](#pretrained-models)
-  - [Training-free letter readout](#letter-readout)
+  - [Training-free choice-token readout](#choice-readout)
 - [📊 3. Benchmark Results](#evaluation)
   - [⏱️ 3.1 Inference efficiency](#efficiency)
 - [🕹️ 4. Examples & Test Environments](#examples--test-environments)
@@ -296,30 +296,43 @@ Pointer and direct-token models share the same API. Pointer supports up to
 See [readout choices](docs/TRAINING.md#pointer-and-direct-token-readouts) for
 training and accuracy tradeoffs.
 
-### Training-free letter readout <a name="letter-readout"></a>
+### Training-free choice-token readout <a name="choice-readout"></a><a name="letter-readout"></a>
 
-The experimental letter readout presents up to 26 options as A–Z, then sums
-the frozen language model's next-token probability mass for every vocabulary
-token that decodes exactly to that uppercase letter. It can run on a base model
-without training, apply a JevAny adapter before the same readout, or combine the
-letter and native pointer distributions. The letter path is text-only and does
-not change the checkpoint metadata. Select it with `--readout letter` in
-`jevany eval`, `jevany decide`, or `jevany serve`; the checkpoint-native
-readout remains the default.
+Choice-token readout assigns the ordered options to the exact one-token IDs
+`A–Z, a–z`, scores those 52 rows at the answer position, and renormalizes over
+the available options. It needs no readout training and works on a frozen base,
+after a JevAny Pointer or Direct-Token adapter, or in a log-linear stack with
+the checkpoint-native distribution. It can change the ranking and the selected
+answer; scalar temperature calibration only changes confidence.
 
-[Method, limits and evaluation command](docs/LETTER_READOUT.md).
+```bash
+jevany eval --run SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA \
+  --suite /path/to/suite --out runs/choice --device cuda --readout choice
+```
+
+[Method, commands, limits and complete tables](docs/CHOICE_READOUT.md) ·
+[Machine-readable results](results/choice-readout-v2.json)
+
+[![Training-free choice-token, checkpoint-native, Transfer-dev-tuned blend, and external baseline accuracy on Typed Decisions and JevJudge text](docs/choice-readout-results.svg)](docs/CHOICE_READOUT.md#results)
+
+- Direct-Token 4B's Transfer-dev-tuned stack reaches **79.83%** on held-out
+  Transfer, **67.65%** on Typed Decisions, and **59.25%** on JevJudge text:
+  +0.96, +0.45, and +0.83 points over its native readout.
+- Pointer 27B's tuned stack reaches **89.10%** on held-out Transfer and
+  **73.30%** on Typed, but drops from **66.44% to 64.36%** on JevJudge text.
+  The target-domain result decides whether stacking is useful.
+- The frozen 4B choice path scores 52.75% on Typed; applying the Direct-Token
+  adapter raises the same choice path to 64.80%. On JevJudge text, the same
+  adapter slightly lowers it, from 58.70% to 57.60%.
 
-[![Accuracy change from native pointer for letter readout and the fixed blend](docs/letter-readout-results.svg)](docs/LETTER_READOUT.md#results)
+**Practical rule**
 
-- The fixed 50/50 blend improved 27B on both diagnostics: +0.43 accuracy
-  points on JevBench public and +0.67 on Transfer-v9. Neither gain is
-  statistically significant.
-- At 4B, the same blend gained +1.73 points on JevBench public but lost 0.10
-  on Transfer-v9. Letter readout is a paired target-domain ablation, not a
-  universal upgrade.
-- Letter-only measured a 1.05x/1.07x median speedup over native at 4B/27B
-  on one warmed H200 panel. Blending runs both paths and cost 1.81x/1.89x the
-  native median latency.
+- Use choice-token readout when the model can reason over the options and the
+  bottleneck is extracting a decision; it cannot create missing task ability.
+- Blend only when paired development errors show that native and choice rescue
+  each other, then freeze one weight before the target evaluation.
+- Use temperature for probability calibration, not accuracy: it cannot change
+  the argmax.
 
 > **Benchmark units:** Cygnet's official **73.70** on JevBench v1.5.4 is a
 > four-axis composite over 1,624 open and sealed decisions, not accuracy. Its
@@ -329,6 +342,7 @@ readout remains the default.
 
 ## 📊 3. Benchmark Results <a name="evaluation"></a>
 
+The release table below uses each checkpoint's native readout.
 JevAny-Qwen3.8-27B leads both benchmarks and has the lowest NLL and Brier.
 Among 4B releases, direct-token leads on JevBench; pointer leads on Transfer.
 
@@ -357,7 +371,8 @@ NLL, Brier and ECE are measured on Transfer.
 [Machine-readable results](results/model-family-v2.json) ·
 [Method and ablation report](reports/JevAny_Tech_Report.pdf)
 
-The external comparison uses the complete Typed Decisions test split, the full
+The external comparison below also uses checkpoint-native readouts. It uses the
+complete Typed Decisions test split, the full
 3,220-record JevJudge multimodal suite, and its 724-record text slice. The same
 13 models stay in the same order; `—` means unsupported native input or no
 matching result.
diff --git a/docs/CHOICE_READOUT.md b/docs/CHOICE_READOUT.md
new file mode 100644
index 0000000..ba70c79
--- /dev/null
+++ b/docs/CHOICE_READOUT.md
@@ -0,0 +1,198 @@
+# Training-free choice-token readout
+
+Choice-token readout turns an ordinary causal language model into a decision
+readout without adding or training a head:
+
+1. Preserve the candidate order and assign the exact one-character IDs
+   `A–Z, a–z`.
+2. Ask for one case-sensitive option ID with thinking disabled.
+3. At the answer position, sum the mass of every vocabulary token that decodes
+   exactly to each available ID.
+4. Renormalize over the available IDs and map the distribution back to the
+   original option keys.
+
+This operation can change the ranking and the selected answer. It is therefore
+not temperature calibration. A scalar temperature changes probability values
+but cannot change argmax accuracy. A native + choice stack is a third operation:
+it pools two distributions and can change the answer.
+
+The same readout works on a frozen base model or after loading a JevAny Pointer
+or Direct-Token adapter. No additional readout training is performed.
+
+## Use it
+
+The checkpoint-native readout remains the default. Select choice-token readout
+with `--readout choice`:
+
+```bash
+jevany eval \
+  --run SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA \
+  --suite /path/to/jevbench-public-v1.4.2.2 \
+  --out runs/choice-readout/qwen35-4b-direct \
+  --device cuda --readout choice --choice-temperature 1.0
+
+jevany decide request.json \
+  --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
+  --device cuda --dtype bf16 --readout choice
+
+jevany serve \
+  --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
+  --device cuda --dtype bf16 --readout choice
+```
+
+Use `--choice-native-weight` to pool the choice and checkpoint-native
+distributions. This executes both paths. `--choice-max-tokens` controls the
+choice prompt limit. The former `letter` selector and `--letter-*` flags remain
+hidden compatibility aliases.
+
+The matrix evaluator also accepts a frozen base directly:
+
+```bash
+python -m scripts.evaluate_choice_readout_matrix \
+  --base Qwen/Qwen3.5-4B \
+  --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \
+  --base-load-path /path/to/Qwen3.5-4B \
+  --model-name Frozen-Qwen3.5-4B \
+  --out runs/choice-readout/base4 \
+  --jevbench /path/to/jevbench-public-v1.4.2.2 \
+  --transfer /path/to/transfer-v9 \
+  --typed /path/to/typed-decisions \
+  --jevjudge /path/to/jevjudge-public
+```
+
+## Limits
+
+- Text input only; JevJudge full multimodal is unsupported and is never
+  converted into a text-only score.
+- 1–52 options per question.
+- Exact one-token aliases must exist in the tokenizer for every used ID.
+- Choice-only runs use one chat-formatted prefill per question. A stack also
+  executes the checkpoint-native prefill.
+- Matrix runs use exact math SDPA below 8,192 tokens and memory-linear fused
+  SDPA for longer prompts. All 724 JevJudge text records complete; no record is
+  truncated or rejected.
+
+## Evaluation protocol
+
+The v2 matrix uses the same prompt and data for five runs:
+
+- frozen Qwen3.5-4B;
+- JevAny Qwen3.5-4B Pointer;
+- JevAny Qwen3.5-4B Direct-Token;
+- frozen Qwen3.8-27B;
+- JevAny Qwen3.8-27B Pointer.
+
+Zero-shot rows use T=1 and no evaluation-label tuning. For tuned rows, each
+model selects one native weight on the 1,046 clean/knowable Transfer-v9
+development decisions; fitted NLL and distance from 0.5 break accuracy ties.
+One additional temperature is then fitted on the same development rows. The
+weight and temperature are frozen before Transfer test, Typed Decisions,
+JevJudge text, and the JevBench public diagnostic are scored.
+
+The native parity gates reproduce the released README results before any
+held-out panel runs:
+
+| Checkpoint | Transfer development | JevBench public |
+|---|---:|---:|
+| JevAny 4B Pointer | 823/1,046 · 78.68% | 185/231 · 80.09% |
+| JevAny 4B Direct-Token | 818/1,046 · 78.20% | 187/231 · 80.95% |
+| JevAny 27B Pointer | 900/1,046 · 86.04% | 208/231 · 90.04% |
+
+## Results
+
+![Training-free choice-token, checkpoint-native, Transfer-dev-tuned blend, and external baseline accuracy on Typed Decisions and JevJudge text](choice-readout-results.svg)
+
+All values below are accuracy. JevBench is its 231-item public development
+diagnostic. Transfer test contains 1,046 clean/knowable held-out decisions.
+Typed covers 2,000 decisions in 400 test cases. JevJudge text covers all 724
+text records; it is not the 3,220-record multimodal suite.
+
+| Weights | Readout | JevBench public | Transfer test | Typed test | JevJudge text |
+|---|---|---:|---:|---:|---:|
+| Frozen Qwen3.5-4B | Choice T=1 | 79.65% | 68.45% | 52.75% | 58.70% |
+| JevAny 4B Pointer | Native shipped | 80.09% | 77.92% | **63.50%** | 58.01% |
+|  | Choice T=1 | **81.39%** | 73.61% | 57.80% | 52.62% |
+|  | Tuned stack · w=.78 | 80.95% | **78.01%** | 62.90% | **59.53%** |
+| JevAny 4B Direct-Token | Native shipped | 80.95% | 78.87% | 67.20% | 58.43% |
+|  | Choice T=1 | **81.39%** | 78.59% | 64.80% | 57.60% |
+|  | Tuned stack · w=.59 | 80.95% | **79.83%** | **67.65%** | **59.25%** |
+| Frozen Qwen3.8-27B | Choice T=1 | 87.88% | 80.69% | 68.25% | 61.74% |
+| JevAny 27B Pointer | Native shipped | **90.04%** | 87.95% | 72.80% | **66.44%** |
+|  | Choice T=1 | 89.61% | 86.04% | 72.60% | 57.87% |
+|  | Tuned stack · w=.52 | **90.04%** | **89.10%** | **73.30%** | 64.36% |
+
+The fixed, untuned 50/50 stacks remain a separate zero-shot reference:
+
+| Checkpoint | JevBench public | Transfer test | Typed test | JevJudge text |
+|---|---:|---:|---:|---:|
+| JevAny 4B Pointer | 81.39% | 77.44% | 60.70% | 59.12% |
+| JevAny 4B Direct-Token | 80.95% | 79.45% | 66.75% | 58.98% |
+| JevAny 27B Pointer | 90.04% | **89.20%** | 73.20% | 64.23% |
+
+The selected parameters differ by checkpoint:
+
+| Checkpoint | Native weight | Additional temperature |
+|---|---:|---:|
+| JevAny 4B Pointer | .78 | 1.10 |
+| JevAny 4B Direct-Token | .59 | 1.12 |
+| JevAny 27B Pointer | .52 | .84 |
+
+### External comparison
+
+| Model/readout | Typed test | JevJudge text |
+|---|---:|---:|
+| meraGPT Decider 1 · published | **76.80%** | — |
+| JevAny 27B Pointer · native | 72.80% | **66.44%** |
+| JevAny 27B Pointer · tuned stack | **73.30%** | 64.36% |
+| TypeSafe Jev 1.13 | 72.70% | 65.06% |
+| Kev-27B | — | 64.23% |
+
+The 27B stack beats its native readout by 12 held-out Transfer decisions and
+10 Typed decisions, while losing 15 JevJudge text decisions. Direct-Token 4B's
+stack improves over native on all three held-out panels. Pointer 4B improves on
+Transfer and JevJudge but loses on Typed. No single blend is universally best.
+
+### Practical principles
+
+- **Selection bottleneck:** use choice-token readout when the model can reason
+  over the candidates but the native extraction path is the bottleneck.
+- **Complementary errors:** keep a stack only when paired development results
+  show that each readout rescues errors made by the other. Select one weight on
+  development data and freeze it.
+- **Capability limit:** a different readout cannot supply knowledge, perception,
+  planning, or instruction-following ability missing from the underlying model.
+- **Calibration limit:** temperature can improve NLL, Brier, or ECE, but it
+  cannot change the selected option or accuracy.
+- **Domain shift:** target-domain validation wins over a global rule. The same
+  27B stack helps Transfer and Typed, ties JevBench, and hurts JevJudge text.
+
+Complete metrics, hashes, runtime versions, selected weights, and the unsupported
+full-JevJudge marker are in
+[`results/choice-readout-v2.json`](../results/choice-readout-v2.json). The
+deterministic builder is
+[`scripts/build_choice_readout_results.py`](../scripts/build_choice_readout_results.py).
+
+## Benchmark units
+
+Cygnet's official `73.70` on JevBench v1.5.4 is a four-axis composite over
+1,624 open and sealed decisions, not accuracy. Its separate public-development
+result is 203/231 (87.9% accuracy). Every JevBench value on this page is
+accuracy over the same 231 public-development items and cannot be compared
+numerically with the composite.
+
+## Historical v1
+
+The original prompt-v1 experiment and one-panel latency diagnostic remain
+unchanged in [`results/letter-readout-v1.json`](../results/letter-readout-v1.json)
+and [`letter-readout-results.svg`](letter-readout-results.svg). Those runs used a
+different prompt and runtime, and their matched native reruns did not reproduce
+the canonical README rows. They are retained for audit and are not mixed into
+the v2 tables.
+
+## Attribution
+
+The Cygnet-compatible prompt and exact option-ID aggregation semantics are
+adapted from `blockbrain-ai/cygnet-recipe` commit `3cf591c`, Copyright 2026
+Nood Co and contributors, under MIT. Cygnet credits the one-token option-ID
+readout to [NInfer](https://github.com/igorls/ninfer), released under Apache-2.0.
+JevAny does not incorporate NInfer source code. See [`NOTICE`](../NOTICE).
diff --git a/docs/EVALUATION.md b/docs/EVALUATION.md
index b6e8999..f858b77 100644
--- a/docs/EVALUATION.md
+++ b/docs/EVALUATION.md
@@ -2,7 +2,7 @@
 
 ## Model family v2
 
-This section contains the full results for the five current LoRA SFT releases.
+This section contains checkpoint-native results for the five current LoRA SFT releases.
 Transfer also informed model development and serves as a diagnostic comparison.
 
 **Transfer** is the cross-domain evaluation built from
@@ -16,7 +16,7 @@ and ECE in the main table are Transfer metrics; every run covers every item.
 Every JevBench value in this document is public-development accuracy
 (`correct / 231`). It is not the official JevBench v1.5.4 composite, which
 combines Intelligence, Calibration, Speed and Cost over 1,624 open and sealed
-decisions. See the [letter-readout guide](LETTER_READOUT.md#benchmark-units) for
+decisions. See the [choice-token readout guide](CHOICE_READOUT.md#benchmark-units) for
 the side-by-side definitions.
 
 | Model | Transfer ↑ | JevBench ↑ | NLL ↓ | Brier ↓ | ECE ↓ |
@@ -95,7 +95,7 @@ a complete local Transfer API run and JevBench's published per-tier accuracy.
 - **Loss:** cross-entropy, pure InfoNCE, and mixed objectives; CE gave the best
   Transfer accuracy in the loss sweep, while small contrastive terms mainly
   improved calibration. The released checkpoints use CE.
-- **Readout family:** at 4B, direct-token improves JevBench (80.95% vs 80.09%),
+- **Trained checkpoint-native readout family:** at 4B, direct-token improves JevBench (80.95% vs 80.09%),
   while pointer is slightly stronger on Transfer (78.68% vs 78.20%).
 
 ### Reproducibility
@@ -140,6 +140,35 @@ excluded. Gemma uses run/checkpoint timestamps because its earlier checkpoint
 format did not store cumulative elapsed seconds; the other figures come from
 checkpoint or terminal trainer telemetry.
 
+## Training-free choice-token readout
+
+Choice-token readout constrains the next token to one of 52 exact option IDs
+and renormalizes their mass. It needs no additional training and can change the
+selected answer. Scalar temperature fitting is separate: it changes confidence,
+not accuracy. A native + choice stack can change both.
+
+The stack weight and additional temperature below are selected only on 1,046
+clean/knowable Transfer-v9 development decisions and then frozen. Transfer test,
+Typed Decisions, and JevJudge text are held out. JevBench is a public diagnostic.
+All values are accuracy.
+
+| Weights | Readout | JevBench public | Transfer test | Typed test | JevJudge text |
+|---|---|---:|---:|---:|---:|
+| Frozen Qwen3.5-4B | Choice T=1 | 79.65% | 68.45% | 52.75% | 58.70% |
+| JevAny 4B Pointer | Native / Choice / Tuned | 80.09 / 81.39 / 80.95 | 77.92 / 73.61 / **78.01** | **63.50** / 57.80 / 62.90 | 58.01 / 52.62 / **59.53** |
+| JevAny 4B Direct-Token | Native / Choice / Tuned | 80.95 / **81.39** / 80.95 | 78.87 / 78.59 / **79.83** | 67.20 / 64.80 / **67.65** | 58.43 / 57.60 / **59.25** |
+| Frozen Qwen3.8-27B | Choice T=1 | 87.88% | 80.69% | 68.25% | 61.74% |
+| JevAny 27B Pointer | Native / Choice / Tuned | **90.04** / 89.61 / **90.04** | 87.95 / 86.04 / **89.10** | 72.80 / 72.60 / **73.30** | **66.44** / 57.87 / 64.36 |
+
+The 4B Direct-Token stack improves over its native readout on all three held-out
+panels. The 27B stack improves Transfer and Typed, ties JevBench, and hurts
+JevJudge text. Retain a stack only when paired development errors are
+complementary and the target distribution validates it.
+
+[Full method, fixed 50/50 controls and protocol](CHOICE_READOUT.md) ·
+[Machine-readable results](../results/choice-readout-v2.json) ·
+[Merged 23-page technical report](../reports/JevAny_Tech_Report.pdf)
+
 ## Earlier releases and evaluations
 
 The [JevBench and Kev comparison](EXTERNAL_EVALUATION.md) evaluates both released
diff --git a/docs/LETTER_READOUT.md b/docs/LETTER_READOUT.md
index 789f56c..136b12f 100644
--- a/docs/LETTER_READOUT.md
+++ b/docs/LETTER_READOUT.md
@@ -1,170 +1,7 @@
-# Training-free option-letter readout
+# Moved: training-free choice-token readout
 
-JevAny includes an experimental evaluator for a training-free, single-answer-slot
-decision readout. It adapts the prompt and probability-aggregation semantics
-from [Cygnet](https://github.com/blockbrain-ai/cygnet-recipe/tree/3cf591c692dec649f7c134449814610307c7bb3a):
+The public method name is **choice-token readout**. See
+[`CHOICE_READOUT.md`](CHOICE_READOUT.md) for the method, commands, limits,
+current results, and retained historical v1 links.
 
-1. Render the options in their original order as `A`, `B`, …, `Z`.
-2. Ask the model for exactly one option letter with thinking disabled.
-3. At the answer position, collect every vocabulary token whose decoded surface
-   is exactly each available uppercase letter. This emulates Cygnet's
-   `structured_outputs.choice` mask; leading-space, punctuated, and lowercase
-   forms are not admitted.
-4. Sum duplicate-token mass per letter and normalize over the available options.
-5. Optionally apply a temperature fitted for that model and evaluation domain.
-
-The model produces no explanation and the method needs no additional training.
-The same readout can be applied after loading a JevAny LoRA. For pointer
-checkpoints, the letter and native pointer distributions can also be combined
-log-linearly. The integrated commands call this knob
-`--letter-pointer-weight`; the standalone experiment script calls it
-`--pointer-weight`.
-
-## Run the public diagnostic
-
-Use `--readout letter` to score a JevAny checkpoint on any regular frozen suite
-or labelled JSONL. This example keeps temperature at 1 rather than copying a
-value fitted for another model:
-
-```bash
-jevany eval \
-  --run SimpleJev/JevAny-Qwen3.5-4B-LoRA \
-  --suite /path/to/jevbench-public-v1.4.2.2 \
-  --out runs/letter-readout/qwen35-4b \
-  --device cuda --readout letter --letter-temperature 1.0
-```
-
-The same selector works for one local decision or an HTTP deployment. Native
-checkpoint readout remains the default when `--readout` is omitted:
-
-```bash
-jevany decide request.json \
-  --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
-  --device cuda --dtype bf16 --readout letter
-
-jevany serve \
-  --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
-  --device cuda --dtype bf16 --readout letter
-```
-
-The standalone evaluator additionally supports a frozen base via `--base`,
-tier sampling, and explicit LoRA scaling:
-
-```bash
-python scripts/evaluate_letter_readout.py \
-  --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
-  --suite /path/to/jevbench-public-v1.4.2.2 \
-  --out runs/letter-readout/qwen35-4b \
-  --device cuda --dtype bf16 --temperature 1.0
-```
-
-Add `--sample-per-tier 1` for a three-record smoke test. Use
-`--lora-scale 0` with a checkpoint to evaluate its exact pinned base without
-the adapter. A nonzero `--pointer-weight` requires a native pointer checkpoint
-and adds a second, pointer-formatted model pass per request.
-
-Current limits:
-
-- text-only model input;
-- 1–26 options per question;
-- safetensors weights for models with an untied language-model output head;
-- one letter-formatted prefill per question, plus one native prefill when
-  pointer blending is enabled;
-- native inference-limit and CUDA-graph flags do not apply to the chat-formatted
-  letter path; use `--letter-max-tokens` for its prompt limit.
-
-Temperature changes reported probabilities but does not change the letter-only
-argmax. Fit it on a separate calibration split for each model. Cygnet's `3.4`
-was fitted for its own Gemma configuration and is not a default for JevAny.
-
-## Benchmark units
-
-The official score and the public diagnostic answer different questions:
-
-| Result | Evaluation set | Unit |
-|---|---:|---|
-| Cygnet 73.70 | JevBench v1.5.4, 1,624 decisions: 904 open + 720 sealed | Equal-weight harmonic mean of Intelligence, Calibration, Speed and Cost |
-| Cygnet 203/231 (87.9%) | Public development set, 231 decisions | Accuracy |
-| JevAny results in the current README | Same public development set, 231 decisions | Accuracy |
-
-Cygnet ranks first among 106 systems on the official v1.5.4 composite and is a
-statistical tie with Winnow-12B Q8. The 73.70 composite is not `73.70%` and
-cannot be compared numerically with public-set accuracy. See Cygnet's
-[official-submission report](https://github.com/blockbrain-ai/cygnet-recipe/pull/4)
-and the [JevBench v1.5.4 board](https://benchmarkheaven.com/jev-models/v1.5.4).
-
-## Results
-
-All JevAny values below are local pinned-checkpoint runs at temperature 1. The
-complete metrics, suite hashes, report hashes, and paired counts are in
-[`results/letter-readout-v1.json`](../results/letter-readout-v1.json).
-These are separate matched reruns of the pinned released checkpoints for this
-ablation; they do not replace the main model-family table, whose frozen
-checkpoints and run provenance differ.
-
-![Accuracy-point change from native pointer for letter and fixed-blend readouts](letter-readout-results.svg)
-
-JevBench public is a 231-question development diagnostic, not the sealed
-v1.5.4 board. `Base + letter` disables the JevAny adapter; the other columns use
-the released adapter. Deltas and two-sided exact McNemar p-values compare each
-adapter readout with the native pointer on the same questions.
-
-| Model | Base + letter | Native | Letter | Fixed 50/50 blend |
-|---|---:|---:|---:|---:|
-| Qwen3.5-4B | 79.65% | 80.09% | 81.39% (+1.30, p=.749) | **81.82%** (+1.73, p=.424) |
-| Qwen3.8-27B | 88.31% | 89.61% | 89.61% (+0.00, p=1.000) | **90.04%** (+0.43, p=1.000) |
-
-Transfer-v9 evaluates all 1,264 requests without rejection or truncation. Its
-accuracy headline uses the 1,046 clean knowable decisions.
-
-| Model | Native | Letter | Fixed 50/50 blend |
-|---|---:|---:|---:|
-| Qwen3.5-4B | **79.16%** | 75.72% (-3.44, p=.0028) | 79.06% (-0.10, p=1.000) |
-| Qwen3.8-27B | 86.23% | 84.23% (-2.01, p=.0375) | **86.90%** (+0.67, p=.337) |
-
-The 27B blend gets 909/1,046 decisions right versus 902/1,046 for native, but
-the seven-question gain is not statistically significant. The fixed blend is
-also not a universal improvement: at 4B it gets one fewer answer right. None of
-the positive gains in either table is significant at the 0.05 level. The
-letter-only Transfer-v9 losses show that a public-diagnostic gain does not by
-itself establish transfer.
-
-## Latency diagnostic
-
-One warmed run per mode on H200/BF16 measured the same heterogeneous 231-record
-panel with 16 warmups, one measured repeat, and concurrency 1. Values are external
-end-to-end median / p95 milliseconds; loading and network transport are
-excluded.
-
-| Model | Native | Letter | Fixed 50/50 blend |
-|---|---:|---:|---:|
-| Qwen3.5-4B | 138.43 / 200.98 | **131.86** / 201.55 (1.05x median speedup) | 250.34 / 388.31 (1.81x median latency) |
-| Qwen3.8-27B | 194.70 / 477.67 | **181.58** / 492.51 (1.07x median speedup) | 367.40 / 943.44 (1.89x median latency) |
-
-Letter-only has a modest median improvement on this panel despite using more
-logical input tokens on average (701 versus 602); p95 does not improve. The
-blend evaluates both prefills and averages 1,303 logical input tokens. This is
-not saturated server throughput or a general speed claim.
-
-## Practical principles
-
-- Treat letter readout as a model-and-domain ablation, not a drop-in upgrade.
-  Keep it only after a paired evaluation on the target decision distribution.
-- Blend only when the two readouts make complementary errors. The fixed 50/50
-  pool helped 27B on both diagnostics; at 4B it helped JevBench public but not
-  Transfer-v9. Select the weight on a separate development split.
-- Calibrate each model, readout, and domain separately. Cygnet's fitted
-  temperature `3.4` does not transfer to these Qwen checkpoints.
-- Re-measure latency in the deployment runtime and traffic mix. The one-pass
-  letter path can trim median latency, while blending requires both letter and
-  native passes and nearly doubles median latency here.
-
-## Attribution
-
-The Cygnet-compatible prompt and aggregation semantics are adapted from
-`blockbrain-ai/cygnet-recipe` commit
-`3cf591c692dec649f7c134449814610307c7bb3a`, Copyright 2026 Nood Co and
-contributors, under MIT. Cygnet credits the one-token option-letter readout
-method to [NInfer](https://github.com/igorls/ninfer), released under Apache-2.0.
-JevAny does not incorporate NInfer source code. See [NOTICE](../NOTICE) for the
-full Cygnet license notice.
+The former `letter` CLI/API spelling remains a compatibility alias.
diff --git a/docs/choice-readout-results.svg b/docs/choice-readout-results.svg
new file mode 100644
index 0000000..a32e254
--- /dev/null
+++ b/docs/choice-readout-results.svg
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diff --git a/jevany/letter_predictor.py b/jevany/letter_predictor.py
index 078bba4..ea9c656 100644
--- a/jevany/letter_predictor.py
+++ b/jevany/letter_predictor.py
@@ -1,9 +1,9 @@
 """Training-free choice-token inference over a frozen base or JevAny adapter.
 
-This is the in-process backend for :mod:`jevany.letter_readout`.  It keeps the
-existing pointer path unchanged: a JevAny checkpoint can instead be applied to
-the chat prompt before the frozen vocabulary readout, and its pointer
-distribution can optionally be combined with the letter distribution.
+This is the in-process backend for :mod:`jevany.letter_readout`. A JevAny
+checkpoint can be applied to the chat prompt before the frozen vocabulary
+readout, and its Pointer or Direct-Token native distribution can optionally be
+combined with the choice distribution.
 """
 
 from __future__ import annotations
diff --git a/reports/JevAny_Tech_Report.pdf b/reports/JevAny_Tech_Report.pdf
index de0ad588a7bc9b185964e4d24d4e35e03437c9f8..5e0b4c473188a421332f0f479802cd82f7c515c2 100644
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diff --git a/results/choice-readout-v2.json b/results/choice-readout-v2.json
new file mode 100644
index 0000000..8c1213f
--- /dev/null
+++ b/results/choice-readout-v2.json
@@ -0,0 +1,2558 @@
+{
+  "schema_version": 2,
+  "artifact": "choice-readout-v2",
+  "title": "Training-free choice-token readout evaluation",
+  "generated_by": "scripts/build_choice_readout_results.py",
+  "method": {
+    "readout": "Constrain the next token to exact one-character option IDs and renormalize their probability mass; this can change argmax decisions.",
+    "training": "No additional training for the choice-token readout.",
+    "option_ids": "A-Z followed by a-z; at most 52 options.",
+    "calibration": "Scalar temperature changes probabilities but not argmax accuracy.",
+    "ensemble": "Native + choice configurations use log-linear/geometric pooling."
+  },
+  "protocol_groups": {
+    "zero_shot": {
+      "meaning": "No Typed, JevJudge, JevBench, or Transfer-test labels tune the readout configuration.",
+      "configurations": [
+        "choice_t1",
+        "native_shipped",
+        "fixed_blend_0_5"
+      ]
+    },
+    "transfer_dev_tuned": {
+      "meaning": "Weight and additional temperature are selected only on Transfer-v9 development, then frozen for every displayed evaluation panel.",
+      "configurations": [
+        "transfer_dev_calibrated_choice",
+        "transfer_dev_tuned_blend"
+      ],
+      "accuracy_note": "Temperature-only calibrated choice has the same hard accuracy as choice_t1; only a changed blend weight can change its argmax."
+    }
+  },
+  "expected_counts": {
+    "transfer_calibration": {
+      "label": "Transfer-v9 development",
+      "role": "selection_only",
+      "records": 1264,
+      "questions": 1264,
+      "headline_n": 1046
+    },
+    "transfer_test": {
+      "label": "Transfer-v9 test",
+      "role": "held_out_evaluation",
+      "records": 1264,
+      "questions": 1264,
+      "headline_n": 1046
+    },
+    "typed_test": {
+      "label": "Typed Decisions test",
+      "role": "held_out_external_evaluation",
+      "records": 400,
+      "questions": 2000,
+      "headline_n": 2000
+    },
+    "jevbench_public": {
+      "label": "JevBench public development",
+      "role": "public_diagnostic_not_used_for_selection",
+      "records": 231,
+      "questions": 231,
+      "headline_n": 231
+    },
+    "jevjudge_text": {
+      "label": "JevJudge text",
+      "role": "held_out_external_evaluation",
+      "records": 724,
+      "questions": 724,
+      "headline_n": 724
+    }
+  },
+  "dataset_hashes": {
+    "jevbench_development_sha256": "b6d6eb34fdbbf46ec11be8657da5e513e3538990d7a00ca5582c55d8bd7941c6",
+    "jevbench_manifest_sha256": "e356ad5a2c1dd3917c126e74bdee58940391568de013502abcdcd902bde3c2a5",
+    "transfer_manifest_sha256": "3c4f0be94509a3612678bfd3a30fd99a8d0ca3c47ddfe7318075d95b2fa365e4",
+    "transfer_development_sha256": "53872ba045564315cf3c3dd8fb656e76441c6c514b1a7ff985ff0db3955710cf",
+    "transfer_test_sha256": "048abea5e12c73c3a622ce5836898c00fa43766e0bf48fb2459e77ee9873497f",
+    "typed_revision": "d0e2f0c42fef86cc15d1688d25a19f5ba7c85b18",
+    "typed_test_sha256": "4f294f218ea1da27f3efef936359389c62ea4d3973a41457732990f1d31b647c",
+    "jevjudge_test_sha256": "5c50f89e9ff1beb2107648b56f2a3cbc19c946e379519caf59afc191700e83cb",
+    "jevjudge_manifest_sha256": "ee52a15f82629742093a89910a8f72e7ca97248b197aad6ec801f984c2a15b68"
+  },
+  "code_revision": "ae891ed97147b57b41a78a4bb4960b018db3bf42",
+  "source_runs": [
+    {
+      "key": "base4",
+      "model": "Frozen-Qwen3.5-4B",
+      "family": "Qwen3.5-4B",
+      "weights": "frozen_base",
+      "native_readout": null,
+      "artifacts": {
+        "manifest": {
+          "path": "/lustre-storage/fsx/tianxinwei/JevAny/runs/choice-readout-v2-20261002-r1/base4/manifest.json",
+          "sha256": "49e57b70600869d5235a3363a3238161b753b3082f9d348cbd25e810a562418b"
+        },
+        "ensemble": {
+          "path": "/lustre-storage/fsx/tianxinwei/JevAny/runs/choice-readout-v2-20261002-r1/base4/ensemble.json",
+          "sha256": "388cf7b00de00c5c5090f27c8436981cadb1a71986f6855f77806c58840b03d2"
+        }
+      },
+      "source": {
+        "base": "Qwen/Qwen3.5-4B",
+        "checkpoint": null
+      },
+      "checkpoint_artifacts": null,
+      "base_loading": {
+        "canonical_base": "Qwen/Qwen3.5-4B",
+        "canonical_base_is_local": false,
+        "canonical_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
+        "override_used": true,
+        "override_config_sha256": "ddc63e1c717afa86c865bb5e01313d89d72bb53b97ad4a8a03ba8510c0621670"
+      },
+      "predictor": {
+        "method": "training-free exact choice-token projection",
+        "constraint_emulation": "exact decoded one-character option IDs",
+        "prompt": "Cygnet-compatible A-Z; extended with a-z above 26 options",
+        "system_prompt_sha256": "e2f0ac9f62eb446d7826b3abc3f7909287f4075175e9b58e4cd3ca72718671fe",
+        "chat_template_sha256": "a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715",
+        "prompt_format_version": 2,
+        "canonical_base": "Qwen/Qwen3.5-4B",
+        "canonical_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
+        "checkpoint": null,
+        "adapter_applied": false,
+        "adapter_scale": 0.0,
+        "tied_output_embeddings": true,
+        "unused_full_output_head_released": false,
+        "letter_choice_token_rows": 52,
+        "output_softcap": null,
+        "temperature": 1.0,
+        "native_weight": 0.0,
+        "native_weight_argument": "native_weight",
+        "native_decision_mode": null,
+        "pointer_weight": 0.0,
+        "native_temperature": null,
+        "pointer_temperature": null,
+        "return_components": false,
+        "exact_kernels": true,
+        "exact_cuda_kernels_applied": true,
+        "efficient_long_context_tokens": 8192,
+        "long_context_attention": "fused SDPA (Flash/Efficient, no math fallback) at or above the recorded threshold; exact math SDPA below it",
+        "max_tokens": 65536,
+        "backbone_context_window": 262144,
+        "effective_max_tokens": 65536,
+        "dtype": "torch.bfloat16",
+        "device": "cuda"
+      },
+      "runtime": {
+        "python": "3.12.14",
+        "torch": "2.8.0+cu128",
+        "cuda": "12.8",
+        "transformers": "5.17.0",
+        "peft": "0.21.0",
+        "numpy": "2.5.3",
+        "pyarrow": "25.0.1",
+        "safetensors": "0.8.0",
+        "code_revision": "ae891ed97147b57b41a78a4bb4960b018db3bf42"
+      },
+      "dataset_hashes": {
+        "jevbench_development_sha256": "b6d6eb34fdbbf46ec11be8657da5e513e3538990d7a00ca5582c55d8bd7941c6",
+        "jevbench_manifest_sha256": "e356ad5a2c1dd3917c126e74bdee58940391568de013502abcdcd902bde3c2a5",
+        "transfer_manifest_sha256": "3c4f0be94509a3612678bfd3a30fd99a8d0ca3c47ddfe7318075d95b2fa365e4",
+        "transfer_development_sha256": "53872ba045564315cf3c3dd8fb656e76441c6c514b1a7ff985ff0db3955710cf",
+        "transfer_test_sha256": "048abea5e12c73c3a622ce5836898c00fa43766e0bf48fb2459e77ee9873497f",
+        "typed_revision": "d0e2f0c42fef86cc15d1688d25a19f5ba7c85b18",
+        "typed_test_sha256": "4f294f218ea1da27f3efef936359389c62ea4d3973a41457732990f1d31b647c",
+        "jevjudge_test_sha256": "5c50f89e9ff1beb2107648b56f2a3cbc19c946e379519caf59afc191700e83cb",
+        "jevjudge_manifest_sha256": "ee52a15f82629742093a89910a8f72e7ca97248b197aad6ec801f984c2a15b68"
+      },
+      "evaluation_protocol": {
+        "kernel_policy": "LocalPredictor-compatible exact CUDA policy",
+        "dtype": "bf16",
+        "attn": "sdpa",
+        "temperature": 1.0,
+        "blend_selection": "not performed here; components are saved for fitting on Transfer-v9 development only",
+        "efficiency_scope": "base calls execute choice only; checkpoint calls execute choice and native. Do not compare component efficiency from these end-to-end timings",
+        "long_context_attention": "exact math SDPA below the configured threshold; memory-linear fused SDPA without a math fallback at or above it",
+        "efficient_long_context_tokens": 8192,
+        "resume": {
+          "enabled": true,
+          "reused_datasets": [
+            "jevbench_public",
+            "transfer_calibration",
+            "transfer_test",
+            "typed_test"
+          ],
+          "preexisting_code_revision": "256001ca806f9d7e302bea967850db214f3d8e4e",
+          "dataset_code_revisions": {
+            "jevbench_public": "256001ca806f9d7e302bea967850db214f3d8e4e",
+            "transfer_calibration": "256001ca806f9d7e302bea967850db214f3d8e4e",
+            "transfer_test": "256001ca806f9d7e302bea967850db214f3d8e4e",
+            "typed_test": "256001ca806f9d7e302bea967850db214f3d8e4e",
+            "jevjudge_text": "ae891ed97147b57b41a78a4bb4960b018db3bf42"
+          }
+        }
+      },
+      "ensemble_protocol": {
+        "selection_split": "Transfer-v9 development clean/knowable accuracy cohort; never any reported test panel",
+        "selection_rows": 1046,
+        "weight_grid": "0.00 to 1.00 inclusive, step 0.01",
+        "pool": "log-linear/geometric",
+        "temperature_fit": "bounded scalar NLL minimization, T in [0.05, 20]",
+        "ece": "15 equal-width bins for Typed Decisions; 10 for every other dataset",
+        "selection_objective": "accuracy; calibrated NLL then distance from 0.5 break ties",
+        "transfer": "the selected weight and temperature are frozen for every held-out dataset",
+        "native_shipped": "the checkpoint's native probabilities, including its shipped inference temperature; no additional temperature is applied",
+        "fixed_blend_0_5": "equal log-linear pooling of T=1 choice probabilities and shipped native probabilities; no additional temperature is applied",
+        "transfer_dev_tuned_blend": "the Transfer-dev-selected native weight followed by the reported scalar additional temperature"
+      },
+      "selected": {
+        "choice_temperature": 1.8514751919432642,
+        "native_weight": 0.0,
+        "blend_temperature": 1.8514751919432642,
+        "selection_objective": "hard-label accuracy on the Transfer-v9 development clean/knowable cohort; calibrated NLL and distance from 0.5 break ties"
+      }
+    },
+    {
+      "key": "pointer4",
+      "model": "JevAny-Qwen3.5-4B-Pointer",
+      "family": "Qwen3.5-4B",
+      "weights": "jevany_sft",
+      "native_readout": "pointer",
+      "artifacts": {
+        "manifest": {
+          "path": "/lustre-storage/fsx/tianxinwei/JevAny/runs/choice-readout-v2-20261002-r1/pointer4/manifest.json",
+          "sha256": "11b64050dc31f834c64dbe54c975df1fb365b9eb9465a4089e7de98ba00242b1"
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+          "sha256": "0e18398586de8760719ea6a3e8956f221a69e341e75d3364e22fda7df9cb6488"
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+      },
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+}
diff --git a/site/files/reports/JevAny_Tech_Report.pdf b/site/files/reports/JevAny_Tech_Report.pdf
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From 2dd0df1f8c426da3d26d8f5e1dacec8903b66a90 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 01:39:38 +0000
Subject: [PATCH 09/10] Clarify choice readout terminology

---
 docs/EVALUATION.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/EVALUATION.md b/docs/EVALUATION.md
index f858b77..a7db080 100644
--- a/docs/EVALUATION.md
+++ b/docs/EVALUATION.md
@@ -150,7 +150,7 @@ not accuracy. A native + choice stack can change both.
 The stack weight and additional temperature below are selected only on 1,046
 clean/knowable Transfer-v9 development decisions and then frozen. Transfer test,
 Typed Decisions, and JevJudge text are held out. JevBench is a public diagnostic.
-All values are accuracy.
+All values are accuracy percentages.
 
 | Weights | Readout | JevBench public | Transfer test | Typed test | JevJudge text |
 |---|---|---:|---:|---:|---:|

From 5b808fcb97ca7e8e2fb66c49249a67c86757ad61 Mon Sep 17 00:00:00 2001
From: Tianxin Wei <weitianxin@users.noreply.github.com>
Date: Fri, 2 Oct 2026 01:57:46 +0000
Subject: [PATCH 10/10] Finalize choice readout report and provenance

---
 docs/CHOICE_READOUT.md                       |  18 +
 docs/EXTERNAL_EVALUATION.md                  |  19 +-
 reports/JevAny_Tech_Report.pdf               | Bin 303110 -> 371281 bytes
 results/choice-readout-v2.json               | 114 ++++++
 scripts/build_choice_readout_results.py      | 137 ++++++-
 scripts/build_external_report_appendix.py    | 384 +++++++++++++++++--
 site/files/reports/JevAny_Tech_Report.pdf    | Bin 303110 -> 371281 bytes
 tests/test_build_choice_readout_results.py   |  66 +++-
 tests/test_build_external_report_appendix.py |  51 ++-
 9 files changed, 737 insertions(+), 52 deletions(-)

diff --git a/docs/CHOICE_READOUT.md b/docs/CHOICE_READOUT.md
index ba70c79..7444be9 100644
--- a/docs/CHOICE_READOUT.md
+++ b/docs/CHOICE_READOUT.md
@@ -98,6 +98,24 @@ held-out panel runs:
 | JevAny 4B Direct-Token | 818/1,046 · 78.20% | 187/231 · 80.95% |
 | JevAny 27B Pointer | 900/1,046 · 86.04% | 208/231 · 90.04% |
 
+### Evaluated model sources
+
+| Run | Canonical public repository | Evaluated revision |
+|---|---|---|
+| Frozen Qwen3.5-4B | `Qwen/Qwen3.5-4B` | `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a` |
+| JevAny 4B Pointer | `SimpleJev/JevAny-Qwen3.5-4B-LoRA` | `1c7aa9bab14ac347aeb917c0bcd757838a8a78ce` |
+| JevAny 4B Direct-Token | `SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA` | Not verified for the evaluated local release |
+| Frozen Qwen3.8-27B | `Qwen/Qwen3.8-27B` | `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0` |
+| JevAny 27B Pointer | `SimpleJev/JevAny-Qwen3.8-27B-LoRA` | `09c9e9102d5b8cc7d56558d25da1202a761b6c0d` |
+
+The Direct-Token run is still byte-identifiable: its evaluated adapter is
+`b768d54b9e5e20204b2db9e24c18ae6b10fa310b241d1931bac6b8882f127c53`
+and its native head is
+`d3909cffc156d077061114627c8aa22f60c7a8f1bdcfd20c489bb3bb009fc4a6`
+(SHA-256). The evaluation manifest and release metadata do not prove which
+immutable public-repository commit contains those exact files, so the result
+artifact records its public revision as `null` rather than guessing.
+
 ## Results
 
 ![Training-free choice-token, checkpoint-native, Transfer-dev-tuned blend, and external baseline accuracy on Typed Decisions and JevJudge text](choice-readout-results.svg)
diff --git a/docs/EXTERNAL_EVALUATION.md b/docs/EXTERNAL_EVALUATION.md
index 0366469..004551c 100644
--- a/docs/EXTERNAL_EVALUATION.md
+++ b/docs/EXTERNAL_EVALUATION.md
@@ -201,15 +201,16 @@ build/report-repro-env/bin/python scripts/build_external_report_appendix.py \
 cp reports/JevAny_Tech_Report.pdf site/files/reports/JevAny_Tech_Report.pdf
 ```
 
-`--base-pages 19` always takes the original report and agent-harness pages and
-discards any previously appended evaluation pages before rebuilding Appendices
-L and M. The result is exactly 23 pages: 19 preserved pages plus four generated
-pages. The base and merged paths may therefore be identical without growing the
-report on repeated runs. Both outputs are written to temporary files in their
-destination directories and installed with `os.replace`; an interrupted build
-cannot leave a partial tracked PDF. `reports/` contains only one PDF—the merged
-report; the `site/files/` copy is its byte-identical deployment mirror. Appendix
-and merged PDF metadata use the fixed creation and modification timestamp
+`--base-pages 19` retains the report and agent-harness pages, synchronizes the
+superseded 27B release headline fields to step 44,319, and discards any
+previously appended evaluation pages before rebuilding Appendices L and M. The
+result is exactly 23 pages: 19 retained pages plus four generated pages. The
+base and merged paths may therefore be identical without growing the report on
+repeated runs. Both outputs are written to temporary files in their destination
+directories and installed with `os.replace`; an interrupted build cannot leave
+a partial tracked PDF. `reports/` contains only one PDF—the merged report; the
+`site/files/` copy is its byte-identical deployment mirror. Appendix and merged
+PDF metadata use the fixed creation and modification timestamp
 `D:20261002000000Z`. With the pinned inputs and dependencies, repeated commands
 produce byte-identical appendix and merged files.
 
diff --git a/reports/JevAny_Tech_Report.pdf b/reports/JevAny_Tech_Report.pdf
index 5e0b4c473188a421332f0f479802cd82f7c515c2..cedc9fbbb46d310a861c6ee8670378c9763fc2fc 100644
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diff --git a/results/choice-readout-v2.json b/results/choice-readout-v2.json
index 8c1213f..05d9a87 100644
--- a/results/choice-readout-v2.json
+++ b/results/choice-readout-v2.json
@@ -98,6 +98,20 @@
         "base": "Qwen/Qwen3.5-4B",
         "checkpoint": null
       },
+      "public_source": {
+        "repository": "Qwen/Qwen3.5-4B",
+        "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
+        "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
+        "revision_status": "verified",
+        "repository_evidence": "manifest.base_loading.canonical_base",
+        "revision_evidence": "manifest.base_loading.canonical_revision",
+        "evaluated_artifact_sha256": {},
+        "base_model": {
+          "repository": "Qwen/Qwen3.5-4B",
+          "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
+        },
+        "limitation": null
+      },
       "checkpoint_artifacts": null,
       "base_loading": {
         "canonical_base": "Qwen/Qwen3.5-4B",
@@ -229,6 +243,33 @@
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       },
+      "public_source": {
+        "repository": "SimpleJev/JevAny-Qwen3.5-4B-LoRA",
+        "url": "https://huggingface.co/SimpleJev/JevAny-Qwen3.5-4B-LoRA",
+        "revision": "1c7aa9bab14ac347aeb917c0bcd757838a8a78ce",
+        "revision_status": "verified",
+        "repository_evidence": "manifest.source.checkpoint",
+        "revision_evidence": "manifest.source.checkpoint",
+        "evaluated_artifact_sha256": {
+          "adapter_model.safetensors": {
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+          },
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+            "bytes": 5513315
+          },
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+            "bytes": 1265
+          }
+        },
+        "base_model": {
+          "repository": "Qwen/Qwen3.5-4B",
+          "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
+        },
+        "limitation": null
+      },
       "checkpoint_artifacts": {
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@@ -377,6 +418,33 @@
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       },
+      "public_source": {
+        "repository": "SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA",
+        "url": "https://huggingface.co/SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA",
+        "revision": null,
+        "revision_status": "not_verified",
+        "repository_evidence": "results/model-family-v2.json#released_models",
+        "revision_evidence": null,
+        "evaluated_artifact_sha256": {
+          "adapter_model.safetensors": {
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+            "bytes": 64993432
+          },
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+          "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
+        },
+        "limitation": "The evaluated checkpoint came from a local release. Its exact files are pinned below by SHA-256, but the run manifest and release metadata do not prove an immutable public-repository revision for those bytes."
+      },
       "checkpoint_artifacts": {
         "requested": "/lustre-storage/fsx/tianxinwei/JevAny/releases/model-family-v2-20260929/JevAny-Qwen3.5-4B-Direct-Token-LoRA",
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@@ -525,6 +593,20 @@
         "base": "Qwen/Qwen3.8-27B",
         "checkpoint": null
       },
+      "public_source": {
+        "repository": "Qwen/Qwen3.8-27B",
+        "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
+        "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
+        "revision_status": "verified",
+        "repository_evidence": "manifest.base_loading.canonical_base",
+        "revision_evidence": "manifest.base_loading.canonical_revision",
+        "evaluated_artifact_sha256": {},
+        "base_model": {
+          "repository": "Qwen/Qwen3.8-27B",
+          "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0"
+        },
+        "limitation": null
+      },
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       "base_loading": {
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@@ -656,6 +738,33 @@
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       },
+      "public_source": {
+        "repository": "SimpleJev/JevAny-Qwen3.8-27B-LoRA",
+        "url": "https://huggingface.co/SimpleJev/JevAny-Qwen3.8-27B-LoRA",
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+        "revision_status": "verified",
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+        "revision_evidence": "manifest.source.checkpoint",
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+      },
       "checkpoint_artifacts": {
         "requested": "/storage/home/tianxinwei/.cache/huggingface/hub/models--SimpleJev--JevAny-Qwen3.8-27B-LoRA/snapshots/09c9e9102d5b8cc7d56558d25da1202a761b6c0d",
         "resolved": "/storage/home/tianxinwei/.cache/huggingface/hub/models--SimpleJev--JevAny-Qwen3.8-27B-LoRA/snapshots/09c9e9102d5b8cc7d56558d25da1202a761b6c0d",
@@ -847,6 +956,11 @@
       }
     }
   },
+  "repository_catalog": {
+    "path": "results/model-family-v2.json",
+    "sha256": "a49f03bd9371f1846d78a09682f000cba9f64004d8d5a222b3868f11ccb48d8c",
+    "release": "model-family-v2"
+  },
   "datasets": {
     "transfer_calibration": {
       "label": "Transfer-v9 development",
diff --git a/scripts/build_choice_readout_results.py b/scripts/build_choice_readout_results.py
index 8eb6fcc..0a37b3c 100644
--- a/scripts/build_choice_readout_results.py
+++ b/scripts/build_choice_readout_results.py
@@ -18,12 +18,14 @@
 import hashlib
 import json
 import math
+import re
 from pathlib import Path
 from typing import Any
 
 
 ROOT = Path(__file__).resolve().parents[1]
 DEFAULT_EXTERNAL = ROOT / "results/external-zero-shot-v1.json"
+DEFAULT_MODEL_CATALOG = ROOT / "results/model-family-v2.json"
 DEFAULT_JSON_OUT = ROOT / "results/choice-readout-v2.json"
 DEFAULT_SVG_OUT = ROOT / "docs/choice-readout-results.svg"
 
@@ -40,6 +42,7 @@
     {
         "key": "base4",
         "family": "Qwen3.5-4B",
+        "public_repository": "Qwen/Qwen3.5-4B",
         "weights": "frozen_base",
         "native_readout": None,
         "native_decision_mode": None,
@@ -47,6 +50,7 @@
     {
         "key": "pointer4",
         "family": "Qwen3.5-4B",
+        "public_repository": "SimpleJev/JevAny-Qwen3.5-4B-LoRA",
         "weights": "jevany_sft",
         "native_readout": "pointer",
         "native_decision_mode": "pointer",
@@ -54,6 +58,7 @@
     {
         "key": "direct4",
         "family": "Qwen3.5-4B",
+        "public_repository": "SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA",
         "weights": "jevany_sft",
         "native_readout": "direct-token",
         "native_decision_mode": "lm_token",
@@ -61,6 +66,7 @@
     {
         "key": "base27",
         "family": "Qwen3.8-27B",
+        "public_repository": "Qwen/Qwen3.8-27B",
         "weights": "frozen_base",
         "native_readout": None,
         "native_decision_mode": None,
@@ -68,6 +74,7 @@
     {
         "key": "pointer27",
         "family": "Qwen3.8-27B",
+        "public_repository": "SimpleJev/JevAny-Qwen3.8-27B-LoRA",
         "weights": "jevany_sft",
         "native_readout": "pointer",
         "native_decision_mode": "pointer",
@@ -244,6 +251,98 @@ def _validate_source(spec: dict, manifest: dict, context: str) -> None:
         )
 
 
+def _immutable_revision(value: Any) -> str | None:
+    if isinstance(value, str) and re.fullmatch(r"[0-9a-f]{40}", value):
+        return value
+    return None
+
+
+def _snapshot_revision(path: Any, repository: str) -> str | None:
+    """Return a Hub snapshot revision only when the path names this repository."""
+
+    if not isinstance(path, str):
+        return None
+    encoded_repository = repository.replace("/", "--")
+    match = re.search(
+        rf"(?:^|/)models--{re.escape(encoded_repository)}/snapshots/([0-9a-f]{{40}})(?:/|$)",
+        path,
+    )
+    return match.group(1) if match else None
+
+
+def _public_source(spec: dict, manifest: dict) -> dict:
+    """Expose reproducible public identity separately from local load paths.
+
+    A local release directory is not silently treated as a public Hub revision.
+    In that case the evaluated files remain identified by their SHA-256 values
+    and the missing public revision is explicit.
+    """
+
+    repository = spec["public_repository"]
+    base_loading = manifest.get("base_loading")
+    base_loading = base_loading if isinstance(base_loading, dict) else {}
+    base_repository = base_loading.get("canonical_base")
+    base_revision = _immutable_revision(base_loading.get("canonical_revision"))
+    if not isinstance(base_repository, str) or not base_repository:
+        raise ValueError(f"{spec['key']}: canonical base repository is missing")
+    if base_revision is None:
+        raise ValueError(f"{spec['key']}: immutable canonical base revision is missing")
+
+    artifacts = manifest.get("checkpoint_artifacts")
+    artifact_hashes = {}
+    if isinstance(artifacts, dict) and isinstance(artifacts.get("files"), dict):
+        for filename, metadata in artifacts["files"].items():
+            if not isinstance(metadata, dict) or not re.fullmatch(
+                r"[0-9a-f]{64}", str(metadata.get("sha256", ""))
+            ):
+                raise ValueError(
+                    f"{spec['key']}: checkpoint artifact {filename!r} lacks SHA-256"
+                )
+            artifact_hashes[filename] = {
+                "sha256": metadata["sha256"],
+                "bytes": metadata.get("bytes"),
+            }
+
+    if spec["weights"] == "frozen_base":
+        if repository != base_repository:
+            raise ValueError(
+                f"{spec['key']}: public base repository does not match canonical base"
+            )
+        revision = base_revision
+        revision_evidence = "manifest.base_loading.canonical_revision"
+        repository_evidence = "manifest.base_loading.canonical_base"
+        limitation = None
+    else:
+        source = manifest["source"]["checkpoint"]
+        revision = _snapshot_revision(source, repository)
+        revision_evidence = "manifest.source.checkpoint" if revision else None
+        repository_evidence = (
+            "manifest.source.checkpoint"
+            if revision
+            else "results/model-family-v2.json#released_models"
+        )
+        limitation = None if revision else (
+            "The evaluated checkpoint came from a local release. Its exact files "
+            "are pinned below by SHA-256, but the run manifest and release metadata "
+            "do not prove an immutable public-repository revision for those bytes."
+        )
+
+    return {
+        "repository": repository,
+        "url": f"https://huggingface.co/{repository}",
+        "revision": revision,
+        "revision_status": "verified" if revision else "not_verified",
+        "repository_evidence": repository_evidence,
+        "revision_evidence": revision_evidence,
+        "evaluated_artifact_sha256": artifact_hashes,
+        "base_model": {
+            "repository": base_repository,
+            "revision": base_revision,
+        },
+        "limitation": limitation,
+    }
+
+
 def _load_run(run_root: Path, spec: dict) -> dict:
     directory = run_root / spec["key"]
     manifest_path = directory / "manifest.json"
@@ -385,6 +484,7 @@ def _load_run(run_root: Path, spec: dict) -> dict:
             },
         },
         "source": copy.deepcopy(manifest.get("source")),
+        "public_source": _public_source(spec, manifest),
         "checkpoint_artifacts": copy.deepcopy(manifest.get("checkpoint_artifacts")),
         "base_loading": copy.deepcopy(manifest.get("base_loading")),
         "predictor": copy.deepcopy(manifest.get("predictor")),
@@ -467,10 +567,41 @@ def _load_external(path: Path) -> dict:
     }
 
 
-def build_results(run_root: Path, external_path: Path = DEFAULT_EXTERNAL) -> dict:
+def _load_repository_catalog(path: Path) -> dict:
+    catalog = _read_object(path)
+    released = catalog.get("released_models")
+    repositories = {
+        row.get("repository")
+        for row in released
+        if isinstance(row, dict) and isinstance(row.get("repository"), str)
+    } if isinstance(released, list) else set()
+    required = {
+        spec["public_repository"]
+        for spec in RUN_SPECS
+        if spec["weights"] != "frozen_base"
+    }
+    missing = required - repositories
+    if missing:
+        raise ValueError(
+            "model release catalog is missing canonical repositories: "
+            + ", ".join(sorted(missing))
+        )
+    return {
+        "path": _display_path(path),
+        "sha256": _sha256(path),
+        "release": catalog.get("release"),
+    }
+
+
+def build_results(
+    run_root: Path,
+    external_path: Path = DEFAULT_EXTERNAL,
+    model_catalog_path: Path = DEFAULT_MODEL_CATALOG,
+) -> dict:
     """Build a validated, JSON-serializable result artifact without writing it."""
 
     run_root = Path(run_root)
+    repository_catalog = _load_repository_catalog(Path(model_catalog_path))
     runs = [_load_run(run_root, spec) for spec in RUN_SPECS]
     if len({run["model"] for run in runs}) != len(runs):
         raise ValueError("the five run manifests must have unique model labels")
@@ -568,6 +699,7 @@ def build_results(run_root: Path, external_path: Path = DEFAULT_EXTERNAL) -> dic
         "code_revision": next(iter(revisions)),
         "source_runs": public_runs,
         "external_source": external["artifact"],
+        "repository_catalog": repository_catalog,
         "datasets": result_datasets,
         "legacy_results": {
             "status": "retained unchanged",
@@ -838,11 +970,12 @@ def main(argv: list[str] | None = None) -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--run-root", type=Path, required=True)
     parser.add_argument("--external", type=Path, default=DEFAULT_EXTERNAL)
+    parser.add_argument("--model-catalog", type=Path, default=DEFAULT_MODEL_CATALOG)
     parser.add_argument("--json-out", type=Path, default=DEFAULT_JSON_OUT)
     parser.add_argument("--svg-out", type=Path, default=DEFAULT_SVG_OUT)
     args = parser.parse_args(argv)
 
-    artifact = build_results(args.run_root, args.external)
+    artifact = build_results(args.run_root, args.external, args.model_catalog)
     write_results(args.json_out, artifact)
     render_svg(artifact, args.svg_out)
     print(f"Wrote {_display_path(args.json_out)} and {_display_path(args.svg_out)}")
diff --git a/scripts/build_external_report_appendix.py b/scripts/build_external_report_appendix.py
index a5dddd6..341bad4 100644
--- a/scripts/build_external_report_appendix.py
+++ b/scripts/build_external_report_appendix.py
@@ -19,7 +19,14 @@
 from matplotlib.backends.backend_pdf import PdfPages
 from matplotlib.patches import FancyBboxPatch
 from pypdf import PdfReader, PdfWriter
-from pypdf.generic import IndirectObject
+from pypdf._cmap import get_encoding
+from pypdf.generic import (
+    ArrayObject,
+    ContentStream,
+    IndirectObject,
+    NumberObject,
+    TextStringObject,
+)
 
 
 ROOT = Path(__file__).resolve().parents[1]
@@ -57,6 +64,49 @@
     "/ModDate": PDF_DATE_LITERAL,
 }
 
+# The source report predates the final 27B release checkpoint.  Synchronize the
+# handful of release-headline fields while merging so the one published PDF
+# does not call two different checkpoints "current".  The original layout,
+# resources, links, and every unrelated result remain untouched.
+CURRENT_RELEASE_TEXT = {
+    1: (("85.76", "86.04", 2), ("90.48", "90.04", 2)),
+    4: (
+        ("22,160", "44,319", 1),
+        ("85.76", "86.04", 1),
+        ("90.48", "90.04", 1),
+        ("0.392", "0.388", 1),
+        ("0.200", "0.195", 1),
+        ("0.030", "0.026", 1),
+    ),
+    7: (("85.76", "86.04", 1),),
+    8: (
+        ("22,160", "44,319", 1),
+        ("18.83", "39.43", 1),
+        ("602.7", "1,261.7", 1),
+        ("1,423", "2,082", 1),
+    ),
+}
+
+CURRENT_COMPUTE_PROVENANCE = (
+    (
+        "onds;itsvalueusestherecordedrunstartandcheckpointtimestamp."
+        "Direct-token,Qwen27B,and",
+        "onds; its value uses the recorded run start and checkpoint timestamp. "
+        "Direct-token and Muse use",
+    ),
+    (
+        "Museusecheckpoint-nativeelapsedtime;Qwen4Bpointerusesterminaltrainertelemetrybecause",
+        "checkpoint-native elapsed time; Qwen 4B pointer uses terminal trainer telemetry. "
+        "Qwen 27B uses",
+    ),
+    (
+        "thereleasedcheckpointisthecompletedstep13,850run."
+        "Peakwithin-runparallelismwas40GPUs.",
+        "estimated cumulative seconds-per-record timing. "
+        "Peak within-run parallelism was 40 GPUs.",
+    ),
+)
+
 
 @contextmanager
 def atomic_destination(destination: Path):
@@ -112,26 +162,48 @@ def overview_page(pdf: PdfPages, data: dict, chart: Path) -> None:
     fig = plt.figure(figsize=(8.5, 11), facecolor="white")
     header(fig, "External decision-suite evaluation",
            "Accuracy on Typed Decisions · JevJudge-Public v0.3 full multimodal suite · text-only subset", 1)
-    ax = fig.add_axes([0.055, 0.42, 0.89, 0.36])
-    ax.imshow(mpimg.imread(chart))
-    ax.axis("off")
+    chart_image = mpimg.imread(chart)
+    # The source asset is a wide, three-panel README graphic. Rendering it as
+    # one image makes its labels roughly four points on a portrait report page.
+    # Crop the panels (including their titles and axes) and use a 1 + 2 layout
+    # so every label is materially larger while the source pixels stay intact.
+    height, width = chart_image.shape[:2]
+    if width >= 3 and height >= 3:
+        y_start, y_stop = int(height * 0.15), int(height * 0.91)
+        panels = (
+            (chart_image[y_start:y_stop, int(width * 0.02):int(width * 0.43)],
+             [0.065, 0.500, 0.870, 0.300]),
+            (chart_image[y_start:y_stop, int(width * 0.42):int(width * 0.71)],
+             [0.065, 0.115, 0.420, 0.335]),
+            (chart_image[y_start:y_stop, int(width * 0.70):int(width * 0.995)],
+             [0.515, 0.115, 0.420, 0.335]),
+        )
+        for panel, bounds in panels:
+            ax = fig.add_axes(bounds)
+            ax.imshow(panel)
+            ax.axis("off")
+    else:
+        # Keep tiny fixture images usable in report-generation tests.
+        ax = fig.add_axes([0.065, 0.115, 0.870, 0.685])
+        ax.imshow(chart_image)
+        ax.axis("off")
     full = {row["model"]: row for row in data["jevjudge_full"]["models"]}
     text = {row["model"]: row for row in data["jevjudge_text"]["models"]}
     typed = {row["model"]: row for row in data["typed_decisions"]["models"]}
-    qwen = full["JevAny-Qwen3.8-27B"]
-    jeff = full["Jeff-Qwen3.5-2B"]
-    card(fig, 0.065, "Full-suite accuracy",
-         f"Qwen3.8-27B: {qwen['accuracy'] * 100:.2f}%\n"
-         f"Jeff-2B: {jeff['accuracy'] * 100:.2f}%\n"
-         f"Gap: {(qwen['accuracy'] - jeff['accuracy']) * 100:.2f} points")
-    card(fig, 0.365, "Typed accuracy",
-         f"JevAny Qwen27: {typed['JevAny-Qwen3.8-27B']['accuracy'] * 100:.2f}%\n"
-         f"Jev 1.13: {typed['Jev 1.13 (OpenRouter)']['accuracy'] * 100:.2f}%\n"
-         "Typed result published · gap 0.10 pt")
-    card(fig, 0.665, "Text-only accuracy",
-         f"JevAny Qwen27: {text['JevAny-Qwen3.8-27B']['accuracy'] * 100:.2f}%\n"
-         f"Jev 1.13: {text['Jev 1.13 (OpenRouter)']['accuracy'] * 100:.2f}%\n"
-         f"Kev-27B: {text['Kev-27B']['accuracy'] * 100:.2f}%")
+    qwen_full = full["JevAny-Qwen3.8-27B"]
+    qwen_typed = typed["JevAny-Qwen3.8-27B"]
+    qwen_text = text["JevAny-Qwen3.8-27B"]
+    fig.text(
+        0.065, 0.818,
+        "Same 13-model order · — unsupported/no result · teal JevAny · gold Jev API · purple Kev · gray other open · hatch published",
+        color=MUTED, fontsize=7.8,
+    )
+    fig.text(
+        0.065, 0.795,
+        f"Qwen3.8-27B topline  ·  Typed {qwen_typed['accuracy'] * 100:.2f}%  ·  "
+        f"JevJudge full {qwen_full['accuracy'] * 100:.2f}%  ·  text-only {qwen_text['accuracy'] * 100:.2f}%",
+        color=TEAL, fontsize=8.2, weight="bold",
+    )
     pdf.savefig(fig)
     plt.close(fig)
 
@@ -407,31 +479,40 @@ def choice_method_page(pdf: PdfPages, data: dict) -> None:
         ("JevAny SFT", "Pointer", "Choice-token", f"{sources['pointer4']['family']}, {sources['pointer27']['family']}"),
         ("JevAny SFT", "Direct-Token", "Choice-token", sources["direct4"]["family"]),
     )
-    fig.text(0.065, 0.405, "Five-run comparison", color=INK, fontsize=12, weight="bold")
+    fig.text(0.065, 0.425, "Five-run comparison", color=INK, fontsize=12, weight="bold")
     columns = (0.065, 0.245, 0.420, 0.610)
     for x, label in zip(columns, ("Weights", "Native reference", "Training-free path", "Backbone family")):
-        fig.text(x, 0.370, label, color=MUTED, fontsize=8.0, weight="bold")
-    fig.lines.append(plt.Line2D([0.065, 0.935], [0.357, 0.357], transform=fig.transFigure,
+        fig.text(x, 0.390, label, color=MUTED, fontsize=8.0, weight="bold")
+    fig.lines.append(plt.Line2D([0.065, 0.935], [0.377, 0.377], transform=fig.transFigure,
                                 color=RULE, linewidth=1))
     for index, values in enumerate(table):
-        y = 0.326 - index * 0.055
+        y = 0.346 - index * 0.052
         for x, value in zip(columns, values):
             fig.text(x, y, value, color=INK, fontsize=8.4)
         fig.lines.append(plt.Line2D([0.065, 0.935], [y - 0.019, y - 0.019],
                                     transform=fig.transFigure, color="#EEF2F6", linewidth=0.7))
 
-    _rounded_box(fig, 0.065, 0.095, 0.870, 0.105, facecolor="#F7FAFC")
-    fig.text(0.083, 0.169, "Scope", color=INK, fontsize=9.5, weight="bold")
+    _rounded_box(
+        fig, 0.065, 0.123, 0.870, 0.075,
+        facecolor="#FFF8E8", edgecolor="#E7C46A",
+    )
+    fig.text(0.083, 0.176, "Release sync and protocol boundary", color="#8A5A00", fontsize=9.5, weight="bold")
     fig.text(
-        0.083, 0.142,
-        "Text only · up to 52 option IDs · JevJudge text 724 is supported.\n"
-        "The full 3,220-record multimodal suite is unsupported; media is never stripped into a score.",
-        color=MUTED, fontsize=8.2, linespacing=1.35,
+        0.083, 0.151,
+        "The merged report and README identify the current 27B release checkpoint: step 44,319.",
+        color=INK, fontsize=8.0,
     )
     fig.text(
-        0.083, 0.112,
-        "The earlier agent-harness appendix is unchanged; these readout experiments do not re-score agent tasks.",
-        color=MUTED, fontsize=8.2,
+        0.083, 0.132,
+        "Appendix M native rows are matched prompt-v2/runtime reruns; Appendix L's earlier external-study row can differ by one decision.",
+        color=INK, fontsize=8.0,
+    )
+    _rounded_box(fig, 0.065, 0.061, 0.870, 0.047, facecolor="#F7FAFC")
+    fig.text(0.083, 0.091, "Scope", color=INK, fontsize=8.7, weight="bold")
+    fig.text(
+        0.137, 0.091,
+        "Text only · up to 52 IDs · JevJudge text 724 supported · full multimodal unsupported · agent tasks unchanged",
+        color=MUTED, fontsize=7.6,
     )
     pdf.savefig(fig)
     plt.close(fig)
@@ -486,7 +567,7 @@ def choice_results_page(pdf: PdfPages, data: dict) -> None:
     header(
         fig,
         "Choice-token accuracy matrix",
-        "Matched runs · Transfer-dev settings frozen before every displayed evaluation panel",
+        "Matched runs · current 27B release step 44,319 · Transfer-dev settings frozen before evaluation",
         2,
         appendix="M",
         section="Choice-token readout",
@@ -655,6 +736,203 @@ def page_invariants(page) -> tuple:
     )
 
 
+def _font_text_maps(page) -> dict[str, tuple[dict[str, str], dict[str, str]]]:
+    maps = {}
+    fonts = page["/Resources"]["/Font"]
+    for name, reference in fonts.items():
+        _encoding, character_map = get_encoding(reference.get_object())
+        reverse = {
+            unicode_text: glyph
+            for glyph, unicode_text in character_map.items()
+            if isinstance(glyph, str)
+            and isinstance(unicode_text, str)
+            and len(unicode_text) == 1
+        }
+        maps[str(name)] = (character_map, reverse)
+    return maps
+
+
+def _decode_text_object(value, character_map: dict[str, str]) -> str:
+    return "".join(character_map.get(glyph, glyph) for glyph in str(value))
+
+
+def _encode_text_object(
+    text: str,
+    reverse_map: dict[str, str],
+    prototype,
+) -> TextStringObject:
+    try:
+        glyphs = "".join(reverse_map[character] for character in text)
+    except KeyError as error:
+        raise RuntimeError(f"release-sync font cannot encode {error.args[0]!r}") from error
+    original_length = len(prototype.original_bytes)
+    glyph_count = len(str(prototype))
+    if glyph_count == 0 or original_length % glyph_count:
+        raise RuntimeError("release-sync text object has an unsupported encoding width")
+    byte_width = original_length // glyph_count
+    if byte_width not in {1, 2}:
+        raise RuntimeError(f"release-sync text uses unsupported {byte_width}-byte glyphs")
+    raw = b"".join(ord(glyph).to_bytes(byte_width, "big") for glyph in glyphs)
+    result = TextStringObject(glyphs)
+    result._original_bytes = raw
+    return result
+
+
+def _word_array(text: str, reverse_map: dict[str, str], prototype) -> ArrayObject:
+    words = text.split()
+    result = ArrayObject()
+    for index, word in enumerate(words):
+        if index:
+            result.append(NumberObject(-300))
+        result.append(_encode_text_object(word, reverse_map, prototype))
+    return result
+
+
+def synchronize_current_release(page, page_number: int) -> bool:
+    """Replace the superseded 27B headline fields in the retained report core."""
+
+    replacements = CURRENT_RELEASE_TEXT.get(page_number)
+    if replacements is None:
+        return False
+    extracted = page.extract_text() or ""
+    legacy_counts = {old: extracted.count(old) for old, _new, _count in replacements}
+    if not any(legacy_counts.values()):
+        return False
+    for old, _new, expected_count in replacements:
+        if legacy_counts[old] != expected_count:
+            raise RuntimeError(
+                f"base report page {page_number}: expected {expected_count} occurrences "
+                f"of legacy value {old!r}, found {legacy_counts[old]}"
+            )
+
+    text_maps = _font_text_maps(page)
+    content = ContentStream(page.get_contents(), page.indirect_reference.pdf)
+    active_font = None
+    replaced_counts = {old: 0 for old, _new, _count in replacements}
+    provenance_replaced = 0
+    for operands, operator in content.operations:
+        if operator == b"Tf":
+            active_font = str(operands[0])
+            continue
+        if operator not in {b"Tj", b"TJ"} or active_font is None:
+            continue
+        character_map, reverse_map = text_maps[active_font]
+        values = operands[0] if operator == b"TJ" else ArrayObject([operands[0]])
+        decoded_line = "".join(
+            _decode_text_object(value, character_map)
+            for value in values
+            if hasattr(value, "original_bytes")
+        )
+
+        if page_number == 8:
+            provenance = next(
+                (replacement for legacy, replacement in CURRENT_COMPUTE_PROVENANCE
+                 if decoded_line == legacy),
+                None,
+            )
+            if provenance is not None:
+                prototype = next(
+                    value for value in values if hasattr(value, "original_bytes")
+                )
+                operands[0] = _word_array(provenance, reverse_map, prototype)
+                provenance_replaced += 1
+                continue
+
+        for index, value in enumerate(values):
+            if not hasattr(value, "original_bytes"):
+                continue
+            decoded = _decode_text_object(value, character_map)
+            updated = decoded
+            for old, new, _expected_count in replacements:
+                count = updated.count(old)
+                if count:
+                    updated = updated.replace(old, new)
+                    replaced_counts[old] += count
+            if updated != decoded:
+                values[index] = _encode_text_object(updated, reverse_map, value)
+        if operator == b"Tj":
+            operands[0] = values[0]
+
+    for old, _new, expected_count in replacements:
+        if replaced_counts[old] != expected_count:
+            raise RuntimeError(
+                f"base report page {page_number}: replaced {replaced_counts[old]} of "
+                f"{expected_count} expected {old!r} values"
+            )
+    if page_number == 8 and provenance_replaced != len(CURRENT_COMPUTE_PROVENANCE):
+        raise RuntimeError(
+            "base report page 8: could not synchronize the 27B compute provenance"
+        )
+    # In-place list edits do not invalidate ContentStream's raw-byte cache.
+    content.operations = content.operations
+    synchronized_parts = []
+    active_font = None
+    for operands, operator in content.operations:
+        if operator == b"Tf":
+            active_font = str(operands[0])
+            continue
+        if operator not in {b"Tj", b"TJ"} or active_font is None:
+            continue
+        character_map, _reverse_map = text_maps[active_font]
+        values = operands[0] if operator == b"TJ" else [operands[0]]
+        synchronized_parts.extend(
+            _decode_text_object(value, character_map)
+            for value in values
+            if hasattr(value, "original_bytes")
+        )
+    synchronized = "".join(synchronized_parts)
+    for old, new, expected_count in replacements:
+        if old in synchronized or synchronized.count(new) < expected_count:
+            raise RuntimeError(
+                f"base report page {page_number}: failed to synchronize {old!r} to {new!r}; "
+                f"old={synchronized.count(old)}, new={synchronized.count(new)}"
+            )
+    page.replace_contents(content)
+    return True
+
+
+def synchronize_report_boundary(page, page_number: int) -> bool:
+    """Update the old appendix claim that the merged core is byte-unchanged."""
+
+    if page_number != 10:
+        return False
+    extracted = page.extract_text() or ""
+    if "The original PDF is pre-" not in extracted:
+        return False
+    replacements = {
+        "original ": "merged ",
+        "pre-": "release-",
+        "serv": "synch",
+        "ed unchanged.": "ronized.",
+    }
+    counts = {old: 0 for old in replacements}
+    content = ContentStream(page.get_contents(), page.indirect_reference.pdf)
+    for operands, operator in content.operations:
+        if operator not in {b"Tj", b"TJ"}:
+            continue
+        values = operands[0] if operator == b"TJ" else ArrayObject([operands[0]])
+        for index, value in enumerate(values):
+            if not isinstance(value, TextStringObject):
+                continue
+            updated = str(value)
+            for old, new in replacements.items():
+                count = updated.count(old)
+                if count:
+                    updated = updated.replace(old, new)
+                    counts[old] += count
+            if updated != str(value):
+                values[index] = TextStringObject(updated)
+        if operator == b"Tj":
+            operands[0] = values[0]
+    if any(count != 1 for count in counts.values()):
+        raise RuntimeError(
+            f"base report page 10: could not synchronize appendix boundary: {counts}"
+        )
+    content.operations = content.operations
+    page.replace_contents(content)
+    return True
+
+
 def merge_report(base_report: Path, appendix_report: Path, merged_output: Path, base_pages: int) -> None:
     if base_pages != REPORT_BASE_PAGES:
         raise ValueError(f"--base-pages must be exactly {REPORT_BASE_PAGES}")
@@ -671,8 +949,15 @@ def merge_report(base_report: Path, appendix_report: Path, merged_output: Path,
 
     writer = PdfWriter()
     writer.pdf_header = "%PDF-1.7"
+    synchronized_pages = set()
+    expected_base_invariants = {}
     for index in range(base_pages):
         writer.add_page(base.pages[index])
+        changed = synchronize_current_release(writer.pages[-1], index + 1)
+        changed = synchronize_report_boundary(writer.pages[-1], index + 1) or changed
+        if changed:
+            synchronized_pages.add(index)
+            expected_base_invariants[index] = page_invariants(writer.pages[-1])
     for page in appendix.pages:
         writer.add_page(page)
     writer.add_metadata(MERGED_METADATA)
@@ -685,8 +970,41 @@ def merge_report(base_report: Path, appendix_report: Path, merged_output: Path,
         if len(merged.pages) != expected_pages:
             raise RuntimeError(f"expected {expected_pages} merged pages, got {len(merged.pages)}")
         for index in range(base_pages):
-            if page_invariants(base.pages[index]) != page_invariants(merged.pages[index]):
+            expected = (
+                expected_base_invariants[index]
+                if index in synchronized_pages
+                else page_invariants(base.pages[index])
+            )
+            actual = page_invariants(merged.pages[index])
+            if index in synchronized_pages:
+                # A writer-attached page temporarily extracts its glyph IDs,
+                # while the serialized page resolves them through ToUnicode.
+                # Compare every invariant except that transient text view.
+                expected = expected[:4] + expected[5:]
+                actual = actual[:4] + actual[5:]
+            if expected != actual:
                 raise RuntimeError(f"base page {index + 1} changed during merge")
+        for page_number, replacements in CURRENT_RELEASE_TEXT.items():
+            page_text = merged.pages[page_number - 1].extract_text() or ""
+            is_release_page = (page_number - 1) in synchronized_pages or any(
+                page_text.count(new) >= expected_count
+                for _old, new, expected_count in replacements
+            )
+            if not is_release_page:
+                continue
+            for old, new, expected_count in replacements:
+                if old in page_text or page_text.count(new) < expected_count:
+                    raise RuntimeError(
+                        f"serialized base page {page_number} does not contain the current "
+                        f"release value {new!r}"
+                    )
+        boundary_text = merged.pages[9].extract_text() or ""
+        boundary_text = boundary_text.replace("-\n", "-")
+        if "The original PDF is pre-" in boundary_text or (
+            "The merged PDF is release-synchronized." not in boundary_text
+            and 9 in synchronized_pages
+        ):
+            raise RuntimeError("serialized base page 10 has a stale appendix boundary")
 
 
 def main(argv: list[str] | None = None) -> None:
diff --git a/site/files/reports/JevAny_Tech_Report.pdf b/site/files/reports/JevAny_Tech_Report.pdf
index 5e0b4c473188a421332f0f479802cd82f7c515c2..cedc9fbbb46d310a861c6ee8670378c9763fc2fc 100644
GIT binary patch
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diff --git a/tests/test_build_choice_readout_results.py b/tests/test_build_choice_readout_results.py
index 7cf760c..eb8863d 100644
--- a/tests/test_build_choice_readout_results.py
+++ b/tests/test_build_choice_readout_results.py
@@ -90,6 +90,14 @@ def _external_fixture(path):
 
 def _run_fixture(root, spec, ordinal):
     is_base = spec["weights"] == "frozen_base"
+    canonical_base = f"Qwen/{spec['family']}"
+    snapshot_revision = f"{ordinal + 1}" * 40
+    encoded_repository = spec["public_repository"].replace("/", "--")
+    checkpoint = (
+        f"/fixture/models--{encoded_repository}/snapshots/{snapshot_revision}"
+        if not is_base and spec["key"] != "direct4"
+        else f"/fixture/local-release/{spec['key']}"
+    )
     components = ["choice"] if is_base else ["choice", "native"]
     model = f"fixture-{spec['key']}"
     reports = {}
@@ -148,8 +156,8 @@ def _run_fixture(root, spec, ordinal):
     manifest = {
         "model": model,
         "source": {
-            "base": f"fixture/{spec['family']}" if is_base else None,
-            "checkpoint": None if is_base else f"fixture/{spec['key']}",
+            "base": spec["public_repository"] if is_base else None,
+            "checkpoint": None if is_base else checkpoint,
         },
         "checkpoint_artifacts": None if is_base else {
             "requested": f"fixture/{spec['key']}",
@@ -161,12 +169,17 @@ def _run_fixture(root, spec, ordinal):
                 }
             },
         },
-        "base_loading": {"requested": spec["family"], "resolved": "/fixture/base"},
+        "base_loading": {
+            "requested": spec["family"],
+            "resolved": "/fixture/base",
+            "canonical_base": canonical_base,
+            "canonical_revision": "a" * 40,
+        },
         "predictor": {
             "method": "training-free exact choice-token projection",
             "adapter_applied": not is_base,
             "native_decision_mode": spec["native_decision_mode"],
-            "canonical_base": spec["family"],
+            "canonical_base": canonical_base,
             "canonical_revision": "a" * 40,
         },
         "runtime": {
@@ -250,6 +263,40 @@ def test_build_results_preserves_all_five_run_types_and_protocol_groups(matrix):
     assert len(artifact["source_runs"][0]["artifacts"]["manifest"]["sha256"]) == 64
     assert artifact["source_runs"][0]["runtime"]["torch"] == "2.fixture"
 
+    sources = {run["key"]: run["public_source"] for run in artifact["source_runs"]}
+    assert sources["base4"] == {
+        "repository": "Qwen/Qwen3.5-4B",
+        "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
+        "revision": "a" * 40,
+        "revision_status": "verified",
+        "repository_evidence": "manifest.base_loading.canonical_base",
+        "revision_evidence": "manifest.base_loading.canonical_revision",
+        "evaluated_artifact_sha256": {},
+        "base_model": {
+            "repository": "Qwen/Qwen3.5-4B",
+            "revision": "a" * 40,
+        },
+        "limitation": None,
+    }
+    assert sources["pointer4"]["revision"] == "2" * 40
+    assert sources["pointer4"]["revision_status"] == "verified"
+    assert sources["pointer27"]["revision"] == "5" * 40
+    assert sources["direct4"]["repository"] == (
+        "SimpleJev/JevAny-Qwen3.5-4B-Direct-Token-LoRA"
+    )
+    assert sources["direct4"]["revision"] is None
+    assert sources["direct4"]["revision_status"] == "not_verified"
+    assert sources["direct4"]["revision_evidence"] is None
+    assert "do not prove an immutable public-repository revision" in (
+        sources["direct4"]["limitation"]
+    )
+    assert sources["direct4"]["evaluated_artifact_sha256"] == {
+        "adapter_model.safetensors": {
+            "sha256": "3" * 64,
+            "bytes": 123,
+        }
+    }
+
 
 def test_build_results_rejects_component_count_drift(matrix):
     root, external = matrix
@@ -286,6 +333,17 @@ def test_build_results_rejects_cross_run_dataset_hash_drift(matrix):
         build_results(root, external)
 
 
+def test_build_results_rejects_release_catalog_without_canonical_repositories(
+    matrix, tmp_path
+):
+    root, external = matrix
+    catalog = tmp_path / "model-catalog.json"
+    _write_json(catalog, {"release": "fixture", "released_models": []})
+
+    with pytest.raises(ValueError, match="missing canonical repositories"):
+        build_results(root, external, catalog)
+
+
 def test_synthetic_artifact_and_svg_are_written_only_to_requested_paths(matrix, tmp_path):
     pytest.importorskip("matplotlib")
     root, external = matrix
diff --git a/tests/test_build_external_report_appendix.py b/tests/test_build_external_report_appendix.py
index 43fffe1..ef2281f 100644
--- a/tests/test_build_external_report_appendix.py
+++ b/tests/test_build_external_report_appendix.py
@@ -2,6 +2,7 @@
 import os
 import shutil
 from datetime import datetime, timezone
+from pathlib import Path
 
 import pytest
 
@@ -198,6 +199,13 @@ def _producer_metric(n, correct, excluded):
 
 def _producer_run(root, spec, ordinal):
     is_base = spec["weights"] == "frozen_base"
+    canonical_base = f"Qwen/{spec['family']}"
+    encoded_repository = spec["public_repository"].replace("/", "--")
+    checkpoint = (
+        f"/fixture/models--{encoded_repository}/snapshots/{str(ordinal + 1) * 40}"
+        if spec["key"] != "direct4"
+        else f"/fixture/local-release/{spec['key']}"
+    )
     components = ["choice"] if is_base else ["choice", "native"]
     model = f"producer-{spec['key']}"
     reports = {}
@@ -251,8 +259,8 @@ def _producer_run(root, spec, ordinal):
     manifest = {
         "model": model,
         "source": {
-            "base": f"fixture/{spec['family']}" if is_base else None,
-            "checkpoint": None if is_base else f"fixture/{spec['key']}",
+            "base": spec["public_repository"] if is_base else None,
+            "checkpoint": None if is_base else checkpoint,
         },
         "checkpoint_artifacts": None if is_base else {
             "requested": f"fixture/{spec['key']}",
@@ -264,12 +272,17 @@ def _producer_run(root, spec, ordinal):
                 },
             },
         },
-        "base_loading": {"requested": spec["family"], "resolved": "/fixture/base"},
+        "base_loading": {
+            "requested": spec["family"],
+            "resolved": "/fixture/base",
+            "canonical_base": canonical_base,
+            "canonical_revision": "a" * 40,
+        },
         "predictor": {
             "method": "training-free exact choice-token projection",
             "adapter_applied": not is_base,
             "native_decision_mode": spec["native_decision_mode"],
-            "canonical_base": spec["family"],
+            "canonical_base": canonical_base,
             "canonical_revision": "a" * 40,
         },
         "runtime": {
@@ -511,3 +524,33 @@ def test_choice_artifact_requires_full_3220_to_be_unsupported(tmp_path):
 
     with pytest.raises(ValueError, match="must be explicitly unsupported"):
         load_choice_artifact(choice)
+
+
+def test_published_report_uses_one_current_27b_release():
+    report = Path(__file__).resolve().parents[1] / "reports" / "JevAny_Tech_Report.pdf"
+    pdf = PdfReader(report)
+    assert len(pdf.pages) == 23
+
+    expected = {
+        1: ("86.04", "90.04"),
+        4: ("44,319", "86.04", "90.04", "0.388", "0.195", "0.026"),
+        7: ("86.04",),
+        8: ("44,319", "39.43", "1,261.7", "2,082"),
+    }
+    stale = (
+        "85.76", "90.48", "22,160", "18.83", "602.7", "1,423",
+        "0.392", "0.200", "0.030",
+    )
+    for page_number, values in expected.items():
+        text = pdf.pages[page_number - 1].extract_text() or ""
+        assert all(value in text for value in values)
+        assert all(value not in text for value in stale)
+    compute_text = pdf.pages[7].extract_text() or ""
+    assert "estimated cumulative seconds-per-record timing" in compute_text
+    boundary_text = (pdf.pages[9].extract_text() or "").replace("-\n", "-")
+    assert "The merged PDF is release-synchronized." in boundary_text
+    assert "The original PDF is pre-" not in boundary_text
+
+    method_text = pdf.pages[21].extract_text() or ""
+    assert "current 27B release checkpoint: step 44,319" in method_text
+    assert "matched prompt-v2/runtime reruns" in method_text