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diff --git a/EVALUATION.md b/EVALUATION.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac47b1381dfd276db6f7ae76c3fb85ca4abd7f38
--- /dev/null
+++ b/EVALUATION.md
@@ -0,0 +1,28 @@
+# Executable verification and canonical identity
+
+For responses already decoded to the task's verifier surface, the supplied `code/oat_drgrpo/math_grader.py` entry point is `validated_modebench_outcome_key(response, row["answer"])`. It returns a canonical key only when that same response passes the executable task verifier; invalid responses return `None`.
+
+| Domain | Executable acceptance | Canonical identity |
+| --- | --- | --- |
+| Graph Coloring | Parse assignments; preserve fixed colors and satisfy every edge | Complete color vector |
+| Countdown | Restricted arithmetic AST, exact operand multiplicities and exact target value | Normalized expression AST with registered commutative normalization |
+| Python Factors | Restricted function AST executed in the bounded external worker on the specified cases | Executed return vector |
+| MathIR | Restricted action menu expanded and executed using exact equation transformations | Executed normalized state trajectory |
+| PantryPlan | Bounded ingredient quantities, serving steps, mass/nutrient constraints and dietary restrictions | Ingredient support |
+
+Install the versioned dependencies recorded in `MANIFEST.json`/`requirements-verifier.txt`; prepend the release's `code` directory to Python's import path. The code is supplied as inspectable ordinary source, not automatically executed by the datasets loader.
+
+```python
+import sys
+sys.path.insert(0, "path/to/ModeBench/code")
+from oat_drgrpo.math_grader import validated_modebench_outcome_key
+
+key = validated_modebench_outcome_key(response, row["answer"])
+verified = key is not None
+```
+
+Policy action adapters must run before this grader when the registered interface uses them. In particular, Pantry's six-bit support mask is not an allocation string: use `decode_pantry_support_mask(mask, json.loads(row["answer"]))` from `oat_drgrpo.pantry_support_action`, then grade the returned allocation. The frozen adapter searches only the chosen support's registered quantity grid and returns an invalid sentinel when infeasible; it does not consult a gold support catalogue. `canonical_actions.py` preserves the other registered action-to-verifier adapters.
+
+Python Factors retains its original separate, syntax-restricted worker. Use the supplied implementation for model outputs rather than evaluating arbitrary response strings in a notebook. The code also contains imported legacy task support needed by the original module; this release's dataset configurations contain only the five listed domains.
+
+All copied verifier/template dependencies are authenticated against the completed Level-3 confirmation's source hashes. The release validation checks canonical *problem* identity uniqueness and disjointness, row/feature preservation, and the original structural/support-histogram records. It does not regenerate every exhaustive answer catalogue, retrain models, or claim a new model-admission result.
diff --git a/LICENSE_PROVENANCE.md b/LICENSE_PROVENANCE.md
new file mode 100644
index 0000000000000000000000000000000000000000..09062826b28ccae4af267ea8f6dda72c4e3e1bd5
--- /dev/null
+++ b/LICENSE_PROVENANCE.md
@@ -0,0 +1,17 @@
+# Existing source terms
+
+{
+ "dataset_specific_license": "No separate dataset license found in the frozen dataset roots; no new dataset license assigned by this export.",
+ "pantry_ingredient_source": {
+ "archive_name": "FoodData_Central_foundation_food_json_2026-04-30.zip",
+ "archive_sha256": "186e988ec542e913f51ef62b86a47758e8cdd0d1dc3889e7b055581f3c09c77a",
+ "dataset": "USDA FoodData Central Foundation Foods",
+ "food_object_rows": 363,
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+ "data_file": "data/level3_pantry_plan/eval.parquet",
+ "domain": "pantry_plan",
+ "features": {
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+ "exact_support_histogram": true,
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+ "row_count": true,
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+ "unique_identities": true
+ },
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+ "level3_legal_allocation_count"
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+ ]
+ }
+ ]
+}
diff --git a/PROMPTS.md b/PROMPTS.md
new file mode 100644
index 0000000000000000000000000000000000000000..89920ef9e56164214c0f211d7e9ddcc70fb488fc
--- /dev/null
+++ b/PROMPTS.md
@@ -0,0 +1,9 @@
+# Prompt formatting
+
+Every Parquet row preserves the original `problem` string and the original serialized `answer` specification. Send the problem through the selected model/experiment's frozen template; do not concatenate the answer specification or support metadata into the prompt.
+
+For the original Qwen Level-1 training interface, Graph, Countdown, Python, and MathIR use `qwen_boxed`; Pantry uses `qwen_pantry_support_mask`. The copied `code/oat_drgrpo/templates.py` preserves the exact template text and Qwen/Falcon role markers. `provenance/source_code/ops/check_paper_domain_prompts.py` binds the original paper examples to those strings.
+
+Level-2/Level-3 admission uses the versioned interface in `provenance/source_code/ops/evaluate_modebench_level2_viability.py` and `evaluate_modebench_level3.py`: Graph uses the direct boxed interface, while the other domains use the registered hybrid solver interface and their legal-output constraints. That admission interface and its model-native tokenizer rendering are distinct from a training experiment's fixed template. Copying a dataset alone does not reproduce a decoder or inference-engine configuration.
+
+The original training and evaluation launchers remain the source of model-specific precision, length limits, guided-decoding profiles, draw seeds, and selected model checkpoints. The files here preserve the shared prompt/template implementation and the dataset rows; they do not silently standardize these experiment-specific choices.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f1008a760e8a2c480105449ad2cd51f99dff4777
--- /dev/null
+++ b/README.md
@@ -0,0 +1,192 @@
+---
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- reasoning
+- reinforcement-learning
+- verifiable-rewards
+- modebench
+pretty_name: ModeBench
+configs:
+- config_name: level1_countdown
+ data_files:
+ - split: train
+ path: data/level1_countdown/train.parquet
+ - split: eval
+ path: data/level1_countdown/eval.parquet
+- config_name: level1_graph_coloring
+ data_files:
+ - split: train
+ path: data/level1_graph_coloring/train.parquet
+ - split: eval
+ path: data/level1_graph_coloring/eval.parquet
+- config_name: level1_graph_coloring_unique_answer
+ data_files:
+ - split: eval
+ path: data/level1_graph_coloring_unique_answer/eval.parquet
+- config_name: level1_mathir
+ data_files:
+ - split: train
+ path: data/level1_mathir/train.parquet
+ - split: eval
+ path: data/level1_mathir/eval.parquet
+- config_name: level1_pantry_plan
+ data_files:
+ - split: train
+ path: data/level1_pantry_plan/train.parquet
+ - split: dev
+ path: data/level1_pantry_plan/dev.parquet
+ - split: eval
+ path: data/level1_pantry_plan/eval.parquet
+- config_name: level1_python_factors
+ data_files:
+ - split: train
+ path: data/level1_python_factors/train.parquet
+ - split: eval
+ path: data/level1_python_factors/eval.parquet
+- config_name: level2_countdown
+ data_files:
+ - split: train
+ path: data/level2_countdown/train.parquet
+ - split: dev
+ path: data/level2_countdown/dev.parquet
+ - split: eval
+ path: data/level2_countdown/eval.parquet
+- config_name: level2_graph_coloring
+ data_files:
+ - split: train
+ path: data/level2_graph_coloring/train.parquet
+ - split: dev
+ path: data/level2_graph_coloring/dev.parquet
+ - split: eval
+ path: data/level2_graph_coloring/eval.parquet
+- config_name: level2_mathir
+ data_files:
+ - split: train
+ path: data/level2_mathir/train.parquet
+ - split: dev
+ path: data/level2_mathir/dev.parquet
+ - split: eval
+ path: data/level2_mathir/eval.parquet
+- config_name: level2_pantry_plan
+ data_files:
+ - split: train
+ path: data/level2_pantry_plan/train.parquet
+ - split: dev
+ path: data/level2_pantry_plan/dev.parquet
+ - split: eval
+ path: data/level2_pantry_plan/eval.parquet
+- config_name: level2_python_factors
+ data_files:
+ - split: train
+ path: data/level2_python_factors/train.parquet
+ - split: dev
+ path: data/level2_python_factors/dev.parquet
+ - split: eval
+ path: data/level2_python_factors/eval.parquet
+- config_name: level3_countdown
+ data_files:
+ - split: train
+ path: data/level3_countdown/train.parquet
+ - split: dev
+ path: data/level3_countdown/dev.parquet
+ - split: eval
+ path: data/level3_countdown/eval.parquet
+- config_name: level3_graph_coloring
+ data_files:
+ - split: train
+ path: data/level3_graph_coloring/train.parquet
+ - split: dev
+ path: data/level3_graph_coloring/dev.parquet
+ - split: eval
+ path: data/level3_graph_coloring/eval.parquet
+- config_name: level3_mathir
+ data_files:
+ - split: train
+ path: data/level3_mathir/train.parquet
+ - split: dev
+ path: data/level3_mathir/dev.parquet
+ - split: eval
+ path: data/level3_mathir/eval.parquet
+- config_name: level3_pantry_plan
+ data_files:
+ - split: train
+ path: data/level3_pantry_plan/train.parquet
+ - split: dev
+ path: data/level3_pantry_plan/dev.parquet
+ - split: eval
+ path: data/level3_pantry_plan/eval.parquet
+- config_name: level3_python_factors
+ data_files:
+ - split: train
+ path: data/level3_python_factors/train.parquet
+ - split: dev
+ path: data/level3_python_factors/dev.parquet
+ - split: eval
+ path: data/level3_python_factors/eval.parquet
+---
+
+# ModeBench
+
+ModeBench measures both correctness and the variety of correct answers produced by language models. Each problem has an executable verifier and a canonical definition of answer identity, so distinct wording alone does not count as a new solution.
+
+This release contains three frozen benchmark levels across **Graph Coloring, Countdown, Python Factors, MathIR, and PantryPlan**. It preserves every original feature, row, and row order. The `answer` field is a serialized executable specification, not a target answer to include in the model prompt.
+
+[Level 1](#level-1) · [Level 2](#level-2) · [Level 3](#level-3) · [Loading](#loading) · [Evaluation](#evaluation) · [Manifest](MANIFEST.json) · [Validation](VALIDATION.json)
+
+The accompanying [model and research-artifact archive](https://huggingface.co/od2961/maxent-grpo-models) contains experiment-specific model indexes and mappings to the paper results. Dataset publication does not imply completed Level-3 training results.
+
+
+## Level 1
+
+The original benchmark uses 384 training problems and 128 primary evaluation problems per domain. PantryPlan additionally has its original 64-row development split. Graph Coloring also includes a separate 128-row `unique_answer` evaluation subset; it is published as the auxiliary `level1_graph_coloring_unique_answer` configuration and is not the primary multi-answer paper evaluation.
+
+
+## Level 2
+
+Level 2 is the frozen `modebench_harder_v2_matched_r5` release. It changes problem structure while retaining the verifier and canonicalizer contracts. Each domain has 384 training, 128 development, and 128 evaluation rows. Support histograms follow the recorded matched reference construction; refer to the original identity manifest for the precise reference splits and scaling factors.
+
+
+## Level 3
+
+Level 3 is the E122-frozen `modebench_level3_matched_v3` release, with 384 training, 128 development, and 128 evaluation rows per domain. It preserves Level-2 support histograms. Admission used adaptive second-round matching to fixed historical Level-1 measurements: Graph and Python have fresh v3 confirmation, while Countdown, MathIR, and Pantry retain prior split bytes and confirmation evidence. This is not a claim of statistical equivalence, or of five fresh same-round confirmations.
+
+The original [identity manifest](provenance/source_data/modebench_level3_matched_v3/identity.json) still records `pending_fresh_candidate_confirmation`. That historical record is preserved verbatim. The later [confirmation report](provenance/confirmation_report.json) records `matched_fixed_reference` and binds that exact dataset identity. Neither record has been rewritten for publication.
+
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("od2961/ModeBench", "level2_countdown")
+train = ds["train"]
+development = ds["dev"]
+evaluation = ds["eval"]
+
+# Raw problem text goes to the policy. Keep the executable answer spec private
+# from the policy and use it only in the verifier.
+problem = evaluation[0]["problem"]
+reference_spec = evaluation[0]["answer"]
+```
+
+Use `level1_`, `level2_`, or `level3_` followed by `graph_coloring`, `countdown`, `python_factors`, `mathir`, or `pantry_plan`. Only existing splits are exposed; Level 1 generally has no `dev` split. No `test` split is invented. The Parquet files load through the standard datasets loader without remote dataset code. Pin a dataset revision for reproducible experiments.
+
+
+## Evaluation
+
+Use the original held-out `eval` rows and the frozen prompt, decoding, and verifier settings for a stated comparison. Use `dev` only where the original release provides it. Do not fit prompts, choose checkpoints, or tune methods on evaluation outcomes; training, development, and evaluation roles remain separate after public release.
+
+The same executable validation produces acceptance and canonical identity. Report correctness and distinct correct identities separately: with K sampled answers, pass@K is whether any answer verifies, and distinct@K counts unique verified canonical keys. The reported main benchmark uses K=8; the papers specify the seeds, draws, and training checkpoints. [Evaluation and prompt guide](EVALUATION.md) documents the supplied verifier interface and its versioned source.
+
+The support-count metadata and reference specifications are for construction, checking, and analysis. Do not supply gold support catalogues or hidden evaluation references to the policy. Original prompt strings are retained; [prompt formatting](PROMPTS.md) distinguishes raw problems from the model-specific chat and decoding interfaces.
+
+## Files and provenance
+
+`MANIFEST.json` records every source identity, feature schema, ordered-row digest, split mapping, and Parquet digest. `VALIDATION.json` records full row and feature round trips, unchanged source files, canonical problem-identity checks, and support matching. `code/oat_drgrpo/` preserves verifier and template source files byte for byte; `provenance/` preserves original identities and construction/admission records.
+
+## License provenance
+
+The original repository's Apache-2.0 source-code license is preserved in [licenses/source_repository_APACHE_2.0.txt](licenses/source_repository_APACHE_2.0.txt), and copied source copyright notices remain intact. The frozen dataset roots contain no separate dataset-specific license declaration; this export does not invent new dataset license terms. PantryPlan ingredient provenance records USDA FoodData Central Foundation Foods under CC0/public-domain source terms in [pantry_ingredient_source.json](provenance/pantry_ingredient_source.json).
diff --git a/SPLITS.md b/SPLITS.md
new file mode 100644
index 0000000000000000000000000000000000000000..6e4c10f02da9f24952462b4f2e909035847af2f2
--- /dev/null
+++ b/SPLITS.md
@@ -0,0 +1,46 @@
+# Configurations and original split mappings
+
+| Config | Split | Rows | Original serialized subset |
+| --- | --- | ---: | --- |
+| `level1_graph_coloring` | `train` | 384 | `train` |
+| `level1_graph_coloring` | `eval` | 128 | `multi_answer` |
+| `level1_graph_coloring_unique_answer` | `eval` | 128 | `unique_answer` |
+| `level1_countdown` | `train` | 384 | `train` |
+| `level1_countdown` | `eval` | 128 | `multi_answer` |
+| `level1_python_factors` | `train` | 384 | `train` |
+| `level1_python_factors` | `eval` | 128 | `multi_answer` |
+| `level1_mathir` | `train` | 384 | `train` |
+| `level1_mathir` | `eval` | 128 | `multi_answer` |
+| `level1_pantry_plan` | `train` | 384 | `train` |
+| `level1_pantry_plan` | `dev` | 64 | `multi_answer` |
+| `level1_pantry_plan` | `eval` | 128 | `multi_answer` |
+| `level2_graph_coloring` | `train` | 384 | `train` |
+| `level2_graph_coloring` | `dev` | 128 | `multi_answer` |
+| `level2_graph_coloring` | `eval` | 128 | `multi_answer` |
+| `level2_countdown` | `train` | 384 | `train` |
+| `level2_countdown` | `dev` | 128 | `multi_answer` |
+| `level2_countdown` | `eval` | 128 | `multi_answer` |
+| `level2_python_factors` | `train` | 384 | `train` |
+| `level2_python_factors` | `dev` | 128 | `multi_answer` |
+| `level2_python_factors` | `eval` | 128 | `multi_answer` |
+| `level2_mathir` | `train` | 384 | `train` |
+| `level2_mathir` | `dev` | 128 | `multi_answer` |
+| `level2_mathir` | `eval` | 128 | `multi_answer` |
+| `level2_pantry_plan` | `train` | 384 | `train` |
+| `level2_pantry_plan` | `dev` | 128 | `multi_answer` |
+| `level2_pantry_plan` | `eval` | 128 | `multi_answer` |
+| `level3_graph_coloring` | `train` | 384 | `train` |
+| `level3_graph_coloring` | `dev` | 128 | `multi_answer` |
+| `level3_graph_coloring` | `eval` | 128 | `multi_answer` |
+| `level3_countdown` | `train` | 384 | `train` |
+| `level3_countdown` | `dev` | 128 | `multi_answer` |
+| `level3_countdown` | `eval` | 128 | `multi_answer` |
+| `level3_python_factors` | `train` | 384 | `train` |
+| `level3_python_factors` | `dev` | 128 | `multi_answer` |
+| `level3_python_factors` | `eval` | 128 | `multi_answer` |
+| `level3_mathir` | `train` | 384 | `train` |
+| `level3_mathir` | `dev` | 128 | `multi_answer` |
+| `level3_mathir` | `eval` | 128 | `multi_answer` |
+| `level3_pantry_plan` | `train` | 384 | `train` |
+| `level3_pantry_plan` | `dev` | 128 | `multi_answer` |
+| `level3_pantry_plan` | `eval` | 128 | `multi_answer` |
diff --git a/VALIDATION.json b/VALIDATION.json
new file mode 100644
index 0000000000000000000000000000000000000000..2164c02230f3cdd416efec76c8fbebb6c5d2c79a
--- /dev/null
+++ b/VALIDATION.json
@@ -0,0 +1,396 @@
+{
+ "all_L2_L3_support_histograms_equal": true,
+ "all_primary_problem_identities_disjoint_across_published_splits": true,
+ "all_source_files_unchanged": true,
+ "config_count": 16,
+ "limits": [
+ "Canonical problem identities and original structural checks are validated; this export does not regenerate every exhaustive answer catalogue or rerun model admission.",
+ "Original source split names and auxiliary Graph unique-answer rows are preserved; no test split is created."
+ ],
+ "row_count": 9152,
+ "schema": "modebench-hf-roundtrip-validation-v1",
+ "source_verifier_code_bound_to_completed_confirmation": true,
+ "split_count": 42,
+ "splits": [
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_graph_coloring_unique_answer",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level1_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": false,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level2_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_graph_coloring",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_countdown",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_python_factors",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_mathir",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "train"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "dev"
+ },
+ {
+ "canonical_problem_identities_unique": true,
+ "config": "level3_pantry_plan",
+ "feature_schema_equal": true,
+ "frozen_row_digest_verified": true,
+ "row_order_equal": true,
+ "source_rows_equal_export_rows": true,
+ "split": "eval"
+ }
+ ],
+ "status": "passed",
+ "validated_at_utc": "2026-09-11T07:38:24.101092+00:00"
+}
diff --git a/code/oat_drgrpo/__init__.py b/code/oat_drgrpo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..13437e8afc6380b580b0dc8ed00c20798ad54c1c
--- /dev/null
+++ b/code/oat_drgrpo/__init__.py
@@ -0,0 +1 @@
+"""Direct MaxEnt-GRPO primitives and the landed xDr.GRPO surrogate."""
diff --git a/code/oat_drgrpo/ant_maze_worker_v10.py b/code/oat_drgrpo/ant_maze_worker_v10.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6c40463ecd2992c71d2547d5f03906f64541b39
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v10.py
@@ -0,0 +1,207 @@
+"""Hash-bound AntMaze executor for the admitted v10 waypoint controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+
+
+ROOT = Path(
+ os.environ.get("OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[2])
+).resolve()
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v10.zip"
+RECEIPT_PATH = (
+ ROOT / "var/maze_runtime/controllers/ant_waypoint_v10.evaluation.json"
+)
+TRAINING_IDENTITY_PATH = (
+ ROOT / "var/artifacts/ant_waypoint_controller_v10_identity.json"
+)
+WAYPOINT_DISTANCE = 4.0
+WAYPOINT_SUCCESS_THRESHOLD = 0.45
+_MODEL = None
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def controller_receipt_sha256() -> str:
+ controller_identity()
+ return _sha256(RECEIPT_PATH)
+
+
+def controller_identity() -> dict[str, Any]:
+ receipt_sha256 = _sha256(RECEIPT_PATH)
+ model_sha256 = _sha256(MODEL_PATH)
+ training_identity_sha256 = _sha256(TRAINING_IDENTITY_PATH)
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ training_identity = json.loads(
+ TRAINING_IDENTITY_PATH.read_text(encoding="utf-8")
+ )
+ if (
+ receipt.get("status") != "pass"
+ or receipt.get("decision") != "admitted_to_fresh_maze_route_gate_v10"
+ ):
+ raise RuntimeError("Ant v10 controller is not admitted")
+ if receipt.get("seed") != 73010 or receipt.get("timesteps") != 2_000_000:
+ raise RuntimeError("Ant v10 receipt differs from its frozen training identity")
+ if (
+ training_identity.get("job_id") != 30193111
+ or training_identity.get("seed") != 73010
+ ):
+ raise RuntimeError("Ant v10 training identity is not the frozen job")
+ checks = receipt.get("checks", {})
+ required_checks = {
+ "episode_count",
+ "success_rate_at_least_0p90",
+ "minimum_heading_success_rate_at_least_0p75",
+ "minimum_map_success_rate_at_least_0p75",
+ "unhealthy_termination_rate_at_most_0p10",
+ "median_success_steps_at_most_300",
+ "all_metrics_finite",
+ }
+ if not isinstance(checks, dict) or not required_checks.issubset(checks) or not all(
+ checks[key] is True for key in required_checks
+ ):
+ raise RuntimeError("Ant v10 receipt contains a failed controller check")
+ if receipt.get("hashes", {}).get("model_sha256") != model_sha256:
+ raise RuntimeError("Ant v10 receipt does not bind the controller model")
+ if receipt.get("training_waypoint_distances") != [WAYPOINT_DISTANCE]:
+ raise RuntimeError("Ant v10 receipt has a different waypoint distance")
+ if receipt.get("waypoint_distance") != WAYPOINT_DISTANCE:
+ raise RuntimeError("Ant v10 receipt has a different executor distance")
+ if receipt.get("success_threshold") != WAYPOINT_SUCCESS_THRESHOLD:
+ raise RuntimeError("Ant v10 receipt has a different waypoint threshold")
+ route_identity_path = os.environ.get("OAT_ZERO_PROTOCOL_IDENTITY")
+ if route_identity_path:
+ route_identity = json.loads(
+ Path(route_identity_path).read_text(encoding="utf-8")
+ )
+ expected = {
+ "controller_receipt_sha256": receipt_sha256,
+ "controller_model_sha256": model_sha256,
+ "controller_training_identity_sha256": training_identity_sha256,
+ }
+ if route_identity.get("schema_version") != (
+ "ant-maze-v10-route-generation-identity-v1"
+ ):
+ raise RuntimeError("Ant v10 route identity schema mismatch")
+ if any(route_identity.get(key) != value for key, value in expected.items()):
+ raise RuntimeError("Ant v10 route identity does not bind the controller")
+ return receipt
+
+
+def _model():
+ global _MODEL
+ controller_identity()
+ if _MODEL is None:
+ from stable_baselines3 import PPO
+
+ _MODEL = PPO.load(MODEL_PATH, device="cpu")
+ return _MODEL
+
+
+def _heading(token: str):
+ import numpy as np
+
+ diagonal = 1.0 / math.sqrt(2.0)
+ values = {
+ "N": (0.0, 1.0), "NE": (diagonal, diagonal), "E": (1.0, 0.0),
+ "SE": (diagonal, -diagonal), "S": (0.0, -1.0),
+ "SW": (-diagonal, -diagonal), "W": (-1.0, 0.0),
+ "NW": (-diagonal, diagonal),
+ }
+ return np.asarray(values[token], dtype=np.float32)
+
+
+def execute_ant_v10_raw(candidate: str, raw_spec: Mapping[str, Any]) -> dict[str, Any]:
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != ANT_MAZE_VERIFIER:
+ raise ValueError("AntMaze executor received a non-AntMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["ant_environment_sha256"]:
+ raise ValueError("AntMaze environment hash differs from installed runtime")
+ if spec.controller_sha256 != controller_receipt_sha256():
+ raise ValueError("AntMaze controller hash differs from frozen v10 receipt")
+ tokens = parse_maze_action_program(candidate, spec)
+ model = _model()
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ try:
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={"reset_cell": list(spec.reset_cell), "goal_cell": list(spec.goal_cell)},
+ )
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ terminated = truncated = False
+ for token in tokens:
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ target = current + WAYPOINT_DISTANCE * _heading(token)
+ for _ in range(spec.action_repeat):
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ relative = np.clip((target - current) / WAYPOINT_DISTANCE, -1.0, 1.0)
+ controller_input = np.concatenate([
+ np.asarray(observation["observation"], dtype=np.float32), relative,
+ ])
+ action, _ = model.predict(controller_input, deterministic=True)
+ observation, _reward, terminated, truncated, info = env.step(action)
+ simulator_steps += 1
+ trajectory.append(observation["achieved_goal"].astype(float).tolist())
+ success = bool(info.get("success", False))
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ waypoint_reached = float(np.linalg.norm(target - current)) <= WAYPOINT_SUCCESS_THRESHOLD
+ if success or waypoint_reached or terminated or truncated:
+ break
+ if success or terminated or truncated:
+ break
+ distance = float(np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ ))
+ finally:
+ env.close()
+ return {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": spec.controller_sha256,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+
+
+def execute_ant_v10(candidate: str, raw_spec: Mapping[str, Any]):
+ execution = execute_ant_v10_raw(candidate, raw_spec)
+ return validate_maze_execution(candidate, raw_spec, execution), execution
diff --git a/code/oat_drgrpo/ant_maze_worker_v11.py b/code/oat_drgrpo/ant_maze_worker_v11.py
new file mode 100644
index 0000000000000000000000000000000000000000..b703dbcff59ca32229dab9285ce15598136f0c08
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v11.py
@@ -0,0 +1,91 @@
+"""Hash-bound AntMaze executor for the admitted v11 waypoint controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from . import ant_maze_worker_v10 as base
+
+
+ROOT = base.ROOT
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v11.zip"
+RECEIPT_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v11.evaluation.json"
+TRAINING_IDENTITY_PATH = ROOT / "var/artifacts/ant_waypoint_controller_v11_identity.json"
+WAYPOINT_DISTANCE = base.WAYPOINT_DISTANCE
+WAYPOINT_SUCCESS_THRESHOLD = base.WAYPOINT_SUCCESS_THRESHOLD
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def controller_receipt_sha256() -> str:
+ controller_identity()
+ return _sha256(RECEIPT_PATH)
+
+
+def controller_identity() -> dict[str, Any]:
+ receipt_sha256 = _sha256(RECEIPT_PATH)
+ model_sha256 = _sha256(MODEL_PATH)
+ training_identity_sha256 = _sha256(TRAINING_IDENTITY_PATH)
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ training_identity = json.loads(TRAINING_IDENTITY_PATH.read_text(encoding="utf-8"))
+ if receipt.get("status") != "pass" or receipt.get("decision") != "admitted_to_fresh_maze_route_gate_v11":
+ raise RuntimeError("Ant v11 controller is not admitted")
+ if receipt.get("seed") != 73011 or receipt.get("timesteps") != 2_000_000:
+ raise RuntimeError("Ant v11 receipt differs from its frozen training identity")
+ if training_identity.get("job_id") != 30198291 or training_identity.get("seed") != 73011:
+ raise RuntimeError("Ant v11 training identity is not the frozen job")
+ checks = receipt.get("checks", {})
+ required_checks = {
+ "episode_count", "success_rate_at_least_0p90",
+ "minimum_heading_success_rate_at_least_0p75",
+ "minimum_map_success_rate_at_least_0p75",
+ "unhealthy_termination_rate_at_most_0p10",
+ "median_success_steps_at_most_300", "all_metrics_finite",
+ }
+ if not isinstance(checks, dict) or not required_checks.issubset(checks) or not all(checks[key] is True for key in required_checks):
+ raise RuntimeError("Ant v11 receipt contains a failed controller check")
+ if receipt.get("hashes", {}).get("model_sha256") != model_sha256:
+ raise RuntimeError("Ant v11 receipt does not bind the controller model")
+ if receipt.get("training_waypoint_distances") != [WAYPOINT_DISTANCE] or receipt.get("waypoint_distance") != WAYPOINT_DISTANCE:
+ raise RuntimeError("Ant v11 receipt has a different waypoint distance")
+ if receipt.get("success_threshold") != WAYPOINT_SUCCESS_THRESHOLD:
+ raise RuntimeError("Ant v11 receipt has a different waypoint threshold")
+ route_identity_path = os.environ.get("OAT_ZERO_PROTOCOL_IDENTITY")
+ if route_identity_path:
+ route_identity = json.loads(Path(route_identity_path).read_text(encoding="utf-8"))
+ expected = {
+ "controller_receipt_sha256": receipt_sha256,
+ "controller_model_sha256": model_sha256,
+ "controller_training_identity_sha256": training_identity_sha256,
+ }
+ if route_identity.get("schema_version") != "ant-maze-v11-route-generation-identity-v1":
+ raise RuntimeError("Ant v11 route identity schema mismatch")
+ if any(route_identity.get(key) != value for key, value in expected.items()):
+ raise RuntimeError("Ant v11 route identity does not bind the controller")
+ return receipt
+
+
+base.MODEL_PATH = MODEL_PATH
+base.RECEIPT_PATH = RECEIPT_PATH
+base.TRAINING_IDENTITY_PATH = TRAINING_IDENTITY_PATH
+base.controller_identity = controller_identity
+base.controller_receipt_sha256 = controller_receipt_sha256
+base._MODEL = None
+
+
+def execute_ant_v11_raw(candidate: str, raw_spec: Mapping[str, Any]) -> dict[str, Any]:
+ return base.execute_ant_v10_raw(candidate, raw_spec)
+
+
+def execute_ant_v11(candidate: str, raw_spec: Mapping[str, Any]):
+ return base.execute_ant_v10(candidate, raw_spec)
diff --git a/code/oat_drgrpo/ant_maze_worker_v12.py b/code/oat_drgrpo/ant_maze_worker_v12.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcf98c2fed9dc9120a7bcd1b82dd428bf6aac9dc
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v12.py
@@ -0,0 +1,248 @@
+"""Grid-anchored AntMaze executor for the admitted v11 controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from . import ant_maze_worker_v10 as executor
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+
+
+ROOT = executor.ROOT
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v11.zip"
+RECEIPT_PATH = (
+ ROOT / "var/maze_runtime/controllers/ant_waypoint_v11.evaluation.json"
+)
+TRAINING_IDENTITY_PATH = (
+ ROOT / "var/artifacts/ant_waypoint_controller_v11_identity.json"
+)
+WAYPOINT_DISTANCE = 4.0
+WAYPOINT_SUCCESS_THRESHOLD = 0.45
+TARGETING_VERSION = "initial-grid-cumulative-v12"
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def _canonical_sha256(value: Any) -> str:
+ return hashlib.sha256(
+ json.dumps(
+ value,
+ allow_nan=False,
+ ensure_ascii=True,
+ separators=(",", ":"),
+ sort_keys=True,
+ ).encode("ascii")
+ ).hexdigest()
+
+
+def _executor_identity() -> dict[str, Any]:
+ return {
+ "targeting_version": TARGETING_VERSION,
+ "worker_source_sha256": _sha256(Path(__file__)),
+ "controller_receipt_sha256": _sha256(RECEIPT_PATH),
+ "controller_model_sha256": _sha256(MODEL_PATH),
+ "controller_training_identity_sha256": _sha256(TRAINING_IDENTITY_PATH),
+ "waypoint_distance": WAYPOINT_DISTANCE,
+ "waypoint_success_threshold": WAYPOINT_SUCCESS_THRESHOLD,
+ }
+
+
+def controller_receipt_sha256() -> str:
+ controller_identity()
+ return _canonical_sha256(_executor_identity())
+
+
+def controller_identity() -> dict[str, Any]:
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ training_identity = json.loads(
+ TRAINING_IDENTITY_PATH.read_text(encoding="utf-8")
+ )
+ if (
+ receipt.get("status") != "pass"
+ or receipt.get("decision") != "admitted_to_fresh_maze_route_gate_v11"
+ or receipt.get("seed") != 73011
+ or receipt.get("timesteps") != 2_000_000
+ ):
+ raise RuntimeError("Ant v12 requires the exact admitted v11 controller")
+ if (
+ training_identity.get("job_id") != 30198291
+ or training_identity.get("seed") != 73011
+ ):
+ raise RuntimeError("Ant v12 training identity differs from admitted v11")
+ checks = receipt.get("checks", {})
+ if not isinstance(checks, dict) or not checks or not all(
+ value is True for value in checks.values()
+ ):
+ raise RuntimeError("Ant v12 received a failed v11 controller check")
+ if receipt.get("hashes", {}).get("model_sha256") != _sha256(MODEL_PATH):
+ raise RuntimeError("Ant v12 receipt does not bind the v11 model")
+ if (
+ receipt.get("training_waypoint_distances") != [WAYPOINT_DISTANCE]
+ or receipt.get("waypoint_distance") != WAYPOINT_DISTANCE
+ or receipt.get("success_threshold") != WAYPOINT_SUCCESS_THRESHOLD
+ ):
+ raise RuntimeError("Ant v12 local waypoint contract drift")
+ route_identity_path = os.environ.get("OAT_ZERO_PROTOCOL_IDENTITY")
+ if route_identity_path:
+ route_identity = json.loads(
+ Path(route_identity_path).read_text(encoding="utf-8")
+ )
+ expected = _executor_identity()
+ if route_identity.get("schema_version") != (
+ "ant-maze-v12-route-generation-identity-v1"
+ ):
+ raise RuntimeError("Ant v12 route identity schema mismatch")
+ for key, value in expected.items():
+ if route_identity.get(key) != value:
+ raise RuntimeError(f"Ant v12 route identity does not bind {key}")
+ return receipt
+
+
+def _model():
+ executor.MODEL_PATH = MODEL_PATH
+ executor.RECEIPT_PATH = RECEIPT_PATH
+ executor.TRAINING_IDENTITY_PATH = TRAINING_IDENTITY_PATH
+ executor.controller_identity = controller_identity
+ executor.controller_receipt_sha256 = controller_receipt_sha256
+ return executor._model()
+
+
+def _heading(token: str):
+ import numpy as np
+
+ diagonal = 1.0 / math.sqrt(2.0)
+ return np.asarray(
+ {
+ "N": (0.0, 1.0),
+ "NE": (diagonal, diagonal),
+ "E": (1.0, 0.0),
+ "SE": (diagonal, -diagonal),
+ "S": (0.0, -1.0),
+ "SW": (-diagonal, -diagonal),
+ "W": (-1.0, 0.0),
+ "NW": (-diagonal, diagonal),
+ }[token],
+ dtype=np.float32,
+ )
+
+
+def execute_ant_v12_raw(candidate: str, raw_spec: Mapping[str, Any]) -> dict[str, Any]:
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != ANT_MAZE_VERIFIER:
+ raise ValueError("AntMaze v12 received a non-AntMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["ant_environment_sha256"]:
+ raise ValueError("AntMaze environment hash differs from installed runtime")
+ if spec.controller_sha256 != controller_receipt_sha256():
+ raise ValueError("AntMaze controller hash differs from frozen v12 executor")
+ tokens = parse_maze_action_program(candidate, spec)
+ model = _model()
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ segment_steps = []
+ nominal_targets = []
+ try:
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={
+ "reset_cell": list(spec.reset_cell),
+ "goal_cell": list(spec.goal_cell),
+ },
+ )
+ initial = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32).copy()
+ cumulative_heading = np.zeros(2, dtype=np.float32)
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ terminated = truncated = False
+ for token in tokens:
+ cumulative_heading += _heading(token)
+ target = initial + WAYPOINT_DISTANCE * cumulative_heading
+ nominal_targets.append(target.astype(float).tolist())
+ before_segment = simulator_steps
+ for _ in range(spec.action_repeat):
+ current = np.asarray(
+ env.unwrapped.data.qpos[:2], dtype=np.float32
+ )
+ relative = np.clip(
+ (target - current) / WAYPOINT_DISTANCE, -1.0, 1.0
+ )
+ controller_input = np.concatenate(
+ [
+ np.asarray(observation["observation"], dtype=np.float32),
+ relative,
+ ]
+ )
+ action, _ = model.predict(controller_input, deterministic=True)
+ observation, _reward, terminated, truncated, info = env.step(action)
+ simulator_steps += 1
+ trajectory.append(
+ observation["achieved_goal"].astype(float).tolist()
+ )
+ success = bool(info.get("success", False))
+ current = np.asarray(
+ env.unwrapped.data.qpos[:2], dtype=np.float32
+ )
+ waypoint_reached = float(np.linalg.norm(target - current)) <= (
+ WAYPOINT_SUCCESS_THRESHOLD
+ )
+ if success or waypoint_reached or terminated or truncated:
+ break
+ segment_steps.append(simulator_steps - before_segment)
+ if success or terminated or truncated:
+ break
+ distance = float(
+ np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ )
+ )
+ finally:
+ env.close()
+ return {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": spec.controller_sha256,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "targeting_version": TARGETING_VERSION,
+ "nominal_targets_xy": nominal_targets,
+ "segment_steps": segment_steps,
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+
+
+def execute_ant_v12(candidate: str, raw_spec: Mapping[str, Any]):
+ execution = execute_ant_v12_raw(candidate, raw_spec)
+ return validate_maze_execution(candidate, raw_spec, execution), execution
diff --git a/code/oat_drgrpo/ant_maze_worker_v5.py b/code/oat_drgrpo/ant_maze_worker_v5.py
new file mode 100644
index 0000000000000000000000000000000000000000..129e24b2a95bdf64247b78d56d49544f0cee19f0
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v5.py
@@ -0,0 +1,170 @@
+"""Hash-bound AntMaze executor for the admitted v5 macro controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+
+
+ROOT = Path(
+ os.environ.get("OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[2])
+).resolve()
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_heading_v1.zip"
+RECEIPT_PATH = ROOT / "var/maze_runtime/controllers/ant_heading_v5.evaluation.json"
+RECEIPT_SHA256 = (
+ "324d3301b8e21b4dbbfcc2e6b9a87aba479bd2da5aa3a040cff24adebaf8828e"
+)
+MODEL_SHA256 = (
+ "526b669bb14cf8a07b44a1c725f94411632a3ac8a8e84966db09336cc96e4b0f"
+)
+_MODEL = None
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def controller_identity() -> dict[str, Any]:
+ """Load and verify the immutable v5 controller receipt."""
+
+ if _sha256(RECEIPT_PATH) != RECEIPT_SHA256:
+ raise RuntimeError("Ant v5 controller receipt hash mismatch")
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ if receipt.get("status") != "pass":
+ raise RuntimeError("Ant v5 controller is not admitted")
+ if _sha256(MODEL_PATH) != MODEL_SHA256:
+ raise RuntimeError("Ant v5 controller model hash mismatch")
+ if receipt.get("hashes", {}).get("model_sha256") != MODEL_SHA256:
+ raise RuntimeError("Ant v5 receipt does not bind the controller model")
+ return receipt
+
+
+def _model():
+ global _MODEL
+ receipt = controller_identity()
+ if _MODEL is None:
+ from stable_baselines3 import PPO
+
+ _MODEL = PPO.load(MODEL_PATH, device="cpu")
+ return _MODEL, receipt["macro_identity"]
+
+
+def execute_ant_v5_raw(
+ candidate: str,
+ raw_spec: Mapping[str, Any],
+) -> dict[str, Any]:
+ """Execute one program and retain its raw record even on goal failure."""
+
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != ANT_MAZE_VERIFIER:
+ raise ValueError("AntMaze executor received a non-AntMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["ant_environment_sha256"]:
+ raise ValueError("AntMaze environment hash differs from installed runtime")
+ if spec.controller_sha256 != RECEIPT_SHA256:
+ raise ValueError("AntMaze controller hash differs from frozen v5 receipt")
+ tokens = parse_maze_action_program(candidate, spec)
+ model, identity = _model()
+ names = tuple(str(value) for value in identity["command_names"])
+ commands = np.asarray(identity["commands"], dtype=np.float32)
+ macros = tuple(
+ tuple(int(index) for index in macro) for macro in identity["macros"]
+ )
+ token_to_index = {name: index for index, name in enumerate(names)}
+ if tuple(token_to_index) != spec.action_tokens:
+ raise RuntimeError("Ant v5 macro alphabet differs from the maze spec")
+
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ try:
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={
+ "reset_cell": list(spec.reset_cell),
+ "goal_cell": list(spec.goal_cell),
+ },
+ )
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ terminated = truncated = False
+ for token in tokens:
+ macro = macros[token_to_index[token]]
+ for primitive_step in range(spec.action_repeat):
+ segment = min(
+ primitive_step * len(macro) // spec.action_repeat,
+ len(macro) - 1,
+ )
+ command = commands[macro[segment]]
+ controller_input = np.concatenate(
+ [
+ np.asarray(observation["observation"], dtype=np.float32),
+ command,
+ ]
+ )
+ action, _ = model.predict(controller_input, deterministic=True)
+ observation, _reward, terminated, truncated, info = env.step(
+ action
+ )
+ simulator_steps += 1
+ trajectory.append(
+ observation["achieved_goal"].astype(float).tolist()
+ )
+ success = bool(info.get("success", False))
+ if success or terminated or truncated:
+ break
+ if success or terminated or truncated:
+ break
+ distance = float(
+ np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ )
+ )
+ finally:
+ env.close()
+ execution = {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": spec.controller_sha256,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+ return execution
+
+
+def execute_ant_v5(candidate: str, raw_spec: Mapping[str, Any]):
+ """Execute and validate one program in the pinned AntMaze runtime."""
+
+ execution = execute_ant_v5_raw(candidate, raw_spec)
+ return validate_maze_execution(candidate, raw_spec, execution), execution
diff --git a/code/oat_drgrpo/ant_maze_worker_v8.py b/code/oat_drgrpo/ant_maze_worker_v8.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7e707168626baf68a047835a9a3b5b8f37d089b
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v8.py
@@ -0,0 +1,196 @@
+"""Hash-bound AntMaze executor for the admitted v8 waypoint controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+
+
+ROOT = Path(
+ os.environ.get("OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[2])
+).resolve()
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v8.zip"
+RECEIPT_PATH = (
+ ROOT / "var/maze_runtime/controllers/ant_waypoint_v8.evaluation.json"
+)
+RECEIPT_SHA256 = (
+ "fc2961d74a1fdfc8f3d2c1936ea61041cff65333ada0e6ebcd571b44919b18f1"
+)
+MODEL_SHA256 = (
+ "77e780dfff1147bc2f542c1761f6efeaa2305fd5ddb625244ebf8e82b3fa871d"
+)
+WAYPOINT_DISTANCE = 4.0
+WAYPOINT_SUCCESS_THRESHOLD = 0.45
+_MODEL = None
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def controller_identity() -> dict[str, Any]:
+ """Load and verify the immutable v8 controller receipt and model."""
+
+ if _sha256(RECEIPT_PATH) != RECEIPT_SHA256:
+ raise RuntimeError("Ant v8 controller receipt hash mismatch")
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ if (
+ receipt.get("status") != "pass"
+ or receipt.get("decision") != "admitted_to_fresh_maze_route_gate_v8"
+ ):
+ raise RuntimeError("Ant v8 controller is not admitted")
+ if _sha256(MODEL_PATH) != MODEL_SHA256:
+ raise RuntimeError("Ant v8 controller model hash mismatch")
+ if receipt.get("hashes", {}).get("model_sha256") != MODEL_SHA256:
+ raise RuntimeError("Ant v8 receipt does not bind the controller model")
+ if receipt.get("training_waypoint_distances") != [WAYPOINT_DISTANCE]:
+ raise RuntimeError("Ant v8 receipt has a different waypoint distance")
+ if receipt.get("success_threshold") != WAYPOINT_SUCCESS_THRESHOLD:
+ raise RuntimeError("Ant v8 receipt has a different waypoint threshold")
+ return receipt
+
+
+def _model():
+ global _MODEL
+ controller_identity()
+ if _MODEL is None:
+ from stable_baselines3 import PPO
+
+ _MODEL = PPO.load(MODEL_PATH, device="cpu")
+ return _MODEL
+
+
+def _heading(token: str):
+ import numpy as np
+
+ diagonal = 1.0 / math.sqrt(2.0)
+ values = {
+ "N": (0.0, 1.0),
+ "NE": (diagonal, diagonal),
+ "E": (1.0, 0.0),
+ "SE": (diagonal, -diagonal),
+ "S": (0.0, -1.0),
+ "SW": (-diagonal, -diagonal),
+ "W": (-1.0, 0.0),
+ "NW": (-diagonal, diagonal),
+ }
+ return np.asarray(values[token], dtype=np.float32)
+
+
+def execute_ant_v8_raw(
+ candidate: str,
+ raw_spec: Mapping[str, Any],
+) -> dict[str, Any]:
+ """Execute commands as successive four-unit relative waypoint targets."""
+
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != ANT_MAZE_VERIFIER:
+ raise ValueError("AntMaze executor received a non-AntMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["ant_environment_sha256"]:
+ raise ValueError("AntMaze environment hash differs from installed runtime")
+ if spec.controller_sha256 != RECEIPT_SHA256:
+ raise ValueError("AntMaze controller hash differs from frozen v8 receipt")
+ tokens = parse_maze_action_program(candidate, spec)
+ model = _model()
+
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ try:
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={
+ "reset_cell": list(spec.reset_cell),
+ "goal_cell": list(spec.goal_cell),
+ },
+ )
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ terminated = truncated = False
+ for token in tokens:
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ target = current + WAYPOINT_DISTANCE * _heading(token)
+ waypoint_reached = False
+ for _ in range(spec.action_repeat):
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ relative = np.clip(
+ (target - current) / WAYPOINT_DISTANCE,
+ -1.0,
+ 1.0,
+ )
+ controller_input = np.concatenate(
+ [
+ np.asarray(observation["observation"], dtype=np.float32),
+ relative,
+ ]
+ )
+ action, _ = model.predict(controller_input, deterministic=True)
+ observation, _reward, terminated, truncated, info = env.step(action)
+ simulator_steps += 1
+ trajectory.append(
+ observation["achieved_goal"].astype(float).tolist()
+ )
+ success = bool(info.get("success", False))
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ waypoint_reached = (
+ float(np.linalg.norm(target - current))
+ <= WAYPOINT_SUCCESS_THRESHOLD
+ )
+ if success or waypoint_reached or terminated or truncated:
+ break
+ if success or terminated or truncated:
+ break
+ distance = float(
+ np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ )
+ )
+ finally:
+ env.close()
+ return {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": spec.controller_sha256,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+
+
+def execute_ant_v8(candidate: str, raw_spec: Mapping[str, Any]):
+ """Execute and validate one v8 program in the pinned AntMaze runtime."""
+
+ execution = execute_ant_v8_raw(candidate, raw_spec)
+ return validate_maze_execution(candidate, raw_spec, execution), execution
diff --git a/code/oat_drgrpo/ant_maze_worker_v9.py b/code/oat_drgrpo/ant_maze_worker_v9.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad2207840d75de00b39eda0e6435832f21cc447c
--- /dev/null
+++ b/code/oat_drgrpo/ant_maze_worker_v9.py
@@ -0,0 +1,240 @@
+"""Hash-bound AntMaze executor for the admitted v9 waypoint controller."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+
+
+ROOT = Path(
+ os.environ.get("OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[2])
+).resolve()
+MODEL_PATH = ROOT / "var/maze_runtime/controllers/ant_waypoint_v9.zip"
+RECEIPT_PATH = (
+ ROOT / "var/maze_runtime/controllers/ant_waypoint_v9.evaluation.json"
+)
+TRAINING_IDENTITY_PATH = (
+ ROOT / "var/artifacts/ant_waypoint_controller_v9_identity.json"
+)
+WAYPOINT_DISTANCE = 4.0
+WAYPOINT_SUCCESS_THRESHOLD = 0.45
+_MODEL = None
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def controller_receipt_sha256() -> str:
+ """Return the exact v9 receipt identity after validating admission."""
+
+ controller_identity()
+ return _sha256(RECEIPT_PATH)
+
+
+def controller_identity() -> dict[str, Any]:
+ """Load and verify the terminal v9 receipt and model as one identity."""
+
+ receipt_sha256 = _sha256(RECEIPT_PATH)
+ model_sha256 = _sha256(MODEL_PATH)
+ training_identity_sha256 = _sha256(TRAINING_IDENTITY_PATH)
+ receipt = json.loads(RECEIPT_PATH.read_text(encoding="utf-8"))
+ training_identity = json.loads(
+ TRAINING_IDENTITY_PATH.read_text(encoding="utf-8")
+ )
+ if (
+ receipt.get("status") != "pass"
+ or receipt.get("decision") != "admitted_to_fresh_maze_route_gate_v9"
+ ):
+ raise RuntimeError("Ant v9 controller is not admitted")
+ if receipt.get("seed") != 73009 or receipt.get("timesteps") != 5_000_000:
+ raise RuntimeError("Ant v9 receipt differs from its frozen training identity")
+ if (
+ training_identity.get("job_id") != 30192731
+ or training_identity.get("seed") != 73009
+ ):
+ raise RuntimeError("Ant v9 training identity is not the frozen job")
+ checks = receipt.get("checks", {})
+ required_checks = {
+ "episode_count",
+ "success_rate_at_least_0p90",
+ "minimum_heading_success_rate_at_least_0p75",
+ "minimum_map_success_rate_at_least_0p75",
+ "unhealthy_termination_rate_at_most_0p10",
+ "median_success_steps_at_most_300",
+ "all_metrics_finite",
+ }
+ if not isinstance(checks, dict) or not required_checks.issubset(checks) or not all(
+ checks[key] is True for key in required_checks
+ ):
+ raise RuntimeError("Ant v9 receipt contains a failed controller check")
+ if receipt.get("hashes", {}).get("model_sha256") != model_sha256:
+ raise RuntimeError("Ant v9 receipt does not bind the controller model")
+ if receipt.get("training_waypoint_distances") != [WAYPOINT_DISTANCE]:
+ raise RuntimeError("Ant v9 receipt has a different waypoint distance")
+ if receipt.get("waypoint_distance") != WAYPOINT_DISTANCE:
+ raise RuntimeError("Ant v9 receipt has a different executor distance")
+ if receipt.get("success_threshold") != WAYPOINT_SUCCESS_THRESHOLD:
+ raise RuntimeError("Ant v9 receipt has a different waypoint threshold")
+ route_identity_path = os.environ.get("OAT_ZERO_PROTOCOL_IDENTITY")
+ if route_identity_path:
+ route_identity = json.loads(
+ Path(route_identity_path).read_text(encoding="utf-8")
+ )
+ expected = {
+ "controller_receipt_sha256": receipt_sha256,
+ "controller_model_sha256": model_sha256,
+ "controller_training_identity_sha256": training_identity_sha256,
+ }
+ if route_identity.get("schema_version") != (
+ "ant-maze-v9-route-generation-identity-v1"
+ ):
+ raise RuntimeError("Ant v9 route identity schema mismatch")
+ if any(route_identity.get(key) != value for key, value in expected.items()):
+ raise RuntimeError("Ant v9 route identity does not bind the controller")
+ return receipt
+
+
+def _model():
+ global _MODEL
+ controller_identity()
+ if _MODEL is None:
+ from stable_baselines3 import PPO
+
+ _MODEL = PPO.load(MODEL_PATH, device="cpu")
+ return _MODEL
+
+
+def _heading(token: str):
+ import numpy as np
+
+ diagonal = 1.0 / math.sqrt(2.0)
+ values = {
+ "N": (0.0, 1.0),
+ "NE": (diagonal, diagonal),
+ "E": (1.0, 0.0),
+ "SE": (diagonal, -diagonal),
+ "S": (0.0, -1.0),
+ "SW": (-diagonal, -diagonal),
+ "W": (-1.0, 0.0),
+ "NW": (-diagonal, diagonal),
+ }
+ return np.asarray(values[token], dtype=np.float32)
+
+
+def execute_ant_v9_raw(
+ candidate: str,
+ raw_spec: Mapping[str, Any],
+) -> dict[str, Any]:
+ """Execute commands as successive four-unit relative waypoint targets."""
+
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != ANT_MAZE_VERIFIER:
+ raise ValueError("AntMaze executor received a non-AntMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["ant_environment_sha256"]:
+ raise ValueError("AntMaze environment hash differs from installed runtime")
+ if spec.controller_sha256 != controller_receipt_sha256():
+ raise ValueError("AntMaze controller hash differs from frozen v9 receipt")
+ tokens = parse_maze_action_program(candidate, spec)
+ model = _model()
+
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ try:
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={
+ "reset_cell": list(spec.reset_cell),
+ "goal_cell": list(spec.goal_cell),
+ },
+ )
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ terminated = truncated = False
+ for token in tokens:
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ target = current + WAYPOINT_DISTANCE * _heading(token)
+ for _ in range(spec.action_repeat):
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ relative = np.clip(
+ (target - current) / WAYPOINT_DISTANCE,
+ -1.0,
+ 1.0,
+ )
+ controller_input = np.concatenate(
+ [
+ np.asarray(observation["observation"], dtype=np.float32),
+ relative,
+ ]
+ )
+ action, _ = model.predict(controller_input, deterministic=True)
+ observation, _reward, terminated, truncated, info = env.step(action)
+ simulator_steps += 1
+ trajectory.append(
+ observation["achieved_goal"].astype(float).tolist()
+ )
+ success = bool(info.get("success", False))
+ current = np.asarray(env.unwrapped.data.qpos[:2], dtype=np.float32)
+ waypoint_reached = (
+ float(np.linalg.norm(target - current))
+ <= WAYPOINT_SUCCESS_THRESHOLD
+ )
+ if success or waypoint_reached or terminated or truncated:
+ break
+ if success or terminated or truncated:
+ break
+ distance = float(
+ np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ )
+ )
+ finally:
+ env.close()
+ return {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": spec.controller_sha256,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+
+
+def execute_ant_v9(candidate: str, raw_spec: Mapping[str, Any]):
+ """Execute and validate one v9 program in the pinned AntMaze runtime."""
+
+ execution = execute_ant_v9_raw(candidate, raw_spec)
+ return validate_maze_execution(candidate, raw_spec, execution), execution
diff --git a/code/oat_drgrpo/canonical_actions.py b/code/oat_drgrpo/canonical_actions.py
new file mode 100644
index 0000000000000000000000000000000000000000..527d0360b3de59fe9b5e18c244e167726118578d
--- /dev/null
+++ b/code/oat_drgrpo/canonical_actions.py
@@ -0,0 +1,915 @@
+"""Fixed-support canonical action policies for exact-answer experiments."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+import itertools
+import json
+import math
+from typing import Any
+
+import torch
+
+from .pantry_support_action import (
+ PANTRY_SUPPORT_MASK_TASK,
+ PANTRY_SUPPORT_MASK_WIDTH,
+ decode_pantry_support_mask,
+ pantry_support_mask_from_allocation,
+)
+
+
+GRAPH_COLOR_ACTIONS = ("1", "2", "3")
+COUNTDOWN_ACTIONS_BY_POSITION = (
+ ("1", "2", "3", "4", "5", "6"),
+ ("1", "2", "3"),
+ ("1", "2", "3", "4", "5", "6"),
+)
+
+
+@dataclass(frozen=True)
+class CanonicalActionSpace:
+ """A finite three-position action grammar resolved into tokenizer IDs."""
+
+ task: str
+ action_strings_by_position: tuple[tuple[str, ...], ...]
+ token_ids_by_position: tuple[tuple[int, ...], ...]
+
+ @property
+ def horizon(self) -> int:
+ return len(self.token_ids_by_position)
+
+ @property
+ def union_token_ids(self) -> tuple[int, ...]:
+ return tuple(
+ dict.fromkeys(
+ token_id
+ for support in self.token_ids_by_position
+ for token_id in support
+ )
+ )
+
+ @property
+ def sequence_count(self) -> int:
+ return math.prod(len(support) for support in self.token_ids_by_position)
+
+ @property
+ def max_sequence_entropy(self) -> float:
+ return math.log(self.sequence_count)
+
+
+def canonical_action_strings_by_position(
+ task: str,
+) -> tuple[tuple[str, ...], ...]:
+ """Return the audited action grammar for a canonical benchmark task."""
+
+ if task == "graph_coloring":
+ return (GRAPH_COLOR_ACTIONS,) * 3
+ if task == "countdown":
+ return COUNTDOWN_ACTIONS_BY_POSITION
+ if task == PANTRY_SUPPORT_MASK_TASK:
+ return (("0", "1"),) * PANTRY_SUPPORT_MASK_WIDTH
+ raise ValueError(f"unsupported canonical action task: {task!r}")
+
+
+def resolve_canonical_action_space(tokenizer: Any, task: str) -> CanonicalActionSpace:
+ """Resolve every grammar position while preserving its support order."""
+
+ action_strings = canonical_action_strings_by_position(task)
+ union_actions = tuple(
+ dict.fromkeys(action for support in action_strings for action in support)
+ )
+ union_ids = resolve_single_token_actions(tokenizer, union_actions)
+ action_to_id = dict(zip(union_actions, union_ids))
+ return CanonicalActionSpace(
+ task=task,
+ action_strings_by_position=action_strings,
+ token_ids_by_position=tuple(
+ tuple(action_to_id[action] for action in support)
+ for support in action_strings
+ ),
+ )
+
+
+def enumerate_countdown_action_codes() -> tuple[str, ...]:
+ """Enumerate the 6 x 3 x 6 Countdown codes in lexical digit order."""
+
+ return tuple(
+ "".join(digits)
+ for digits in itertools.product(
+ COUNTDOWN_ACTIONS_BY_POSITION[0],
+ COUNTDOWN_ACTIONS_BY_POSITION[1],
+ COUNTDOWN_ACTIONS_BY_POSITION[2],
+ )
+ )
+
+
+def decode_countdown_action_code(code: str, reference: str | dict[str, Any]) -> str:
+ """Decode one audited three-digit code into a full Countdown expression.
+
+ The singleton digit selects one operand in dataset order. The inner digit
+ combines the other two operands (also in dataset order), and the root
+ digit combines that pair with the singleton. No answer or target is used.
+ """
+
+ text = str(code).strip()
+ if (
+ len(text) != 3
+ or text[0] not in COUNTDOWN_ACTIONS_BY_POSITION[0]
+ or text[1] not in COUNTDOWN_ACTIONS_BY_POSITION[1]
+ or text[2] not in COUNTDOWN_ACTIONS_BY_POSITION[2]
+ ):
+ raise ValueError(f"invalid canonical Countdown action code: {code!r}")
+ try:
+ spec = json.loads(reference) if isinstance(reference, str) else reference
+ if not isinstance(spec, dict) or spec.get("verifier") != "countdown":
+ raise ValueError
+ numbers = tuple(int(value) for value in spec["numbers"])
+ except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc:
+ raise ValueError("canonical Countdown reference is malformed") from exc
+ if len(numbers) != 3 or len(set(numbers)) != 3:
+ raise ValueError(
+ "canonical Countdown requires exactly three distinct operands"
+ )
+
+ root_action, singleton_action, inner_action = (int(value) for value in text)
+ singleton_index = singleton_action - 1
+ singleton = numbers[singleton_index]
+ a, b = (
+ value for index, value in enumerate(numbers) if index != singleton_index
+ )
+ pair = {
+ 1: f"({a} + {b})",
+ 2: f"({a} * {b})",
+ 3: f"({a} - {b})",
+ 4: f"({b} - {a})",
+ 5: f"({a} / {b})",
+ 6: f"({b} / {a})",
+ }[inner_action]
+ return {
+ 1: f"({pair} + {singleton})",
+ 2: f"({pair} * {singleton})",
+ 3: f"({pair} - {singleton})",
+ 4: f"({singleton} - {pair})",
+ 5: f"({pair} / {singleton})",
+ 6: f"({singleton} / {pair})",
+ }[root_action]
+
+
+def decode_canonical_action_response(
+ task: str, code: str, reference: str | dict[str, Any]
+) -> str:
+ """Map policy action tokens to the benchmark response seen by the grader."""
+
+ text = str(code).strip()
+ if task == "graph_coloring":
+ if len(text) != 3 or any(action not in GRAPH_COLOR_ACTIONS for action in text):
+ raise ValueError(f"invalid canonical graph action code: {code!r}")
+ return text
+ if task == "countdown":
+ return decode_countdown_action_code(text, reference)
+ if task == PANTRY_SUPPORT_MASK_TASK:
+ try:
+ spec = json.loads(reference) if isinstance(reference, str) else reference
+ except json.JSONDecodeError:
+ spec = {}
+ if not isinstance(spec, dict):
+ spec = {}
+ return decode_pantry_support_mask(text, spec)
+ raise ValueError(f"unsupported canonical action task: {task!r}")
+
+
+def canonical_action_code_from_verified_response(
+ task: str,
+ response: str,
+ reference: str | dict[str, Any],
+) -> str:
+ """Represent a verified replay witness on its registered policy surface."""
+
+ text = str(response).strip()
+ if task == "graph_coloring":
+ decode_canonical_action_response(task, text, reference)
+ return text
+ if task == PANTRY_SUPPORT_MASK_TASK:
+ try:
+ spec = json.loads(reference) if isinstance(reference, str) else reference
+ except json.JSONDecodeError as exc:
+ raise ValueError("canonical Pantry reference is malformed") from exc
+ if not isinstance(spec, dict):
+ raise ValueError("canonical Pantry reference is malformed")
+ return pantry_support_mask_from_allocation(text, spec)
+ raise ValueError(
+ f"verified replay projection is unsupported for canonical task {task!r}"
+ )
+
+
+def canonical_action_code_token_ids(
+ action_space: CanonicalActionSpace,
+ code: str,
+) -> tuple[int, ...]:
+ """Encode one canonical code as exactly one registered token per action."""
+
+ text = str(code).strip()
+ if len(text) != action_space.horizon:
+ raise ValueError(
+ f"canonical action code has length {len(text)}, expected "
+ f"{action_space.horizon}"
+ )
+ token_ids: list[int] = []
+ for position, action in enumerate(text):
+ actions = action_space.action_strings_by_position[position]
+ try:
+ action_index = actions.index(action)
+ except ValueError as exc:
+ raise ValueError(
+ f"canonical action {action!r} is outside position {position} support"
+ ) from exc
+ token_ids.append(action_space.token_ids_by_position[position][action_index])
+ return tuple(token_ids)
+
+
+def _sample_canonical_actions_from_logits(
+ next_token_logits: torch.Tensor,
+ *,
+ allowed_token_ids: Sequence[int],
+ uniforms: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Sample one categorical action per row from a fixed token support.
+
+ Explicit uniforms make the sampling stream reproducible independently of
+ CUDA generator implementation. The caller remains responsible for the
+ autoregressive loop: this helper samples exactly one next action.
+ """
+
+ if next_token_logits.ndim != 2:
+ raise ValueError("next_token_logits must have shape [batch, vocab]")
+ if next_token_logits.shape[0] <= 0:
+ raise ValueError("next_token_logits batch must be nonempty")
+ if uniforms.shape != (next_token_logits.shape[0],):
+ raise ValueError("uniforms must have shape [batch]")
+ ids = tuple(int(token_id) for token_id in allowed_token_ids)
+ if not ids or len(ids) != len(set(ids)):
+ raise ValueError("allowed_token_ids must be nonempty and unique")
+ if min(ids) < 0 or max(ids) >= int(next_token_logits.shape[-1]):
+ raise ValueError("allowed_token_ids must lie inside the model vocabulary")
+ if not bool(torch.isfinite(uniforms).all()) or not bool(
+ ((uniforms >= 0) & (uniforms < 1)).all()
+ ):
+ raise ValueError("uniforms must be finite values in [0, 1)")
+
+ allowed = torch.tensor(ids, dtype=torch.long, device=next_token_logits.device)
+ action_logits = next_token_logits.index_select(-1, allowed).float()
+ if not bool(torch.isfinite(action_logits).all()):
+ raise FloatingPointError("canonical next-action logits are nonfinite")
+ full_log_probs = torch.log_softmax(action_logits, dim=-1)
+ probabilities = torch.exp(full_log_probs)
+ cumulative = torch.cumsum(probabilities, dim=-1)
+ uniform_rows = uniforms.to(
+ device=next_token_logits.device, dtype=probabilities.dtype
+ )
+ sampled_indices = (uniform_rows.unsqueeze(-1) >= cumulative).sum(dim=-1)
+ sampled_indices = sampled_indices.clamp_max(len(ids) - 1)
+ token_ids = allowed[sampled_indices]
+ selected_log_probs = torch.gather(
+ full_log_probs, -1, sampled_indices.unsqueeze(-1)
+ ).squeeze(-1)
+ return (
+ token_ids.detach(),
+ selected_log_probs.detach(),
+ full_log_probs.detach(),
+ )
+
+
+def resolve_single_token_actions(
+ tokenizer: Any,
+ actions: Sequence[str],
+) -> tuple[int, ...]:
+ """Resolve a one-token, one-to-one serialization for canonical actions."""
+
+ if not actions:
+ raise ValueError("canonical action support cannot be empty")
+ token_ids: list[int] = []
+ for action in actions:
+ encoded = list(tokenizer.encode(action, add_special_tokens=False))
+ if len(encoded) != 1:
+ raise ValueError(
+ f"canonical action {action!r} must encode to exactly one token; "
+ f"got {encoded}"
+ )
+ token_id = int(encoded[0])
+ decoded = str(
+ tokenizer.decode(
+ [token_id],
+ skip_special_tokens=False,
+ clean_up_tokenization_spaces=False,
+ )
+ )
+ if decoded != action:
+ raise ValueError(
+ f"canonical action {action!r} is not one-to-one: token "
+ f"{token_id} decodes as {decoded!r}"
+ )
+ token_ids.append(token_id)
+ if len(set(token_ids)) != len(token_ids):
+ raise ValueError("canonical actions must resolve to distinct token ids")
+ return tuple(token_ids)
+
+
+def resolve_graph_color_action_token_ids(tokenizer: Any) -> tuple[int, ...]:
+ """Return the exact token ids for graph-color actions 1, 2, and 3."""
+
+ return resolve_single_token_actions(tokenizer, GRAPH_COLOR_ACTIONS)
+
+
+def restricted_action_log_probs_entropy_and_distribution(
+ logits: torch.Tensor,
+ labels: torch.Tensor,
+ response_masks: torch.Tensor,
+ *,
+ allowed_token_ids: Sequence[int],
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Score a fixed-support autoregressive action policy.
+
+ ``logits`` and ``labels`` have the ordinary causal-LM sequence shapes.
+ Only active response labels must belong to ``allowed_token_ids``. Returned
+ tensors have the shifted ``[batch, sequence - 1]`` shape and are zero away
+ from response actions.
+ """
+
+ if logits.ndim != 3:
+ raise ValueError("logits must have shape [batch, sequence, vocab]")
+ if labels.shape != logits.shape[:-1]:
+ raise ValueError("labels must match logits batch/sequence dimensions")
+ if response_masks.shape != (logits.shape[0], logits.shape[1] - 1):
+ raise ValueError("response_masks must have shape [batch, sequence - 1]")
+ ids = tuple(int(token_id) for token_id in allowed_token_ids)
+ if not ids or len(set(ids)) != len(ids):
+ raise ValueError("allowed_token_ids must be nonempty and unique")
+ if min(ids) < 0 or max(ids) >= int(logits.shape[-1]):
+ raise ValueError("allowed_token_ids must lie inside the model vocabulary")
+
+ shifted_labels = labels[:, 1:]
+ active = response_masks.to(torch.bool)
+ allowed = torch.tensor(ids, dtype=torch.long, device=logits.device)
+ matches = shifted_labels.unsqueeze(-1).eq(allowed)
+ match_count = matches.sum(dim=-1)
+ invalid_active = active & match_count.ne(1)
+ if bool(invalid_active.any()):
+ invalid_ids = shifted_labels[invalid_active].detach().cpu().unique().tolist()
+ raise ValueError(
+ "canonical response contains token ids outside its action support: "
+ f"{invalid_ids}"
+ )
+
+ action_logits = logits[:, :-1, :].index_select(-1, allowed).float()
+ if not bool(torch.isfinite(action_logits[active]).all()):
+ raise FloatingPointError(
+ "canonical policy has nonfinite allowed-support logits on active actions"
+ )
+ action_log_probs = torch.log_softmax(action_logits, dim=-1)
+ selected_indices = matches.to(torch.int64).argmax(dim=-1)
+ selected = torch.gather(
+ action_log_probs, dim=-1, index=selected_indices.unsqueeze(-1)
+ ).squeeze(-1)
+ probabilities = torch.softmax(action_logits, dim=-1)
+ entropy = -(probabilities * action_log_probs).sum(dim=-1)
+ if not bool(torch.isfinite(selected[active]).all()):
+ raise FloatingPointError(
+ "canonical policy has nonfinite selected log probabilities"
+ )
+ if not bool(torch.isfinite(entropy[active]).all()):
+ raise FloatingPointError("canonical policy has nonfinite action entropy")
+ zeros = torch.zeros_like(selected)
+ masked_distribution = torch.where(
+ active.unsqueeze(-1), action_log_probs, torch.zeros_like(action_log_probs)
+ )
+ return (
+ torch.where(active, selected, zeros),
+ torch.where(active, entropy, zeros),
+ masked_distribution,
+ )
+
+
+def restricted_action_log_probs_and_entropy(
+ logits: torch.Tensor,
+ labels: torch.Tensor,
+ response_masks: torch.Tensor,
+ *,
+ allowed_token_ids: Sequence[int],
+) -> tuple[torch.Tensor, torch.Tensor]:
+ """Backward-compatible two-output canonical-policy scorer."""
+
+ selected, entropy, _ = restricted_action_log_probs_entropy_and_distribution(
+ logits,
+ labels,
+ response_masks,
+ allowed_token_ids=allowed_token_ids,
+ )
+ return selected, entropy
+
+
+def restricted_position_action_log_probs_entropy_and_distribution(
+ logits: torch.Tensor,
+ labels: torch.Tensor,
+ response_masks: torch.Tensor,
+ *,
+ allowed_token_ids_by_position: Sequence[Sequence[int]],
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Score a fixed-horizon policy with position-specific token supports.
+
+ The full distribution uses the ordered union vocabulary. Disallowed union
+ entries are ``-inf`` on active rows and accompanied by an explicit support
+ mask, so callers never treat a masked action as probability mass.
+ """
+
+ if logits.ndim != 3:
+ raise ValueError("logits must have shape [batch, sequence, vocab]")
+ if labels.shape != logits.shape[:-1]:
+ raise ValueError("labels must match logits batch/sequence dimensions")
+ if response_masks.shape != (logits.shape[0], logits.shape[1] - 1):
+ raise ValueError("response_masks must have shape [batch, sequence - 1]")
+ supports = tuple(
+ tuple(int(token_id) for token_id in support)
+ for support in allowed_token_ids_by_position
+ )
+ if not supports or any(
+ not support or len(support) != len(set(support)) for support in supports
+ ):
+ raise ValueError("every positional action support must be nonempty and unique")
+ union_ids = tuple(
+ dict.fromkeys(token_id for support in supports for token_id in support)
+ )
+ if min(union_ids) < 0 or max(union_ids) >= int(logits.shape[-1]):
+ raise ValueError("allowed_token_ids must lie inside the model vocabulary")
+
+ active = response_masks.to(torch.bool)
+ row_counts = active.sum(dim=-1)
+ if not bool(row_counts.eq(len(supports)).all()):
+ raise ValueError(
+ "canonical rows must contain exactly one action per positional support"
+ )
+ ordinals = active.to(torch.int64).cumsum(dim=-1) - 1
+ safe_ordinals = ordinals.clamp(min=0, max=len(supports) - 1)
+ union_allowed = torch.tensor(
+ [
+ [token_id in set(support) for token_id in union_ids]
+ for support in supports
+ ],
+ dtype=torch.bool,
+ device=logits.device,
+ )
+ support_mask = union_allowed[safe_ordinals] & active.unsqueeze(-1)
+ union = torch.tensor(union_ids, dtype=torch.long, device=logits.device)
+ action_logits = logits[:, :-1, :].index_select(-1, union).float()
+ if not bool(torch.isfinite(action_logits[support_mask]).all()):
+ raise FloatingPointError(
+ "canonical policy has nonfinite positional-support logits"
+ )
+ masked_logits = action_logits.masked_fill(~support_mask, -torch.inf)
+ # Inactive prompt/padding positions have no support by construction. Give
+ # those rows a harmless finite distribution before log-softmax; they are
+ # zeroed in every returned tensor below. This avoids all--inf softmax NaNs
+ # (and their gradients) outside the response mask.
+ masked_logits = torch.where(
+ active.unsqueeze(-1), masked_logits, torch.zeros_like(masked_logits)
+ )
+ action_log_probs = torch.log_softmax(masked_logits, dim=-1)
+
+ shifted_labels = labels[:, 1:]
+ matches = shifted_labels.unsqueeze(-1).eq(union)
+ valid_selected = (matches & support_mask).sum(dim=-1).eq(1)
+ if bool((active & ~valid_selected).any()):
+ invalid_ids = shifted_labels[active & ~valid_selected].unique().tolist()
+ raise ValueError(
+ "canonical response contains token ids outside its positional support: "
+ f"{invalid_ids}"
+ )
+ selected_indices = matches.to(torch.int64).argmax(dim=-1)
+ selected = torch.gather(
+ action_log_probs, dim=-1, index=selected_indices.unsqueeze(-1)
+ ).squeeze(-1)
+ probabilities = torch.exp(action_log_probs)
+ finite_log_probs = action_log_probs.masked_fill(~support_mask, 0.0)
+ entropy_terms = probabilities * finite_log_probs
+ entropy = -entropy_terms.sum(dim=-1)
+ if not bool(torch.isfinite(selected[active]).all()) or not bool(
+ torch.isfinite(entropy[active]).all()
+ ):
+ raise FloatingPointError("canonical positional policy scoring is nonfinite")
+ zeros = torch.zeros_like(selected)
+ return (
+ torch.where(active, selected, zeros),
+ torch.where(active, entropy, zeros),
+ torch.where(
+ active.unsqueeze(-1),
+ action_log_probs,
+ torch.zeros_like(action_log_probs),
+ ),
+ support_mask,
+ )
+
+
+def materialize_position_canonical_behavior_policy(
+ shifted_labels: torch.Tensor,
+ response_masks: torch.Tensor,
+ *,
+ action_ids: Sequence[Sequence[int]],
+ selected_log_probs: Sequence[Sequence[float]],
+ full_log_probs: Sequence[Sequence[Sequence[float]]],
+ behavior_action_token_ids_by_position: Sequence[Sequence[Sequence[int]]],
+ allowed_token_ids_by_position: Sequence[Sequence[int]],
+ normalizer_atol: float = 1e-6,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float, float]:
+ """Align a ragged positional behavior trace into an ordered union tensor."""
+
+ if shifted_labels.shape != response_masks.shape:
+ raise ValueError("shifted_labels and response_masks must have equal shape")
+ if normalizer_atol < 0:
+ raise ValueError("normalizer_atol must be nonnegative")
+ supports = tuple(
+ tuple(int(token_id) for token_id in support)
+ for support in allowed_token_ids_by_position
+ )
+ union_ids = tuple(
+ dict.fromkeys(token_id for support in supports for token_id in support)
+ )
+ union_index = {token_id: index for index, token_id in enumerate(union_ids)}
+ batch_size, shifted_length = shifted_labels.shape
+ fields = {
+ "action_ids": action_ids,
+ "selected_log_probs": selected_log_probs,
+ "full_log_probs": full_log_probs,
+ "behavior_action_token_ids_by_position": (
+ behavior_action_token_ids_by_position
+ ),
+ }
+ for name, rows in fields.items():
+ if len(rows) != batch_size:
+ raise RuntimeError(
+ f"canonical behavior {name} has {len(rows)} rows; "
+ f"expected {batch_size}"
+ )
+
+ dtype = torch.float32
+ device = shifted_labels.device
+ selected = torch.zeros((batch_size, shifted_length), dtype=dtype, device=device)
+ full = torch.full(
+ (batch_size, shifted_length, len(union_ids)),
+ -torch.inf,
+ dtype=dtype,
+ device=device,
+ )
+ support_mask = torch.zeros_like(full, dtype=torch.bool)
+ active = response_masks.to(torch.bool)
+ normalization_error_max = 0.0
+ selected_echo_diff_max = 0.0
+
+ for row_index in range(batch_size):
+ positions = torch.where(active[row_index])[0]
+ row_actions = [int(value) for value in action_ids[row_index]]
+ row_selected = [float(value) for value in selected_log_probs[row_index]]
+ row_full = list(full_log_probs[row_index])
+ row_supports = tuple(
+ tuple(int(value) for value in support)
+ for support in behavior_action_token_ids_by_position[row_index]
+ )
+ if row_supports != supports:
+ raise RuntimeError(
+ "canonical behavior positional support mismatch at row "
+ f"{row_index}: expected={supports} observed={row_supports}"
+ )
+ if not (
+ len(positions)
+ == len(row_actions)
+ == len(row_selected)
+ == len(row_full)
+ == len(supports)
+ ):
+ raise RuntimeError(
+ f"canonical behavior trace length mismatch at row {row_index}"
+ )
+ active_labels = shifted_labels[row_index, positions].detach().cpu().tolist()
+ if row_actions != [int(value) for value in active_labels]:
+ raise RuntimeError(
+ "canonical behavior action IDs do not match shifted learner labels "
+ f"at row {row_index}"
+ )
+
+ for action_position, (position, action_id, support) in enumerate(
+ zip(positions.tolist(), row_actions, supports)
+ ):
+ if action_id not in support:
+ raise RuntimeError(
+ f"canonical behavior action {action_id} is outside support {support}"
+ )
+ values = torch.as_tensor(
+ row_full[action_position], dtype=dtype, device=device
+ )
+ selected_value = torch.as_tensor(
+ row_selected[action_position], dtype=dtype, device=device
+ )
+ if values.shape != (len(support),) or not bool(
+ torch.isfinite(values).all()
+ ) or not bool(torch.isfinite(selected_value)):
+ raise RuntimeError("canonical behavior trace is malformed or nonfinite")
+ log_normalizer = torch.logsumexp(values, dim=-1)
+ normalization_error = float(torch.abs(log_normalizer).cpu().item())
+ if normalization_error > normalizer_atol:
+ raise RuntimeError(
+ "canonical behavior probabilities are not normalized at "
+ f"row={row_index} position={action_position}: "
+ f"abs(logsumexp)={normalization_error:.6g}"
+ )
+ support_action_index = support.index(action_id)
+ echo_diff = float(
+ torch.abs(selected_value - values[support_action_index]).cpu().item()
+ )
+ if echo_diff > 1e-7:
+ raise RuntimeError(
+ "canonical selected behavior log probability does not match "
+ f"its positional row at row={row_index} "
+ f"position={action_position}: difference={echo_diff:.6g}"
+ )
+ normalized = values - log_normalizer
+ selected[row_index, position] = normalized[support_action_index]
+ union_indices = torch.tensor(
+ [union_index[token_id] for token_id in support],
+ dtype=torch.long,
+ device=device,
+ )
+ full[row_index, position, union_indices] = normalized
+ support_mask[row_index, position, union_indices] = True
+ normalization_error_max = max(
+ normalization_error_max, normalization_error
+ )
+ selected_echo_diff_max = max(selected_echo_diff_max, echo_diff)
+
+ return (
+ selected,
+ full,
+ support_mask,
+ normalization_error_max,
+ selected_echo_diff_max,
+ )
+
+
+def materialize_canonical_behavior_policy(
+ shifted_labels: torch.Tensor,
+ response_masks: torch.Tensor,
+ *,
+ action_ids: Sequence[Sequence[int]],
+ selected_log_probs: Sequence[Sequence[float]],
+ full_log_probs: Sequence[Sequence[Sequence[float]]],
+ behavior_action_token_ids: Sequence[Sequence[int]],
+ allowed_token_ids: Sequence[int],
+ normalizer_atol: float = 1e-6,
+) -> tuple[torch.Tensor, torch.Tensor, float, float]:
+ """Align the exact actor behavior trace with shifted learner positions.
+
+ Returns selected behavior log probabilities, ordered full-support behavior
+ log probabilities, maximum normalization error, and maximum discrepancy
+ between the separately transported selected value and its full-row entry.
+ """
+
+ if shifted_labels.shape != response_masks.shape:
+ raise ValueError("shifted_labels and response_masks must have equal shape")
+ if normalizer_atol < 0:
+ raise ValueError("normalizer_atol must be nonnegative")
+ batch_size, shifted_length = shifted_labels.shape
+ ids = tuple(int(token_id) for token_id in allowed_token_ids)
+ if not ids or len(ids) != len(set(ids)):
+ raise ValueError("allowed_token_ids must be nonempty and unique")
+ fields = {
+ "action_ids": action_ids,
+ "selected_log_probs": selected_log_probs,
+ "full_log_probs": full_log_probs,
+ "behavior_action_token_ids": behavior_action_token_ids,
+ }
+ for name, rows in fields.items():
+ if len(rows) != batch_size:
+ raise RuntimeError(
+ f"canonical behavior {name} has {len(rows)} rows; "
+ f"expected {batch_size}"
+ )
+
+ dtype = torch.float32
+ device = shifted_labels.device
+ selected = torch.zeros((batch_size, shifted_length), dtype=dtype, device=device)
+ full = torch.zeros(
+ (batch_size, shifted_length, len(ids)), dtype=dtype, device=device
+ )
+ id_to_index = {token_id: index for index, token_id in enumerate(ids)}
+ normalization_error_max = 0.0
+ selected_echo_diff_max = 0.0
+ active = response_masks.to(torch.bool)
+
+ for row_index in range(batch_size):
+ positions = torch.where(active[row_index])[0]
+ expected_count = int(positions.numel())
+ row_actions = [int(value) for value in action_ids[row_index]]
+ row_selected = [float(value) for value in selected_log_probs[row_index]]
+ row_full = list(full_log_probs[row_index])
+ row_support = tuple(int(value) for value in behavior_action_token_ids[row_index])
+ if row_support != ids:
+ raise RuntimeError(
+ "canonical behavior support order mismatch at row "
+ f"{row_index}: expected={ids} observed={row_support}"
+ )
+ if not (
+ len(row_actions)
+ == len(row_selected)
+ == len(row_full)
+ == expected_count
+ ):
+ raise RuntimeError(
+ "canonical behavior trace length mismatch at row "
+ f"{row_index}: actions={len(row_actions)} "
+ f"selected={len(row_selected)} full={len(row_full)} "
+ f"active={expected_count}"
+ )
+ active_labels = shifted_labels[row_index, positions].detach().cpu().tolist()
+ if row_actions != [int(value) for value in active_labels]:
+ raise RuntimeError(
+ "canonical behavior action IDs do not match shifted learner labels "
+ f"at row {row_index}"
+ )
+
+ for trace_index, (position, action_id) in enumerate(
+ zip(positions.tolist(), row_actions)
+ ):
+ if action_id not in id_to_index:
+ raise RuntimeError(
+ f"canonical behavior action {action_id} is outside support {ids}"
+ )
+ values = torch.as_tensor(
+ row_full[trace_index], dtype=dtype, device=device
+ )
+ if values.shape != (len(ids),):
+ raise RuntimeError(
+ "canonical behavior full row has wrong shape at "
+ f"row={row_index} position={trace_index}: {tuple(values.shape)}"
+ )
+ selected_value = torch.as_tensor(
+ row_selected[trace_index], dtype=dtype, device=device
+ )
+ if not bool(torch.isfinite(values).all()) or not bool(
+ torch.isfinite(selected_value)
+ ):
+ raise RuntimeError("canonical behavior trace contains nonfinite values")
+ log_normalizer = torch.logsumexp(values, dim=-1)
+ normalization_error = float(
+ torch.abs(log_normalizer).detach().cpu().item()
+ )
+ if normalization_error > normalizer_atol:
+ raise RuntimeError(
+ "canonical behavior probabilities are not normalized at "
+ f"row={row_index} position={trace_index}: "
+ f"abs(logsumexp)={normalization_error:.6g}"
+ )
+ selected_from_full_raw = values[id_to_index[action_id]]
+ echo_diff = float(
+ torch.abs(selected_value - selected_from_full_raw)
+ .detach()
+ .cpu()
+ .item()
+ )
+ if echo_diff > 1e-7:
+ raise RuntimeError(
+ "canonical selected behavior log probability does not match "
+ f"its full row at row={row_index} position={trace_index}: "
+ f"difference={echo_diff:.6g}"
+ )
+ normalized_values = values - log_normalizer
+ selected[row_index, position] = normalized_values[id_to_index[action_id]]
+ full[row_index, position] = normalized_values
+ normalization_error_max = max(
+ normalization_error_max, normalization_error
+ )
+ selected_echo_diff_max = max(selected_echo_diff_max, echo_diff)
+
+ return selected, full, normalization_error_max, selected_echo_diff_max
+
+
+def canonical_behavior_overlap_diagnostics(
+ behavior_selected_log_probs: torch.Tensor,
+ behavior_full_log_probs: torch.Tensor,
+ learner_selected_log_probs: torch.Tensor,
+ learner_full_log_probs: torch.Tensor,
+ response_masks: torch.Tensor,
+ support_mask: torch.Tensor | None = None,
+) -> dict[str, torch.Tensor]:
+ """Compute full-distribution overlap and horizon-three IS diagnostics."""
+
+ if behavior_selected_log_probs.shape != response_masks.shape:
+ raise ValueError("behavior selected log-probabilities have the wrong shape")
+ if learner_selected_log_probs.shape != response_masks.shape:
+ raise ValueError("learner selected log-probabilities have the wrong shape")
+ expected_full_shape = (*response_masks.shape, behavior_full_log_probs.shape[-1])
+ if behavior_full_log_probs.shape != expected_full_shape:
+ raise ValueError("behavior full log-probabilities have the wrong shape")
+ if learner_full_log_probs.shape != expected_full_shape:
+ raise ValueError("learner full log-probabilities have the wrong shape")
+ if support_mask is not None and support_mask.shape != expected_full_shape:
+ raise ValueError("canonical support mask has the wrong shape")
+
+ active = response_masks.to(torch.bool)
+ if not bool(active.any()):
+ raise ValueError("canonical behavior diagnostics require active actions")
+ behavior_rows = behavior_full_log_probs[active].double()
+ learner_rows = learner_full_log_probs[active].double()
+ active_support = (
+ torch.ones_like(behavior_rows, dtype=torch.bool)
+ if support_mask is None
+ else support_mask[active].to(torch.bool)
+ )
+ if not bool(active_support.any(dim=-1).all()):
+ raise ValueError("canonical behavior diagnostics found an empty support row")
+ if not bool(torch.isfinite(behavior_rows[active_support]).all()) or not bool(
+ torch.isfinite(learner_rows[active_support]).all()
+ ):
+ raise FloatingPointError("canonical behavior diagnostics are nonfinite")
+ behavior_rows = behavior_rows - torch.logsumexp(
+ behavior_rows.masked_fill(~active_support, -torch.inf),
+ dim=-1,
+ keepdim=True,
+ )
+ learner_rows = learner_rows - torch.logsumexp(
+ learner_rows.masked_fill(~active_support, -torch.inf),
+ dim=-1,
+ keepdim=True,
+ )
+
+ log_ratio_rows = torch.where(
+ active_support,
+ learner_rows - behavior_rows,
+ torch.zeros_like(learner_rows),
+ )
+ density_ratios = torch.exp(log_ratio_rows[active_support])
+ behavior_probabilities = torch.where(
+ active_support, torch.exp(behavior_rows), torch.zeros_like(behavior_rows)
+ )
+ learner_probabilities = torch.where(
+ active_support, torch.exp(learner_rows), torch.zeros_like(learner_rows)
+ )
+ tv = 0.5 * torch.abs(
+ learner_probabilities - behavior_probabilities
+ ).sum(dim=-1)
+ kl_actor_learner = (
+ torch.where(
+ active_support,
+ behavior_probabilities * (behavior_rows - learner_rows),
+ torch.zeros_like(behavior_probabilities),
+ )
+ ).sum(dim=-1)
+ kl_learner_actor = (
+ torch.where(
+ active_support,
+ learner_probabilities * (learner_rows - behavior_rows),
+ torch.zeros_like(learner_probabilities),
+ )
+ ).sum(dim=-1)
+
+ selected_log_ratio = (
+ learner_selected_log_probs.double() - behavior_selected_log_probs.double()
+ )
+ row_counts = active.sum(dim=-1)
+ if not bool(row_counts.eq(row_counts[0]).all()):
+ raise RuntimeError("canonical behavior rows have unequal horizons")
+ horizon = int(row_counts[0].item())
+ compact_log_ratio = torch.stack(
+ [selected_log_ratio[row][active[row]] for row in range(active.shape[0])]
+ )
+ inclusive_prefix_log_ratio = torch.cumsum(compact_log_ratio, dim=-1)
+ exclusive_prefix_log_ratio = torch.cat(
+ (
+ torch.zeros_like(inclusive_prefix_log_ratio[:, :1]),
+ inclusive_prefix_log_ratio[:, :-1],
+ ),
+ dim=-1,
+ )
+
+ def _ess_fraction(weights: torch.Tensor) -> torch.Tensor:
+ count = torch.tensor(
+ float(weights.numel()), dtype=weights.dtype, device=weights.device
+ )
+ return weights.sum().square() / weights.square().sum().clamp_min(1e-12) / count
+
+ prefix_ess = []
+ for depth in range(horizon):
+ prefix_ess.append(
+ _ess_fraction(torch.exp(exclusive_prefix_log_ratio[:, depth]))
+ )
+ sequence_weights = torch.exp(inclusive_prefix_log_ratio[:, horizon - 1])
+
+ return {
+ "canonical_behavior_ratio_min": density_ratios.min(),
+ "canonical_behavior_ratio_max": density_ratios.max(),
+ "canonical_behavior_tv_mean": tv.mean(),
+ "canonical_behavior_tv_max": tv.max(),
+ "canonical_behavior_kl_actor_learner_mean": kl_actor_learner.mean(),
+ "canonical_behavior_kl_actor_learner_max": kl_actor_learner.max(),
+ "canonical_behavior_kl_learner_actor_mean": kl_learner_actor.mean(),
+ "canonical_behavior_kl_learner_actor_max": kl_learner_actor.max(),
+ "canonical_behavior_sequence_ess_fraction": _ess_fraction(sequence_weights),
+ "canonical_behavior_prefix_ess_fraction_min": torch.stack(prefix_ess).min(),
+ }
diff --git a/code/oat_drgrpo/math_grader.py b/code/oat_drgrpo/math_grader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8c9de23e450ab68e7ecb5c487fea2e3eaf40fca
--- /dev/null
+++ b/code/oat_drgrpo/math_grader.py
@@ -0,0 +1,2141 @@
+# Copyright 2025 Garena Online Private Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Provides a math answer grading function with high recall.
+Based on HF math_verify, verl, open reasoner zero, etc.
+"""
+
+import ast
+from dataclasses import dataclass
+import functools
+import json
+from multiprocessing import TimeoutError as MultiprocessingTimeoutError
+import queue
+import re
+import signal
+import threading
+from collections import Counter
+from fractions import Fraction
+from itertools import islice, zip_longest
+from math import isclose
+from typing import Any, Optional
+
+import sympy
+from latex2sympy2_extended import latex2sympy
+from math_verify import ExprExtractionConfig, LatexExtractionConfig, parse, verify
+from math_verify import grader as math_verify_grader
+from math_verify import parser as math_verify_parser
+from math_verify.errors import TimeoutException as MathVerifyTimeout
+from math_verify.utils import timeout as math_verify_signal_timeout
+from pylatexenc import latex2text
+from sympy import N, simplify
+from sympy.parsing import sympy_parser
+from sympy.parsing.latex import parse_latex
+from sympy.parsing.sympy_parser import parse_expr
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ POINT_MAZE_VERIFIER,
+)
+from .maze_modebench_process import validate_maze_action_program_external
+from .mathir import (
+ MATHIR_MENU_VERIFIER,
+ MATHIR_VERIFIER,
+ validate_mathir_action_menu,
+ validate_mathir_algebra,
+)
+from .math_route import validate_math_route_response
+from .pantry_plan import (
+ PANTRY_PLAN_VERIFIER,
+ validate_pantry_plan,
+)
+from .python_modebench import (
+ PYTHON_FACTOR_VERIFIER,
+ python_factor_route_signature,
+)
+from .python_modebench_process import validate_python_factor_function_external
+
+
+def _thread_compatible_math_verify_timeout(timeout_seconds: int = 10):
+ """Keep math_verify bounded when grading runs in an actor worker thread.
+
+ math_verify's POSIX timeout installs SIGALRM. Python only permits signal
+ handler installation on the interpreter's main thread, while OAT grades
+ actor responses in a ThreadPool to avoid forking a CUDA/vLLM process. On
+ the main thread retain math_verify's native alarm. Else run the protected
+ operation in a daemon helper thread and bound the caller's wait.
+ """
+
+ seconds = int(timeout_seconds)
+ if seconds <= 0:
+ raise ValueError("timeout_seconds must be positive")
+
+ def decorator(func):
+ signal_wrapped = math_verify_signal_timeout(timeout_seconds=seconds)(func)
+
+ @functools.wraps(func)
+ def wrapper(*args, **kwargs):
+ if threading.current_thread() is threading.main_thread():
+ return signal_wrapped(*args, **kwargs)
+ result_queue: queue.Queue[tuple[bool, Any]] = queue.Queue(maxsize=1)
+
+ def run() -> None:
+ try:
+ result_queue.put((True, func(*args, **kwargs)))
+ except BaseException as error:
+ result_queue.put((False, error))
+
+ worker = threading.Thread(
+ target=run,
+ name="math-verify-timeout",
+ daemon=True,
+ )
+ worker.start()
+ worker.join(seconds)
+ if worker.is_alive():
+ raise MathVerifyTimeout("Operation timed out!")
+ succeeded, value = result_queue.get_nowait()
+ if succeeded:
+ return value
+ raise value
+
+ return wrapper
+
+ return decorator
+
+
+# parse() and verify() resolve `timeout` from their defining module globals at
+# call time. Install the actor-thread-safe policy without altering the external
+# math_verify package or its grading semantics.
+math_verify_parser.timeout = _thread_compatible_math_verify_timeout
+math_verify_grader.timeout = _thread_compatible_math_verify_timeout
+
+
+_math_verify_sympy_solve_and_compare = math_verify_grader.sympy_solve_and_compare
+
+
+def _robust_math_verify_sympy_solve_and_compare(
+ gold,
+ pred,
+ float_rounding: int,
+ numeric_precision: int,
+):
+ """Repair math_verify's scalar ``solve(Eq)`` compatibility edge case.
+
+ Some SymPy versions return a list of scalar roots for ``solve(Eq, symbols)``
+ while math_verify assumes a list of dictionaries and calls ``.items()``.
+ Retry only that failing equality path with ``dict=True`` and preserve the
+ package's exact symbol-key and expression-comparison semantics.
+ """
+
+ try:
+ return _math_verify_sympy_solve_and_compare(
+ gold,
+ pred,
+ float_rounding,
+ numeric_precision,
+ )
+ except AttributeError as error:
+ if (
+ "items" not in str(error)
+ or not isinstance(gold, sympy.Eq)
+ or not isinstance(pred, sympy.Eq)
+ ):
+ raise
+
+ solved_gold = sympy.solve(
+ gold,
+ gold.free_symbols,
+ dict=True,
+ )
+ solved_pred = sympy.solve(
+ pred,
+ pred.free_symbols,
+ dict=True,
+ )
+ if not isinstance(solved_gold, list) or not isinstance(solved_pred, list):
+ return False
+ if len(solved_gold) != len(solved_pred):
+ return False
+
+ unmatched = list(solved_pred)
+ for gold_solution in solved_gold:
+ if not isinstance(gold_solution, dict):
+ return False
+ match_index = None
+ for index, pred_solution in enumerate(unmatched):
+ if (
+ isinstance(pred_solution, dict)
+ and set(gold_solution) == set(pred_solution)
+ and all(
+ math_verify_grader.sympy_expr_eq(
+ gold_solution[key],
+ pred_solution[key],
+ float_rounding,
+ numeric_precision,
+ )
+ for key in gold_solution
+ )
+ ):
+ match_index = index
+ break
+ if match_index is None:
+ return False
+ unmatched.pop(match_index)
+ return not unmatched
+
+
+math_verify_grader.sympy_solve_and_compare = _robust_math_verify_sympy_solve_and_compare
+
+
+def collect_threaded_math_rewards(
+ pool,
+ reward_fn,
+ responses,
+ references,
+ *,
+ timeout_seconds: float,
+):
+ """Grade a response batch concurrently with one bounded wait per result."""
+
+ pending = [
+ pool.apply_async(reward_fn, (response, reference))
+ for response, reference in zip(responses, references)
+ ]
+ rewards = []
+ infos = []
+ for result in pending:
+ try:
+ info, reward = result.get(timeout=timeout_seconds)
+ rewards.append(reward)
+ infos.append(info)
+ except MultiprocessingTimeoutError:
+ rewards.append(0.0)
+ infos.append({"formatted": False})
+ return rewards, infos
+
+
+# Dan Hendrycks' code
+def mathd_normalize_answer(answer: Optional[str]) -> Optional[str]:
+ if answer is None:
+ return None
+ answer = answer.strip()
+ try:
+ # Remove enclosing `\text{}`.
+ m = re.search(r"^\\text\{(?P.+?)\}$", answer)
+ if m is not None:
+ answer = m.group("text").strip()
+ return _strip_string(answer)
+ except Exception:
+ return answer
+
+
+# units mainly from MathQA
+unit_texts = [
+ "east",
+ "degree",
+ "mph",
+ "kmph",
+ "ft",
+ "m sqaure",
+ " m east",
+ "sq m",
+ "deg",
+ "mile",
+ "q .",
+ "monkey",
+ "prime",
+ "ratio",
+ "profit of rs",
+ "rd",
+ "o",
+ "gm",
+ "p . m",
+ "lb",
+ "tile",
+ "per",
+ "dm",
+ "lt",
+ "gain",
+ "ab",
+ "way",
+ "west",
+ "a .",
+ "b .",
+ "c .",
+ "d .",
+ "e .",
+ "f .",
+ "g .",
+ "h .",
+ "t",
+ "a",
+ "h",
+ "no change",
+ "men",
+ "soldier",
+ "pie",
+ "bc",
+ "excess",
+ "st",
+ "inches",
+ "noon",
+ "percent",
+ "by",
+ "gal",
+ "kmh",
+ "c",
+ "acre",
+ "rise",
+ "a . m",
+ "th",
+ "π r 2",
+ "sq",
+ "mark",
+ "l",
+ "toy",
+ "coin",
+ "sq . m",
+ "gallon",
+ "° f",
+ "profit",
+ "minw",
+ "yr",
+ "women",
+ "feet",
+ "am",
+ "pm",
+ "hr",
+ "cu cm",
+ "square",
+ "v â € ™",
+ "are",
+ "rupee",
+ "rounds",
+ "cubic",
+ "cc",
+ "mtr",
+ "s",
+ "ohm",
+ "number",
+ "kmph",
+ "day",
+ "hour",
+ "minute",
+ "min",
+ "second",
+ "man",
+ "woman",
+ "sec",
+ "cube",
+ "mt",
+ "sq inch",
+ "mp",
+ "∏ cm ³",
+ "hectare",
+ "more",
+ "sec",
+ "unit",
+ "cu . m",
+ "cm 2",
+ "rs .",
+ "rs",
+ "kg",
+ "g",
+ "month",
+ "km",
+ "m",
+ "cm",
+ "mm",
+ "apple",
+ "liter",
+ "loss",
+ "yard",
+ "pure",
+ "year",
+ "increase",
+ "decrease",
+ "d",
+ "less",
+ "Surface",
+ "litre",
+ "pi sq m",
+ "s .",
+ "metre",
+ "meter",
+ "inch",
+]
+
+unit_texts.extend([t + "s" for t in unit_texts])
+
+
+def _strip_string(string):
+ def _fix_fracs(string):
+ substrs = string.split("\\frac")
+ new_str = substrs[0]
+ if len(substrs) > 1:
+ substrs = substrs[1:]
+ for substr in substrs:
+ new_str += "\\frac"
+ if substr[0] == "{":
+ new_str += substr
+ else:
+ try:
+ assert len(substr) >= 2
+ except Exception:
+ return string
+ a = substr[0]
+ b = substr[1]
+ if b != "{":
+ if len(substr) > 2:
+ post_substr = substr[2:]
+ new_str += "{" + a + "}{" + b + "}" + post_substr
+ else:
+ new_str += "{" + a + "}{" + b + "}"
+ else:
+ if len(substr) > 2:
+ post_substr = substr[2:]
+ new_str += "{" + a + "}" + b + post_substr
+ else:
+ new_str += "{" + a + "}" + b
+ string = new_str
+ return string
+
+ def _fix_a_slash_b(string):
+ if len(string.split("/")) != 2:
+ return string
+ a = string.split("/")[0]
+ b = string.split("/")[1]
+ try:
+ a = int(a)
+ b = int(b)
+ assert string == "{}/{}".format(a, b)
+ new_string = "\\frac{" + str(a) + "}{" + str(b) + "}"
+ return new_string
+ except Exception:
+ return string
+
+ def _remove_right_units(string):
+ # "\\text{ " only ever occurs (at least in the val set) when describing units
+ if "\\text{ " in string:
+ splits = string.split("\\text{ ")
+ assert len(splits) == 2
+ return splits[0]
+ else:
+ return string
+
+ def _fix_sqrt(string):
+ if "\\sqrt" not in string:
+ return string
+ splits = string.split("\\sqrt")
+ new_string = splits[0]
+ for split in splits[1:]:
+ if split[0] != "{":
+ a = split[0]
+ new_substr = "\\sqrt{" + a + "}" + split[1:]
+ else:
+ new_substr = "\\sqrt" + split
+ new_string += new_substr
+ return new_string
+
+ # linebreaks
+ string = string.replace("\n", "")
+ # print(string)
+
+ # remove inverse spaces
+ string = string.replace("\\!", "")
+ # print(string)
+
+ # replace \\ with \
+ string = string.replace("\\\\", "\\")
+ # print(string)
+
+ # matrix
+ string = re.sub(r"\\begin\{array\}\{.*?\}", r"\\begin{pmatrix}", string)
+ string = re.sub(r"\\end\{array\}", r"\\end{pmatrix}", string)
+ string = string.replace("bmatrix", "pmatrix")
+
+ # replace tfrac and dfrac with frac
+ string = string.replace("tfrac", "frac")
+ string = string.replace("dfrac", "frac")
+ string = (
+ string.replace("\\neq", "\\ne")
+ .replace("\\leq", "\\le")
+ .replace("\\geq", "\\ge")
+ )
+ # print(string)
+
+ # remove \left and \right
+ string = string.replace("\\left", "")
+ string = string.replace("\\right", "")
+ # print(string)
+
+ # Remove unit: miles, dollars if after is not none
+ _string = re.sub(r"\\text{.*?}$", "", string).strip()
+ if _string != "" and _string != string:
+ # print("Warning: unit not removed: '{}' -> '{}'".format(string, _string))
+ string = _string
+
+ # Remove unit: texts
+ for _ in range(2):
+ for unit_text in unit_texts:
+ # use regex, the prefix should be either the start of the string or a non-alphanumeric character
+ # the suffix should be either the end of the string or a non-alphanumeric character
+ _string = re.sub(r"(^|\W)" + unit_text + r"($|\W)", r"\1\2", string)
+ if _string != "":
+ string = _string
+
+ # Remove circ (degrees)
+ string = string.replace("^{\\circ}", "")
+ string = string.replace("^\\circ", "")
+
+ # remove dollar signs
+ string = string.replace("\\$", "")
+
+ # remove units (on the right)
+ string = _remove_right_units(string)
+
+ # remove percentage
+ string = string.replace("\\%", "")
+ string = string.replace(r"\%", "")
+
+ # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string
+ string = string.replace(" .", " 0.")
+ string = string.replace("{.", "{0.")
+ # if empty, return empty string
+ if len(string) == 0:
+ return string
+ if string[0] == ".":
+ string = "0" + string
+
+ # to consider: get rid of e.g. "k = " or "q = " at beginning
+ if len(string.split("=")) == 2:
+ if len(string.split("=")[0]) <= 2:
+ string = string.split("=")[1]
+
+ # fix sqrt3 --> sqrt{3}
+ string = _fix_sqrt(string)
+
+ # remove spaces
+ string = string.replace(" ", "")
+
+ # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b}
+ string = _fix_fracs(string)
+
+ # manually change 0.5 --> \frac{1}{2}
+ if string == "0.5":
+ string = "\\frac{1}{2}"
+
+ # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y
+ string = _fix_a_slash_b(string)
+
+ return string
+
+
+SUBSTITUTIONS = [
+ ("an ", ""),
+ ("a ", ""),
+ (".$", "$"),
+ ("\\$", ""),
+ (r"\ ", ""),
+ (" ", ""),
+ ("mbox", "text"),
+ (",\\text{and}", ","),
+ ("\\text{and}", ","),
+ ("\\text{m}", "\\text{}"),
+]
+
+
+REMOVED_EXPRESSIONS = [
+ "square",
+ "ways",
+ "integers",
+ "dollars",
+ "mph",
+ "inches",
+ "ft",
+ "hours",
+ "km",
+ "units",
+ "\\ldots",
+ "sue",
+ "points",
+ "feet",
+ "minutes",
+ "digits",
+ "cents",
+ "degrees",
+ "cm",
+ "gm",
+ "pounds",
+ "meters",
+ "meals",
+ "edges",
+ "students",
+ "childrentickets",
+ "multiples",
+ "\\text{s}",
+ "\\text{.}",
+ "\\text{\ns}",
+ "\\text{}^2",
+ "\\text{}^3",
+ "\\text{\n}",
+ "\\text{}",
+ r"\mathrm{th}",
+ r"^\circ",
+ r"^{\circ}",
+ r"\;",
+ r",\!",
+ "{,}",
+ '"',
+ "\\dots",
+]
+
+
+def normalize_final_answer(final_answer: str) -> str:
+ """
+ Normalize a final answer to a quantitative reasoning question.
+ This code comes from https://arxiv.org/pdf/2206.14858.pdf, page18.
+ """
+ # final_answer = final_answer.split("=")[-1]
+
+ for before, after in SUBSTITUTIONS:
+ final_answer = final_answer.replace(before, after)
+ for expr in REMOVED_EXPRESSIONS:
+ final_answer = final_answer.replace(expr, "")
+
+ # Extract answer that is in LaTeX math, is bold,
+ # is surrounded by a box, etc.
+ final_answer = re.sub(r"(.*?)(\$)(.*?)(\$)(.*)", "$\\3$", final_answer)
+ final_answer = re.sub(r"(\\text\{)(.*?)(\})", "\\2", final_answer)
+ final_answer = re.sub(r"(\\textbf\{)(.*?)(\})", "\\2", final_answer)
+ final_answer = re.sub(r"(\\overline\{)(.*?)(\})", "\\2", final_answer)
+ final_answer = re.sub(r"(\\boxed\{)(.*)(\})", "\\2", final_answer)
+
+ # Normalize shorthand TeX:
+ # \fracab -> \frac{a}{b}
+ # \frac{abc}{bef} -> \frac{abc}{bef}
+ # \fracabc -> \frac{a}{b}c
+ # \sqrta -> \sqrt{a}
+ # \sqrtab -> sqrt{a}b
+ final_answer = re.sub(r"(frac)([^{])(.)", "frac{\\2}{\\3}", final_answer)
+ final_answer = re.sub(r"(sqrt)([^{])", "sqrt{\\2}", final_answer)
+ final_answer = final_answer.replace("$", "")
+
+ # Normalize 100,000 -> 100000
+ if final_answer.replace(",", "").isdigit():
+ final_answer = final_answer.replace(",", "")
+
+ return final_answer
+
+
+def repeatness(s: str):
+ def ranks(values):
+ index = {v: i for i, v in enumerate(sorted(set(values)))}
+ return [index[v] for v in values]
+
+ def suffixArray(s):
+ line = ranks(s)
+ n, k, ans, sa = len(s), 1, line, [0] * len(s)
+ while k < n - 1:
+ line = ranks(list(zip_longest(line, islice(line, k, None), fillvalue=-1)))
+ ans, k = line, k << 1
+ for i, k in enumerate(ans):
+ sa[k] = i
+ return ans, sa
+
+ def lcp(arr, suffixArr, inv_suff):
+ n, ans, k = len(arr), [0] * len(arr), 0
+
+ for i in range(n):
+ if inv_suff[i] == n - 1:
+ k = 0
+ continue
+
+ j = suffixArr[inv_suff[i] + 1]
+ while i + k < n and j + k < n and arr[i + k] == arr[j + k]:
+ k += 1
+
+ ans[inv_suff[i]] = k
+ if k > 0:
+ k -= 1
+
+ return ans
+
+ arr = [ord(i) for i in s]
+ n = len(arr)
+ if n <= 1:
+ return 0
+ c, sa = suffixArray(arr)
+ cnt = sum(lcp(arr, sa, c))
+
+ return (cnt * 2 / (n * (n + 1))) > 0.2
+
+
+class timeout:
+ def __init__(self, seconds=1, error_message="Timeout"):
+ self.seconds = seconds
+ self.error_message = error_message
+ self._armed = False
+ self._old_handler = None
+
+ def handle_timeout(self, signum, frame):
+ raise TimeoutError(self.error_message)
+
+ def __enter__(self):
+ if threading.current_thread() is not threading.main_thread():
+ return self
+ self._old_handler = signal.getsignal(signal.SIGALRM)
+ signal.signal(signal.SIGALRM, self.handle_timeout)
+ signal.alarm(self.seconds)
+ self._armed = True
+ return self
+
+ def __exit__(self, type, value, traceback):
+ if self._armed:
+ signal.alarm(0)
+ signal.signal(signal.SIGALRM, self._old_handler)
+ self._armed = False
+
+
+def latex_eval(latex):
+ sym = parse_latex(latex)
+ val = sym.evalf()
+ return sym, val
+
+
+def numeric_equal(prediction: float, reference: float):
+ # Note that relative tolerance has significant impact
+ # on the result of the synthesized GSM-Hard dataset
+ # if reference.is_integer():
+ # return isclose(reference, round(prediction), abs_tol=1e-4)
+ # else:
+ # prediction = round(prediction, len(str(reference).split(".")[-1]))
+ return isclose(reference, prediction, rel_tol=1e-4)
+
+
+def symbolic_equal(a, b):
+ def _parse(s):
+ for f in [parse_latex, parse_expr, latex2sympy]:
+ try:
+ return f(s.replace("\\\\", "\\"))
+ except Exception:
+ try:
+ return f(s)
+ except Exception:
+ pass
+ return s
+
+ a = _parse(a)
+ b = _parse(b)
+
+ # direct equal
+ try:
+ if str(a) == str(b) or a == b:
+ return True
+ except Exception:
+ pass
+
+ # simplify equal
+ try:
+ if a.equals(b) or simplify(a - b) == 0:
+ return True
+ except Exception:
+ pass
+
+ # equation equal
+ try:
+ if (abs(a.lhs - a.rhs)).equals(abs(b.lhs - b.rhs)):
+ return True
+ except Exception:
+ pass
+
+ try:
+ if numeric_equal(float(N(a)), float(N(b))):
+ return True
+ except Exception:
+ pass
+
+ # matrix
+ try:
+ # if a and b are matrix
+ if a.shape == b.shape:
+ _a = a.applyfunc(lambda x: round(x, 3))
+ _b = b.applyfunc(lambda x: round(x, 3))
+ if _a.equals(_b):
+ return True
+ except Exception:
+ pass
+
+ return False
+
+
+def _is_latex_equal(str1, str2):
+ try:
+ sym1, val1 = latex_eval(str1)
+ sym2, val2 = latex_eval(str2)
+ if sym1 == sym2 or val1 == val2:
+ return True
+ else:
+ raise ValueError
+ except Exception: # noqa
+ try:
+ norm1, norm2 = normalize_final_answer(str1), normalize_final_answer(str2)
+ sym1, val1 = latex_eval(norm1)
+ sym2, val2 = latex_eval(norm2)
+ if sym1 == sym2 or val1 == val2:
+ return True
+ except Exception: # noqa
+ return norm1 == norm2
+ return False
+
+
+def is_latex_equal(given_answer: str, ground_truth: str) -> bool:
+ try:
+ with timeout(1):
+ try:
+ if (len(given_answer) > 128 and repeatness(given_answer)) or (
+ len(ground_truth) > 128 and repeatness(ground_truth)
+ ):
+ return False
+ # First conduct normalized string matching.
+ ground_truth_normalized = _normalize(ground_truth)
+ given_normalized = _normalize(given_answer)
+ if ground_truth_normalized is None:
+ return False
+ if ground_truth_normalized == given_normalized:
+ return True
+
+ # Next call math verify.
+ given_answer.replace("\n", "")
+ ground_truth.replace("\n", "")
+ if "$" not in given_answer:
+ given_answer = f"${given_answer}$"
+ if "$" not in ground_truth:
+ ground_truth = f"${ground_truth}$"
+ return verify(
+ parse(
+ ground_truth,
+ extraction_config=(
+ LatexExtractionConfig(boxed_match_priority=0),
+ ExprExtractionConfig(),
+ ),
+ fallback_mode="no_fallback",
+ extraction_mode=["first_match"],
+ parsing_timeout=1,
+ ),
+ parse(
+ given_answer,
+ extraction_config=(
+ LatexExtractionConfig(boxed_match_priority=0),
+ ExprExtractionConfig(),
+ ),
+ fallback_mode="no_fallback",
+ extraction_mode=["first_match"],
+ parsing_timeout=1,
+ ),
+ timeout_seconds=1,
+ )
+ # or symbolic_equal(ground_truth, given_answer)
+ except Exception:
+ return False
+ except TimeoutError:
+ return False
+
+
+def is_value_equal(given_answer: str, ground_truth: str) -> bool:
+ assert ground_truth is not None
+ ground_truth_normalized_mathd = mathd_normalize_answer(ground_truth)
+ given_answer_normalized_mathd = mathd_normalize_answer(given_answer)
+
+ str_equal = ground_truth_normalized_mathd == given_answer_normalized_mathd
+ try:
+ number_equal = float(ground_truth_normalized_mathd) == float(
+ given_answer_normalized_mathd
+ )
+ return str_equal or number_equal
+ except Exception:
+ return str_equal
+
+
+# sympy might hang -- we don't care about trying to be lenient in these cases
+BAD_SUBSTRINGS = ["^{", "^("]
+BAD_REGEXES = [r"\^[0-9]+\^", r"\^[0-9][0-9]+"]
+TUPLE_CHARS = "()[]"
+
+
+def _sympy_parse(expr: str):
+ """Parses an expression with sympy."""
+ py_expr = expr.replace("^", "**")
+ return sympy_parser.parse_expr(
+ py_expr,
+ transformations=(
+ sympy_parser.standard_transformations
+ + (sympy_parser.implicit_multiplication_application,)
+ ),
+ )
+
+
+def _parse_latex(expr: str) -> str:
+ """Attempts to parse latex to an expression sympy can read."""
+ expr = expr.replace("\\tfrac", "\\frac")
+ expr = expr.replace("\\dfrac", "\\frac")
+ expr = expr.replace("\\frac", " \\frac") # Play nice with mixed numbers.
+ expr = latex2text.LatexNodes2Text().latex_to_text(expr)
+
+ # Replace the specific characters that this parser uses.
+ expr = expr.replace("√", "sqrt")
+ expr = expr.replace("π", "pi")
+ expr = expr.replace("∞", "inf")
+ expr = expr.replace("∪", "U")
+ expr = expr.replace("·", "*")
+ expr = expr.replace("×", "*")
+
+ return expr.strip()
+
+
+def _is_float(num: str) -> bool:
+ try:
+ float(num)
+ return True
+ except ValueError:
+ return False
+
+
+def _is_int(x: float) -> bool:
+ try:
+ return abs(x - int(round(x))) <= 1e-7
+ except Exception:
+ return False
+
+
+def _is_frac(expr: str) -> bool:
+ return bool(re.search(r"^-?[0-9]+.?/0*[1-9][0-9]*.?$", expr))
+
+
+def _str_is_int(x: str) -> bool:
+ try:
+ x = _strip_properly_formatted_commas(x)
+ x = float(x)
+ return abs(x - int(round(x))) <= 1e-7
+ except Exception:
+ return False
+
+
+def _str_to_int(x: str) -> bool:
+ x = x.replace(",", "")
+ x = float(x)
+ return int(x)
+
+
+def _inject_implicit_mixed_number(step: str):
+ """
+ Automatically make a mixed number evalable
+ e.g. 7 3/4 => 7+3/4
+ """
+ p1 = re.compile("([0-9]) +([0-9])")
+ step = p1.sub("\\1+\\2", step) ## implicit mults
+ return step
+
+
+def _strip_properly_formatted_commas(expr: str):
+ # We want to be careful because we don't want to strip tuple commas
+ p1 = re.compile(r"(\d)(,)(\d\d\d)($|\D)")
+ while True:
+ next_expr = p1.sub("\\1\\3\\4", expr)
+ if next_expr == expr:
+ break
+ expr = next_expr
+ return next_expr
+
+
+def _normalize(expr: str) -> str:
+ """Normalize answer expressions."""
+ if expr is None:
+ return None
+
+ # Remove enclosing `\text{}`.
+ m = re.search(r"^\\text\{(?P.+?)\}$", expr)
+ if m is not None:
+ expr = m.group("text")
+
+ expr = expr.replace("\\%", "%")
+ expr = expr.replace("\\$", "$")
+ expr = expr.replace("$", "")
+ expr = expr.replace("%", "")
+ expr = expr.replace(" or ", " , ")
+ expr = expr.replace(" and ", " , ")
+
+ expr = expr.replace("million", "*10^6")
+ expr = expr.replace("billion", "*10^9")
+ expr = expr.replace("trillion", "*10^12")
+
+ for unit in [
+ "degree",
+ "cm",
+ "centimeter",
+ "meter",
+ "mile",
+ "second",
+ "minute",
+ "hour",
+ "day",
+ "week",
+ "month",
+ "year",
+ "foot",
+ "feet",
+ "inch",
+ "yard",
+ ]:
+ expr = re.sub(rf"{unit}(es)?(s)? *(\^[0-9]+)?", "", expr)
+ expr = re.sub(r"\^ *\\\\circ", "", expr)
+
+ if len(expr) > 0 and expr[0] == "{" and expr[-1] == "}":
+ expr = expr[1:-1]
+
+ expr = re.sub(",\\\\! *", "", expr)
+ if _is_float(expr) and _is_int(float(expr)):
+ expr = str(int(round(float(expr))))
+ if "\\" in expr:
+ try:
+ expr = _parse_latex(expr)
+ except Exception:
+ pass
+
+ # edge case with mixed numbers and negative signs
+ expr = re.sub("- *", "-", expr)
+
+ expr = _inject_implicit_mixed_number(expr)
+ expr = expr.replace(" ", "")
+
+ # if we somehow still have latex braces here, just drop them
+ expr = expr.replace("{", "")
+ expr = expr.replace("}", "")
+
+ # don't be case sensitive for text answers
+ expr = expr.lower()
+
+ if _str_is_int(expr):
+ expr = str(_str_to_int(expr))
+
+ return expr
+
+
+def count_unknown_letters_in_expr(expr: str):
+ expr = expr.replace("sqrt", "")
+ expr = expr.replace("frac", "")
+ letters_in_expr = set([x for x in expr if x.isalpha()])
+ return len(letters_in_expr)
+
+
+def should_allow_eval(expr: str):
+ # we don't want to try parsing unknown text or functions of more than two variables
+ if count_unknown_letters_in_expr(expr) > 2:
+ return False
+
+ for bad_string in BAD_SUBSTRINGS:
+ if bad_string in expr:
+ return False
+
+ for bad_regex in BAD_REGEXES:
+ if re.search(bad_regex, expr) is not None:
+ return False
+
+ return True
+
+
+def are_equal_under_sympy(ground_truth_normalized: str, given_normalized: str):
+ are_equal = False
+ try:
+ expr = f"({ground_truth_normalized})-({given_normalized})"
+ if should_allow_eval(expr):
+ sympy_diff = _sympy_parse(expr)
+ simplified = sympy.simplify(sympy_diff)
+ if simplified == 0:
+ are_equal = True
+ except Exception:
+ pass
+ return are_equal
+
+
+def split_tuple(expr: str):
+ """
+ Split the elements in a tuple/interval, while handling well-formatted commas in large numbers
+ """
+ expr = _strip_properly_formatted_commas(expr)
+ if len(expr) == 0:
+ return []
+ if (
+ len(expr) > 2
+ and expr[0] in TUPLE_CHARS
+ and expr[-1] in TUPLE_CHARS
+ and all([ch not in expr[1:-1] for ch in TUPLE_CHARS])
+ ):
+ elems = [elem.strip() for elem in expr[1:-1].split(",")]
+ else:
+ elems = [expr]
+ return elems
+
+
+def last_boxed_only_string(string):
+ idx = string.rfind("\\boxed")
+ if idx < 0:
+ idx = string.rfind("\\fbox")
+ if idx < 0:
+ return None
+
+ i = idx
+ right_brace_idx = None
+ num_left_braces_open = 0
+ while i < len(string):
+ if string[i] == "{":
+ num_left_braces_open += 1
+ if string[i] == "}":
+ num_left_braces_open -= 1
+ if num_left_braces_open == 0:
+ right_brace_idx = i
+ break
+ i += 1
+
+ if right_brace_idx is None:
+ retval = None
+ else:
+ retval = string[idx : right_brace_idx + 1]
+
+ return retval
+
+
+def remove_boxed(s):
+ left = "\\boxed{"
+ try:
+ assert s[: len(left)] == left
+ assert s[-1] == "}"
+ return s[len(left) : -1]
+ except Exception:
+ return None
+
+
+def extract_boxed_answer(solution: str) -> str:
+ """Extract the answer from inside a LaTeX \\boxed{} command"""
+ solution = last_boxed_only_string(solution)
+ solution = remove_boxed(solution)
+ return solution
+
+
+def grade_answer_sympy(given_answer: str, ground_truth: str) -> bool:
+ ground_truth_normalized = _normalize(ground_truth)
+ given_normalized = _normalize(given_answer)
+
+ if ground_truth_normalized is None:
+ return False
+
+ if ground_truth_normalized == given_normalized:
+ return True
+
+ if len(given_normalized) == 0:
+ return False
+
+ ground_truth_elems = split_tuple(ground_truth_normalized)
+ given_elems = split_tuple(given_normalized)
+
+ if len(ground_truth_elems) > 1 and (
+ ground_truth_normalized[0] != given_normalized[0]
+ or ground_truth_normalized[-1] != given_normalized[-1]
+ ):
+ is_correct = False
+ elif len(ground_truth_elems) != len(given_elems):
+ is_correct = False
+ else:
+ for ground_truth_elem, given_elem in zip(ground_truth_elems, given_elems):
+ if _is_frac(ground_truth_elem) and _is_frac(given_elem):
+ # if fractions aren't reduced, then shouldn't be marked as correct
+ # so, we don't want to allow sympy.simplify in this case
+ is_correct = ground_truth_elem == given_elem
+ elif _str_is_int(ground_truth_elem) != _str_is_int(given_elem):
+ # if the ground truth answer is an integer, we require the given answer to be a strict match (no sympy.simplify)
+ is_correct = False
+ else:
+ is_correct = are_equal_under_sympy(ground_truth_elem, given_elem)
+ if not is_correct:
+ break
+
+ return is_correct
+
+
+def grade_answer_mathd(given_answer: str, ground_truth: str) -> bool:
+ ground_truth_normalized_mathd = mathd_normalize_answer(ground_truth)
+ given_answer_normalized_mathd = mathd_normalize_answer(given_answer)
+
+ # be at least as lenient as mathd
+ if ground_truth_normalized_mathd == given_answer_normalized_mathd:
+ return True
+ return False
+
+
+def extract_answer(passage: str) -> str:
+ if "\\boxed" in passage:
+ return extract_boxed_answer(passage)
+ return None
+
+
+def _parse_modebench_spec(gt_answer: Any) -> dict[str, Any] | None:
+ if isinstance(gt_answer, dict):
+ spec = gt_answer
+ elif isinstance(gt_answer, str):
+ text = gt_answer.strip()
+ if not (text.startswith("{") and text.endswith("}")):
+ return None
+ try:
+ spec = json.loads(text)
+ except Exception:
+ return None
+ else:
+ return None
+ verifier = spec.get("verifier")
+ if verifier in {
+ "graph_coloring",
+ "countdown",
+ MATHIR_VERIFIER,
+ MATHIR_MENU_VERIFIER,
+ PANTRY_PLAN_VERIFIER,
+ PYTHON_FACTOR_VERIFIER,
+ POINT_MAZE_VERIFIER,
+ ANT_MAZE_VERIFIER,
+ }:
+ return spec
+ return None
+
+
+PYTHON_FACTOR_RESPONSE_SURFACE_VERSION = (
+ "python-factor-response-v2-latex-lambda"
+)
+
+
+def _normalize_python_factor_lambda_surface(candidate: str) -> str:
+ """Normalize one formatting-only LaTeX spelling of ``lambda n:``."""
+
+ # Qwen's native boxed surface can typeset the Python keyword as the LaTeX
+ # command ``\lambda``. Accept only the exact required signature (plus the
+ # conventional ``\,`` spacing alias); the restricted AST parser and
+ # isolated executable validator remain the authority.
+ return re.sub(
+ r"^\\lambda(?:\s+|\\,\s*)n\s*:\s*",
+ "lambda n: ",
+ str(candidate).strip(),
+ count=1,
+ ).strip()
+
+
+def _extract_modebench_candidate(model_response: str, gt_answer: Any) -> str | None:
+ spec = _parse_modebench_spec(gt_answer)
+ if spec is None:
+ return None
+ if "\\boxed" in str(model_response):
+ candidate = extract_answer(str(model_response))
+ if candidate is None:
+ return None
+ if str(spec.get("verifier")) == PYTHON_FACTOR_VERIFIER:
+ candidate = _normalize_python_factor_lambda_surface(candidate)
+ return candidate or None
+ candidate = str(model_response).strip()
+ if not candidate:
+ return None
+ if str(spec.get("verifier")) in {
+ MATHIR_VERIFIER,
+ MATHIR_MENU_VERIFIER,
+ }:
+ fenced = re.fullmatch(
+ r"```(?:mathir)?\s*(.*?)\s*```",
+ candidate,
+ flags=re.IGNORECASE | re.DOTALL,
+ )
+ if fenced is not None:
+ candidate = fenced.group(1).strip()
+ if len(candidate) > 200 or "<" in candidate or ">" in candidate:
+ return None
+ # Whitespace, a terminal semicolon, and an all-program code fence are
+ # formatting aliases. The restricted parser remains the authority.
+ return candidate or None
+ if str(spec.get("verifier")) == PYTHON_FACTOR_VERIFIER:
+ fenced = re.fullmatch(
+ r"```(?:python)?\s*(.*?)\s*```",
+ candidate,
+ flags=re.IGNORECASE | re.DOTALL,
+ )
+ if fenced is not None:
+ candidate = fenced.group(1).strip()
+ if (
+ len(candidate) > 240
+ or "\n" in candidate
+ or "\r" in candidate
+ or "<" in candidate
+ or ">" in candidate
+ ):
+ return None
+ candidate = re.sub(
+ r"^\s*(?:the\s+)?(?:final\s+)?(?:answer|program|function)\s*"
+ r"(?:is|=|:)\s*",
+ "",
+ candidate,
+ flags=re.IGNORECASE,
+ ).strip()
+ candidate = _normalize_python_factor_lambda_surface(candidate)
+ return candidate or None
+ if str(spec.get("verifier")) in {
+ POINT_MAZE_VERIFIER,
+ ANT_MAZE_VERIFIER,
+ }:
+ fenced = re.fullmatch(
+ r"```(?:actions?|plan)?\s*(.*?)\s*```",
+ candidate,
+ flags=re.IGNORECASE | re.DOTALL,
+ )
+ if fenced is not None:
+ candidate = fenced.group(1).strip()
+ if len(candidate) > 4096 or "<" in candidate or ">" in candidate:
+ return None
+ candidate = re.sub(
+ r"^\s*(?:the\s+)?(?:final\s+)?(?:answer|plan|program|actions?)\s*"
+ r"(?:is|=|:)\s*",
+ "",
+ candidate,
+ flags=re.IGNORECASE,
+ ).strip()
+ return candidate or None
+ if str(spec.get("verifier")) == PANTRY_PLAN_VERIFIER:
+ if (
+ len(candidate) > 512
+ or "\n" in candidate
+ or "\r" in candidate
+ or "<" in candidate
+ or ">" in candidate
+ ):
+ return None
+ candidate = re.sub(
+ r"^\s*(?:the\s+)?(?:final\s+)?(?:answer|plan|recipe)\s*"
+ r"(?:is|=|:)\s*",
+ "",
+ candidate,
+ flags=re.IGNORECASE,
+ ).strip()
+ return candidate or None
+ if "\n" in candidate or "\r" in candidate or "<" in candidate or ">" in candidate:
+ return None
+ if len(candidate) > 160:
+ return None
+ candidate = re.sub(
+ r"^\s*(?:the\s+)?(?:final\s+)?(?:answer|expression|coloring|program)\s*"
+ r"(?:is|=|:)\s*",
+ "",
+ candidate,
+ flags=re.IGNORECASE,
+ ).strip()
+ return candidate or None
+
+
+def _parse_graph_coloring_answer(candidate: str, n: int) -> list[int] | None:
+ text = str(candidate).strip().lower()
+ text = re.sub(r"^(?:coloring|answer)\s*(?:is|=|:)\s*", "", text).strip()
+ compact = re.sub(r"[\s,;|\[\]\(\)\{\}:.-]+", "", text)
+ if len(compact) == n and all(ch in "123" for ch in compact):
+ return [int(ch) for ch in compact]
+ tokens = re.findall(r"[123]", text)
+ if len(tokens) == n:
+ return [int(token) for token in tokens]
+ return None
+
+
+def _parse_graph_digit_sequence(candidate: str, expected_len: int) -> list[int] | None:
+ text = str(candidate).strip().lower()
+ text = re.sub(
+ r"^(?:missing\s+)?(?:colors?|digits?|answer)\s*(?:are|is|=|:)\s*",
+ "",
+ text,
+ ).strip()
+ compact = re.sub(r"[\s,;|\[\]\(\)\{\}:.-]+", "", text)
+ if len(compact) == expected_len and all(ch in "123" for ch in compact):
+ return [int(ch) for ch in compact]
+ tokens = re.findall(r"[123]", text)
+ if len(tokens) == expected_len:
+ return [int(token) for token in tokens]
+ return None
+
+
+def _verify_graph_coloring_colors(
+ colors: list[int] | None,
+ spec: dict[str, Any],
+) -> bool:
+ """Validate one already-parsed coloring object against its problem."""
+
+ try:
+ n = int(spec["n"])
+ edges = spec["edges"]
+ except Exception:
+ return False
+ if colors is None or len(colors) != n:
+ return False
+ partial_colors = spec.get("partial_colors")
+ if partial_colors is not None:
+ try:
+ if len(partial_colors) != n:
+ return False
+ for index, color in enumerate(partial_colors):
+ if color is None:
+ continue
+ if colors[index] != int(color):
+ return False
+ except Exception:
+ return False
+ for edge in edges:
+ try:
+ u, v = int(edge[0]), int(edge[1])
+ except Exception:
+ return False
+ if u < 1 or v < 1 or u > n or v > n:
+ return False
+ if colors[u - 1] == colors[v - 1]:
+ return False
+ return True
+
+
+def _verify_graph_coloring_answer(candidate: str, spec: dict[str, Any]) -> bool:
+ colors = _graph_coloring_from_candidate(candidate, spec)
+ return _verify_graph_coloring_colors(colors, spec)
+
+
+def _normalize_countdown_expression(candidate: str) -> str:
+ text = str(candidate).strip()
+ text = text.replace("\\times", "*").replace("\\cdot", "*")
+ text = text.replace("\\div", "/").replace("÷", "/").replace("×", "*")
+ text = text.replace("^", "**")
+ text = re.sub(r"^\s*(?:expression|answer)\s*(?:is|=|:)\s*", "", text, flags=re.I)
+ return text.strip()
+
+
+def _countdown_eval_and_numbers(node: ast.AST) -> tuple[Fraction, list[int]]:
+ if isinstance(node, ast.Expression):
+ return _countdown_eval_and_numbers(node.body)
+ if isinstance(node, ast.Constant):
+ if isinstance(node.value, bool) or not isinstance(node.value, int):
+ raise ValueError("Countdown constants must be integers.")
+ return Fraction(int(node.value), 1), [int(node.value)]
+ if isinstance(node, ast.UnaryOp) and isinstance(node.op, (ast.UAdd, ast.USub)):
+ value, numbers = _countdown_eval_and_numbers(node.operand)
+ if isinstance(node.op, ast.USub):
+ value = -value
+ return value, numbers
+ if isinstance(node, ast.BinOp):
+ left, left_numbers = _countdown_eval_and_numbers(node.left)
+ right, right_numbers = _countdown_eval_and_numbers(node.right)
+ if isinstance(node.op, ast.Add):
+ value = left + right
+ elif isinstance(node.op, ast.Sub):
+ value = left - right
+ elif isinstance(node.op, ast.Mult):
+ value = left * right
+ elif isinstance(node.op, ast.Div):
+ if right == 0:
+ raise ValueError("Countdown division by zero.")
+ value = left / right
+ else:
+ raise ValueError("Unsupported Countdown operator.")
+ return value, left_numbers + right_numbers
+ raise ValueError("Unsupported Countdown expression.")
+
+
+def _verify_countdown_expression(candidate: str, spec: dict[str, Any]) -> bool:
+ try:
+ target = Fraction(int(spec["target"]), 1)
+ expected_numbers = Counter(int(value) for value in spec["numbers"])
+ except Exception:
+ return False
+ text = _normalize_countdown_expression(candidate)
+ if not text:
+ return False
+ parts = [part.strip() for part in text.split("=") if part.strip()]
+ if not parts:
+ parts = [text]
+ for part in parts:
+ if not re.fullmatch(r"[0-9+\-*/().\s*]+", part):
+ continue
+ try:
+ parsed = ast.parse(part, mode="eval")
+ value, used_numbers = _countdown_eval_and_numbers(parsed)
+ except Exception:
+ continue
+ if value == target and Counter(used_numbers) == expected_numbers:
+ return True
+ return False
+
+
+def _graph_coloring_from_candidate(
+ candidate: str,
+ spec: dict[str, Any],
+) -> list[int] | None:
+ try:
+ n = int(spec["n"])
+ except Exception:
+ return None
+ colors = _parse_graph_coloring_answer(candidate, n)
+ partial_colors = spec.get("partial_colors")
+ if colors is None and partial_colors is not None:
+ try:
+ hidden_positions = [
+ index for index, color in enumerate(partial_colors) if color is None
+ ]
+ fill = _parse_graph_digit_sequence(candidate, len(hidden_positions))
+ if fill is not None:
+ colors = [
+ int(color) if color is not None else 0 for color in partial_colors
+ ]
+ for index, color in zip(hidden_positions, fill):
+ colors[index] = color
+ except Exception:
+ return None
+ if colors is None or len(colors) != n:
+ return None
+ return colors
+
+
+def _canonical_countdown_ast(node: ast.AST) -> str:
+ if isinstance(node, ast.Expression):
+ return _canonical_countdown_ast(node.body)
+ if isinstance(node, ast.Constant):
+ if isinstance(node.value, bool) or not isinstance(node.value, int):
+ raise ValueError("Countdown constants must be integers.")
+ return str(int(node.value))
+ if isinstance(node, ast.UnaryOp) and isinstance(node.op, (ast.UAdd, ast.USub)):
+ inner = _canonical_countdown_ast(node.operand)
+ if isinstance(node.op, ast.USub):
+ return f"neg({inner})"
+ return inner
+ if isinstance(node, ast.BinOp):
+ left = _canonical_countdown_ast(node.left)
+ right = _canonical_countdown_ast(node.right)
+ if isinstance(node.op, ast.Add):
+ parts = sorted([left, right])
+ return f"add({parts[0]},{parts[1]})"
+ if isinstance(node.op, ast.Mult):
+ parts = sorted([left, right])
+ return f"mul({parts[0]},{parts[1]})"
+ if isinstance(node.op, ast.Sub):
+ return f"sub({left},{right})"
+ if isinstance(node.op, ast.Div):
+ return f"div({left},{right})"
+ raise ValueError("Unsupported Countdown expression.")
+
+
+def _canonical_countdown_route_ast(node: ast.AST) -> str:
+ """Canonical operator/dependency skeleton with numeric leaves abstracted."""
+
+ if isinstance(node, ast.Expression):
+ return _canonical_countdown_route_ast(node.body)
+ if isinstance(node, ast.Constant):
+ if isinstance(node.value, bool) or not isinstance(node.value, int):
+ raise ValueError("Countdown constants must be integers.")
+ return "input"
+ if isinstance(node, ast.UnaryOp) and isinstance(node.op, (ast.UAdd, ast.USub)):
+ inner = _canonical_countdown_route_ast(node.operand)
+ return f"neg({inner})" if isinstance(node.op, ast.USub) else inner
+ if isinstance(node, ast.BinOp):
+ left = _canonical_countdown_route_ast(node.left)
+ right = _canonical_countdown_route_ast(node.right)
+ if isinstance(node.op, ast.Add):
+ parts = sorted([left, right])
+ return f"add({parts[0]},{parts[1]})"
+ if isinstance(node.op, ast.Mult):
+ parts = sorted([left, right])
+ return f"mul({parts[0]},{parts[1]})"
+ if isinstance(node.op, ast.Sub):
+ return f"sub({left},{right})"
+ if isinstance(node.op, ast.Div):
+ return f"div({left},{right})"
+ raise ValueError("Unsupported Countdown route expression.")
+
+
+def _canonical_countdown_expression_key(
+ candidate: str,
+ spec: dict[str, Any],
+) -> str | None:
+ try:
+ expected_numbers = Counter(int(value) for value in spec["numbers"])
+ except Exception:
+ return None
+ text = _normalize_countdown_expression(candidate)
+ if not text:
+ return None
+ parts = [part.strip() for part in text.split("=") if part.strip()] or [text]
+ for part in parts:
+ if not re.fullmatch(r"[0-9+\-*/().\s*]+", part):
+ continue
+ try:
+ parsed = ast.parse(part, mode="eval")
+ _, used_numbers = _countdown_eval_and_numbers(parsed)
+ if Counter(used_numbers) != expected_numbers:
+ continue
+ return f"countdown:{_canonical_countdown_ast(parsed)}"
+ except Exception:
+ continue
+ return None
+
+
+def _modebench_answer_key(
+ model_response: str,
+ gt_answer: Any,
+) -> str | None:
+ spec = _parse_modebench_spec(gt_answer)
+ if spec is None:
+ return None
+ candidate = _extract_modebench_candidate(model_response, gt_answer)
+ if candidate is None:
+ return None
+ verifier = str(spec.get("verifier"))
+ if verifier == "graph_coloring":
+ colors = _graph_coloring_from_candidate(candidate, spec)
+ if colors is None:
+ return None
+ return "graph_coloring:" + "".join(str(int(color)) for color in colors)
+ if verifier == "countdown":
+ return _canonical_countdown_expression_key(candidate, spec)
+ if verifier == MATHIR_VERIFIER:
+ validation = validate_mathir_algebra(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier == MATHIR_MENU_VERIFIER:
+ validation = validate_mathir_action_menu(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier == PYTHON_FACTOR_VERIFIER:
+ validation = validate_python_factor_function_external(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier == PANTRY_PLAN_VERIFIER:
+ validation = validate_pantry_plan(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier in {POINT_MAZE_VERIFIER, ANT_MAZE_VERIFIER}:
+ validation = validate_maze_action_program_external(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ return None
+
+
+def validated_modebench_outcome_key(
+ model_response: str,
+ gt_answer: Any,
+) -> str | None:
+ """Return a canonical outcome only when that same response verifies.
+
+ This is the admission boundary for online canonical banks. It deliberately
+ supports only ModeBench tasks with executable validators:
+
+ - graph colorings are parsed and checked against every graph edge;
+ - Countdown expressions are parsed, checked for exact operand use, and
+ executed against the requested target.
+
+ - MathIR algebra programs are parsed by a restricted grammar, executed as
+ state transformations, exact-normalized, and accepted only when execution
+ isolates the right solution. Their key comes from those executed states.
+
+ - Python factor functions are syntax-restricted and called in an isolated
+ Python worker. Their key is the vector returned by those exact tool calls.
+
+ Ordinary MATH final-answer grading is not a proof/strategy verifier and is
+ therefore rejected here rather than being mislabeled as canonical search.
+ """
+
+ spec = _parse_modebench_spec(gt_answer)
+ if spec is None:
+ return None
+ candidate = _extract_modebench_candidate(model_response, gt_answer)
+ if candidate is None:
+ return None
+ verifier = str(spec.get("verifier"))
+ if verifier == "graph_coloring":
+ colors = _graph_coloring_from_candidate(candidate, spec)
+ if not _verify_graph_coloring_colors(colors, spec):
+ return None
+ assert colors is not None
+ return "graph_coloring:" + "".join(str(int(color)) for color in colors)
+ if verifier == MATHIR_VERIFIER:
+ validation = validate_mathir_algebra(candidate, spec)
+ # The key is constructed from the exact normalized equation states
+ # produced by the same interpreter execution that validated the answer.
+ return validation.canonical_key if validation is not None else None
+ if verifier == MATHIR_MENU_VERIFIER:
+ validation = validate_mathir_action_menu(candidate, spec)
+ # Menu labels are expanded first. Identity comes only from the exact
+ # normalized states produced by executing those concrete operations.
+ return validation.canonical_key if validation is not None else None
+ if verifier == PYTHON_FACTOR_VERIFIER:
+ validation = validate_python_factor_function_external(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier == PANTRY_PLAN_VERIFIER:
+ validation = validate_pantry_plan(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier in {POINT_MAZE_VERIFIER, ANT_MAZE_VERIFIER}:
+ validation = validate_maze_action_program_external(candidate, spec)
+ return validation.canonical_key if validation is not None else None
+ if verifier != "countdown":
+ return None
+ try:
+ target = Fraction(int(spec["target"]), 1)
+ expected_numbers = Counter(int(value) for value in spec["numbers"])
+ except Exception:
+ return None
+ text = _normalize_countdown_expression(candidate)
+ parts = [part.strip() for part in text.split("=") if part.strip()] or [text]
+ for part in parts:
+ if not re.fullmatch(r"[0-9+\-*/().\s*]+", part):
+ continue
+ try:
+ parsed = ast.parse(part, mode="eval")
+ value, used_numbers = _countdown_eval_and_numbers(parsed)
+ if value != target or Counter(used_numbers) != expected_numbers:
+ continue
+ # The key is derived from the exact AST object that passed
+ # execution and operand validation above.
+ return f"countdown:{_canonical_countdown_ast(parsed)}"
+ except Exception:
+ continue
+ return None
+
+
+@dataclass(frozen=True)
+class VerifiedExplorationIdentity:
+ """Separate prompt-local endpoint and cross-prompt route identities."""
+
+ verifier: str
+ endpoint_key: str
+ route_signature: str | None
+
+
+def validated_modebench_exploration_identity(
+ model_response: str,
+ gt_answer: Any,
+) -> VerifiedExplorationIdentity | None:
+ """Return hierarchical identity only after exact executable validation."""
+
+ spec = _parse_modebench_spec(gt_answer)
+ if spec is None:
+ return None
+ candidate = _extract_modebench_candidate(model_response, gt_answer)
+ if candidate is None:
+ return None
+ verifier = str(spec.get("verifier"))
+ if verifier == "graph_coloring":
+ colors = _graph_coloring_from_candidate(candidate, spec)
+ if not _verify_graph_coloring_colors(colors, spec):
+ return None
+ assert colors is not None
+ endpoint = "graph_coloring:" + "".join(str(int(color)) for color in colors)
+ return VerifiedExplorationIdentity(verifier, endpoint, None)
+ if verifier == MATHIR_VERIFIER:
+ validation = validate_mathir_algebra(candidate, spec)
+ if validation is None:
+ return None
+ solution = validation.solution
+ endpoint = f"mathir-solution:{solution.numerator}/{solution.denominator}"
+ return VerifiedExplorationIdentity(
+ verifier,
+ endpoint,
+ validation.route_signature,
+ )
+ if verifier == MATHIR_MENU_VERIFIER:
+ validation = validate_mathir_action_menu(candidate, spec)
+ if validation is None:
+ return None
+ solution = validation.solution
+ endpoint = f"mathir-solution:{solution.numerator}/{solution.denominator}"
+ return VerifiedExplorationIdentity(
+ verifier,
+ endpoint,
+ validation.route_signature,
+ )
+ if verifier == PYTHON_FACTOR_VERIFIER:
+ validation = validate_python_factor_function_external(candidate, spec)
+ if validation is None:
+ return None
+ try:
+ route = python_factor_route_signature(candidate)
+ except Exception:
+ return None
+ return VerifiedExplorationIdentity(
+ verifier,
+ validation.canonical_key,
+ route,
+ )
+ if verifier == PANTRY_PLAN_VERIFIER:
+ validation = validate_pantry_plan(candidate, spec)
+ if validation is None:
+ return None
+ return VerifiedExplorationIdentity(
+ verifier,
+ validation.canonical_key,
+ None,
+ )
+ if verifier in {POINT_MAZE_VERIFIER, ANT_MAZE_VERIFIER}:
+ validation = validate_maze_action_program_external(candidate, spec)
+ if validation is None:
+ return None
+ return VerifiedExplorationIdentity(
+ verifier,
+ f"{verifier}:goal:{spec.get('map_id')}",
+ validation.canonical_key,
+ )
+ if verifier != "countdown":
+ return None
+ try:
+ target = Fraction(int(spec["target"]), 1)
+ expected_numbers = Counter(int(value) for value in spec["numbers"])
+ except Exception:
+ return None
+ text = _normalize_countdown_expression(candidate)
+ parts = [part.strip() for part in text.split("=") if part.strip()] or [text]
+ for part in parts:
+ if not re.fullmatch(r"[0-9+\-*/().\s*]+", part):
+ continue
+ try:
+ parsed = ast.parse(part, mode="eval")
+ value, used_numbers = _countdown_eval_and_numbers(parsed)
+ if value != target or Counter(used_numbers) != expected_numbers:
+ continue
+ endpoint = f"countdown-value:{value.numerator}/{value.denominator}"
+ route = "countdown-route:v1:" + _canonical_countdown_route_ast(parsed)
+ return VerifiedExplorationIdentity(verifier, endpoint, route)
+ except Exception:
+ continue
+ return None
+
+
+def _grade_modebench_answer(model_answer: str, gt_answer: Any) -> bool | None:
+ spec = _parse_modebench_spec(gt_answer)
+ if spec is None:
+ return None
+ verifier = str(spec.get("verifier"))
+ if verifier == "graph_coloring":
+ return _verify_graph_coloring_answer(model_answer, spec)
+ if verifier == "countdown":
+ return _verify_countdown_expression(model_answer, spec)
+ if verifier == MATHIR_VERIFIER:
+ return validate_mathir_algebra(model_answer, spec) is not None
+ if verifier == MATHIR_MENU_VERIFIER:
+ return validate_mathir_action_menu(model_answer, spec) is not None
+ if verifier == PYTHON_FACTOR_VERIFIER:
+ return validate_python_factor_function_external(model_answer, spec) is not None
+ if verifier == PANTRY_PLAN_VERIFIER:
+ return validate_pantry_plan(model_answer, spec) is not None
+ if verifier in {POINT_MAZE_VERIFIER, ANT_MAZE_VERIFIER}:
+ return validate_maze_action_program_external(model_answer, spec) is not None
+ return False
+
+
+def _clean_final_answer_candidate(candidate: str | None) -> str | None:
+ """Return a compact final-answer candidate, or ``None`` for malformed text."""
+
+ if candidate is None:
+ return None
+ candidate = str(candidate).strip()
+ if not candidate:
+ return None
+ # Answer identity is deliberately stricter than correctness grading: mode
+ # metrics need one stable, compact key per formatted final answer.
+ if "\n" in candidate or "\r" in candidate or "<" in candidate or ">" in candidate:
+ return None
+ if len(candidate) > 160:
+ return None
+ candidate = re.sub(
+ r"^\s*(?:the\s+)?(?:final\s+)?answer\s*(?:is|=|:)\s*",
+ "",
+ candidate,
+ flags=re.IGNORECASE,
+ ).strip()
+ return candidate or None
+
+
+def _extract_r1_reasoning_and_answer_sections(
+ model_response: str,
+) -> tuple[str | None, str | None]:
+ """Return ``( body, body)`` for an R1-style trace.
+
+ Training prompts already end inside an open ```` tag, so many model
+ responses begin with reasoning text and only emit the closing ````
+ before ````. We accept both:
+
+ 1. full tagged traces containing ``......``
+ 2. response-only continuations containing ``... ...``
+ """
+
+ if "" not in model_response or "" not in model_response:
+ return None, None
+ try:
+ prefix, answer_suffix = model_response.split("", 1)
+ answer = answer_suffix.split("", 1)[0].strip()
+ reasoning = None
+ if "" in prefix and "" in prefix:
+ reasoning = prefix.split("", 1)[1].split("", 1)[0].strip()
+ elif "" in prefix:
+ reasoning = prefix.split("", 1)[0].strip()
+ else:
+ return None, None
+ answer = answer
+ except Exception:
+ return None, None
+ return reasoning or None, answer or None
+
+
+def extract_normalized_final_answer(
+ model_response: str, *, template: str = "r1", gt_answer: Any = None
+) -> str | None:
+ """Return a conservative canonical key for an answer or exact benchmark mode."""
+
+ try:
+ if gt_answer is not None:
+ if _parse_modebench_spec(gt_answer) is not None:
+ return _modebench_answer_key(
+ model_response,
+ gt_answer,
+ )
+ modebench_key = _modebench_answer_key(
+ model_response,
+ gt_answer,
+ )
+ if modebench_key is not None:
+ return modebench_key
+ candidate = None
+ if template == "r1":
+ # Match the training reward's strict R1 formatting gate.
+ _, candidate = _extract_r1_reasoning_and_answer_sections(model_response)
+ if candidate is None:
+ return None
+ else:
+ candidate = extract_answer(model_response)
+ if candidate is None:
+ return None
+ extracted = extract_answer(candidate) if "\\boxed" in candidate else candidate
+ extracted = _clean_final_answer_candidate(extracted)
+ if extracted is None:
+ return None
+ normalized = normalize_final_answer(extracted)
+ normalized = _normalize(normalized)
+ if normalized is None:
+ normalized = normalize_final_answer(extracted)
+ if normalized is None:
+ return None
+ normalized = str(normalized).strip().lower()
+ return normalized or None
+ except Exception:
+ return None
+
+
+def grade(model_answer: str, gt_answer: str, fast: bool = True):
+ if "\\boxed" in gt_answer:
+ gt_answer = extract_answer(gt_answer)
+ correct = grade_answer_mathd(model_answer, gt_answer) or grade_answer_sympy(
+ model_answer, gt_answer
+ )
+ if not fast:
+ # This mode further uses math_verify to recall originally false positives.
+ # Will be a bit slower, and sensitive to bad inputs.
+ correct = correct or is_latex_equal(
+ model_answer,
+ gt_answer,
+ )
+ return correct
+
+
+def boxed_reward_fn(model_response, gt_answer, fast=False):
+ model_answer = _extract_modebench_candidate(model_response, gt_answer)
+ if model_answer is not None:
+ modebench_correct = _grade_modebench_answer(model_answer, gt_answer)
+ if modebench_correct is not None:
+ return {"formatted": True}, 1.0 if modebench_correct else 0.0
+ model_answer = extract_answer(model_response)
+ if model_answer is None:
+ return {"formatted": False}, 0.0 # Cannot even parse anything.
+ modebench_correct = _grade_modebench_answer(model_answer, gt_answer)
+ if modebench_correct is not None:
+ return {"formatted": True}, 1.0 if modebench_correct else 0.0
+ if isinstance(gt_answer, float) or isinstance(gt_answer, int):
+ gt_answer = str(gt_answer)
+ if isinstance(gt_answer, str):
+ is_correct = grade(model_answer, gt_answer, fast)
+ elif isinstance(gt_answer, list):
+ is_correct = False
+ for gt in gt_answer:
+ is_correct |= grade(model_answer, gt, fast)
+ if is_correct:
+ return {"formatted": True}, 1.0 # Correctness reward.
+ else:
+ return {
+ "formatted": True
+ }, 0.0 # Formatted but wrong answer; no format reward to avoid hacking.
+
+
+def validated_math_route_signature(
+ model_response: str,
+ problem: str,
+ gt_answer: Any,
+ *,
+ fast: bool = False,
+ task_verified: bool | None = None,
+) -> str | None:
+ """Return an executable route identity only for a task-correct response.
+
+ Task correctness, route execution, and agreement between the trace terminal
+ value and the response's boxed answer are independent fail-closed checks.
+ A correct answer without a valid trace keeps its ordinary task reward but
+ has no route identity.
+ """
+
+ try:
+ if task_verified is None:
+ _info, reward = boxed_reward_fn(
+ model_response,
+ gt_answer,
+ fast=fast,
+ )
+ task_verified = float(reward) > 0.0
+ if not bool(task_verified):
+ return None
+ validation = validate_math_route_response(model_response, problem)
+ if validation is None:
+ return None
+ model_answer = extract_answer(model_response)
+ if model_answer is None:
+ return None
+ terminal_answer = sympy.latex(validation.terminal_value)
+ if not grade(model_answer, terminal_answer, fast=fast):
+ return None
+ return validation.route_signature
+ except Exception:
+ return None
+
+
+def validated_exploration_identity(
+ model_response: str,
+ problem: str,
+ gt_answer: Any,
+ *,
+ fast: bool = False,
+ task_verified: bool | None = None,
+) -> VerifiedExplorationIdentity | None:
+ """Return the endpoint/route pair for ModeBench or free-form MATH.
+
+ ModeBench references always remain on their exact executable validator
+ path. Free-form MATH may reuse the actor's already-computed task verdict,
+ but route admission still independently executes the restricted trace and
+ checks its terminal value against the boxed response. Thus a task-correct
+ response without a valid route retains an endpoint identity and ordinary
+ reward while contributing no route identity.
+ """
+
+ if _parse_modebench_spec(gt_answer) is not None:
+ return validated_modebench_exploration_identity(
+ model_response,
+ gt_answer,
+ )
+ try:
+ if task_verified is None:
+ _info, reward = boxed_reward_fn(
+ model_response,
+ gt_answer,
+ fast=fast,
+ )
+ task_verified = float(reward) > 0.0
+ if not bool(task_verified):
+ return None
+ endpoint = extract_normalized_final_answer(
+ model_response,
+ template="qwen_math_route",
+ gt_answer=gt_answer,
+ )
+ if endpoint is None:
+ return None
+ route = validated_math_route_signature(
+ model_response,
+ problem,
+ gt_answer,
+ fast=fast,
+ task_verified=True,
+ )
+ return VerifiedExplorationIdentity(
+ verifier="math_verify",
+ endpoint_key=f"math-answer:{endpoint}",
+ route_signature=route,
+ )
+ except Exception:
+ return None
+
+
+def answer_tag_reward_fn(model_response, gt_answer, fast=False):
+ # We are strict about format to evaluate our models.
+ if " " in model_response and "" in model_response:
+ model_answer = model_response.split("")[-1].replace("", "")
+ if "\\boxed" in model_answer:
+ model_answer = extract_answer(model_answer)
+ if model_answer is None:
+ return {"formatted": True}, 0.0
+ modebench_plain_answer = _extract_modebench_candidate(model_answer, gt_answer)
+ if modebench_plain_answer is not None:
+ model_answer = modebench_plain_answer
+ modebench_correct = _grade_modebench_answer(model_answer, gt_answer)
+ if modebench_correct is not None:
+ return {"formatted": True}, 1.0 if modebench_correct else 0.0
+ if isinstance(gt_answer, float) or isinstance(gt_answer, int):
+ gt_answer = str(gt_answer)
+ if isinstance(gt_answer, str):
+ is_correct = grade(model_answer, gt_answer, fast)
+ elif isinstance(gt_answer, list):
+ is_correct = False
+ for gt in gt_answer:
+ is_correct |= grade(model_answer, gt, fast)
+ if is_correct:
+ return {"formatted": True}, 1.0 # Correctness reward.
+ else:
+ return (
+ {"formatted": True},
+ 0.0,
+ ) # Formatted but wrong answer; no format reward to avoid hacking.
+ else:
+ return {"formatted": False}, 0.0 # Unformatted.
+
+
+def answer_tag_reward_fn_for_orz(model_response, gt_answer, fast=False):
+ # We are a bit less strict for baselines.
+ if "" in model_response and "" in model_response:
+ model_answer = model_response.split("")[-1].replace("", "")
+ if "\\boxed" in model_answer:
+ model_answer = extract_answer(model_answer)
+ if model_answer is None:
+ return {"formatted": True}, 0.0
+ modebench_plain_answer = _extract_modebench_candidate(model_answer, gt_answer)
+ if modebench_plain_answer is not None:
+ model_answer = modebench_plain_answer
+ modebench_correct = _grade_modebench_answer(model_answer, gt_answer)
+ if modebench_correct is not None:
+ return {"formatted": True}, 1.0 if modebench_correct else 0.0
+ if isinstance(gt_answer, float) or isinstance(gt_answer, int):
+ gt_answer = str(gt_answer)
+ if isinstance(gt_answer, str):
+ is_correct = grade(model_answer, gt_answer, fast)
+ elif isinstance(gt_answer, list):
+ is_correct = False
+ for gt in gt_answer:
+ is_correct |= grade(model_answer, gt, fast)
+ if is_correct:
+ return {"formatted": True}, 1.0 # Correctness reward.
+ else:
+ return (
+ {"formatted": True},
+ 0.0,
+ ) # Formatted but wrong answer; no format reward to avoid hacking.
+ else:
+ return {"formatted": False}, 0.0 # Unformatted.
diff --git a/code/oat_drgrpo/math_route.py b/code/oat_drgrpo/math_route.py
new file mode 100644
index 0000000000000000000000000000000000000000..934d0b61350b9d75a780ab045ae2fe08e41e3bf3
--- /dev/null
+++ b/code/oat_drgrpo/math_route.py
@@ -0,0 +1,458 @@
+"""Fail-closed executable route traces for answer-verified free-form math.
+
+The trace is deliberately separate from the natural-language derivation and
+from the boxed answer. It contains only references and operations; it never
+contains model-declared intermediate results. Numeric leaves must be grounded
+in the problem text, every operation is executed exactly, and every trace node
+must contribute to the terminal value.
+
+This validator establishes a checkable computational route, not a proof that
+the route's modeling assumptions follow from the prose problem. Ordinary MATH
+answer verification remains a separate mandatory admission condition.
+"""
+
+from __future__ import annotations
+
+from collections import Counter
+from dataclasses import dataclass
+from fractions import Fraction
+import json
+import math
+import re
+from typing import Any, Mapping
+
+import sympy
+
+
+MATH_ROUTE_VERSION = "math-route-v1"
+MATH_ROUTE_RPN_VERSION = "math-route-rpn-v2"
+_MAX_TRACE_CHARS = 4_096
+_MAX_STEPS = 20
+_MAX_ARGS = 6
+_MAX_INTEGER_BITS = 512
+_ID_RE = re.compile(r"s[1-9][0-9]?")
+_NUMBER_RE = re.compile(
+ r"(?(.*?)", re.DOTALL)
+_COMMUTATIVE_OPS = frozenset({"add", "mul", "gcd", "lcm", "min", "max"})
+_UNARY_OPS = frozenset(
+ {"neg", "abs", "square", "cube", "sqrt", "factorial", "percent"}
+)
+_BINARY_OPS = frozenset(
+ {"sub", "div", "pow", "mod", "choose", "permute"}
+)
+_NARY_OPS = frozenset({"add", "mul", "gcd", "lcm", "min", "max", "average"})
+_ALLOWED_TOP_LEVEL = frozenset({"version", "steps", "final"})
+_ALLOWED_STEP_KEYS = frozenset({"id", "op", "args", "value"})
+_RPN_UNARY_OPS = {
+ "neg": "neg",
+ "abs": "abs",
+ "square": "square",
+ "cube": "cube",
+ "sqrt": "sqrt",
+ "factorial": "factorial",
+ "percent": "percent",
+}
+_RPN_BINARY_OPS = {
+ "add": "add",
+ "mul": "mul",
+ "sub": "sub",
+ "div": "div",
+ "pow": "pow",
+ "mod": "mod",
+ "choose": "choose",
+ "permute": "permute",
+ "gcd": "gcd",
+ "lcm": "lcm",
+ "min": "min",
+ "max": "max",
+ "average": "average",
+}
+
+
+@dataclass(frozen=True)
+class MathRouteValidation:
+ route_signature: str
+ terminal_value: sympy.Expr
+ operations: tuple[str, ...]
+ step_count: int
+ source_count: int
+
+
+class MathRouteError(ValueError):
+ """Raised for a malformed, ungrounded, or non-executable trace."""
+
+
+def _fraction(text: str) -> Fraction:
+ compact = str(text).replace(",", "").strip()
+ if re.fullmatch(r"-?\d+", compact):
+ return Fraction(int(compact), 1)
+ if re.fullmatch(r"-?\d+/\d+", compact):
+ numerator, denominator = compact.split("/", 1)
+ if int(denominator) == 0:
+ raise MathRouteError("zero denominator")
+ return Fraction(int(numerator), int(denominator))
+ if re.fullmatch(r"-?\d+\.\d+", compact):
+ return Fraction(compact)
+ raise MathRouteError("source values must be exact integers, decimals, or fractions")
+
+
+def problem_number_inventory(problem: str) -> Counter[Fraction]:
+ """Return exact numeric leaves present in problem text, with multiplicity."""
+
+ text = str(problem)
+ inventory: Counter[Fraction] = Counter()
+
+ def consume_latex(match: re.Match[str]) -> str:
+ numerator, denominator = match.groups()
+ inventory[Fraction(int(numerator.replace(",", "")), int(denominator.replace(",", "")))] += 1
+ return " "
+
+ def consume_slash(match: re.Match[str]) -> str:
+ numerator, denominator = match.groups()
+ inventory[Fraction(int(numerator.replace(",", "")), int(denominator.replace(",", "")))] += 1
+ return " "
+
+ text = _LATEX_FRACTION_RE.sub(consume_latex, text)
+ text = _SLASH_FRACTION_RE.sub(consume_slash, text)
+ for match in _NUMBER_RE.finditer(text):
+ inventory[_fraction(match.group(0))] += 1
+ return inventory
+
+
+def extract_math_route_block(model_response: str) -> str | None:
+ matches = _ROUTE_BLOCK_RE.findall(str(model_response))
+ if len(matches) != 1:
+ return None
+ block = matches[0].strip()
+ if not block or len(block) > _MAX_TRACE_CHARS:
+ return None
+ return block
+
+
+def _exact_integer(value: sympy.Expr, *, name: str) -> int:
+ simplified = sympy.simplify(value)
+ if not simplified.is_Integer:
+ raise MathRouteError(f"{name} requires an exact integer")
+ integer = int(simplified)
+ if abs(integer).bit_length() > _MAX_INTEGER_BITS:
+ raise MathRouteError(f"{name} integer is too large")
+ return integer
+
+
+def _bounded(value: sympy.Expr) -> sympy.Expr:
+ simplified = sympy.simplify(value)
+ if simplified.has(
+ sympy.nan,
+ sympy.zoo,
+ sympy.oo,
+ -sympy.oo,
+ sympy.I,
+ ):
+ raise MathRouteError("operation produced a non-finite or complex value")
+ if len(str(simplified)) > 1_024 or int(sympy.count_ops(simplified)) > 128:
+ raise MathRouteError("operation result is too large")
+ for atom in simplified.atoms(sympy.Integer):
+ if abs(int(atom)).bit_length() > _MAX_INTEGER_BITS:
+ raise MathRouteError("operation result integer is too large")
+ return simplified
+
+
+def _execute_operation(op: str, arguments: tuple[sympy.Expr, ...]) -> sympy.Expr:
+ if op in _UNARY_OPS and len(arguments) != 1:
+ raise MathRouteError(f"{op} requires one argument")
+ if op in _BINARY_OPS and len(arguments) != 2:
+ raise MathRouteError(f"{op} requires two arguments")
+ if op in _NARY_OPS and not 2 <= len(arguments) <= _MAX_ARGS:
+ raise MathRouteError(f"{op} requires two to {_MAX_ARGS} arguments")
+ if op == "neg":
+ result = -arguments[0]
+ elif op == "abs":
+ result = sympy.Abs(arguments[0])
+ elif op == "square":
+ result = arguments[0] ** 2
+ elif op == "cube":
+ result = arguments[0] ** 3
+ elif op == "sqrt":
+ if arguments[0].is_nonnegative is not True:
+ raise MathRouteError("sqrt requires a provably nonnegative argument")
+ result = sympy.sqrt(arguments[0])
+ elif op == "factorial":
+ integer = _exact_integer(arguments[0], name=op)
+ if not 0 <= integer <= 100:
+ raise MathRouteError("factorial input is outside [0,100]")
+ result = sympy.factorial(integer)
+ elif op == "percent":
+ result = arguments[0] / 100
+ elif op == "add":
+ result = sum(arguments, sympy.Integer(0))
+ elif op == "mul":
+ result = math.prod(arguments, start=sympy.Integer(1))
+ elif op == "sub":
+ result = arguments[0] - arguments[1]
+ elif op == "div":
+ if sympy.simplify(arguments[1]) == 0:
+ raise MathRouteError("division by zero")
+ result = arguments[0] / arguments[1]
+ elif op == "pow":
+ exponent = _exact_integer(arguments[1], name=op)
+ if not -12 <= exponent <= 12:
+ raise MathRouteError("power exponent is outside [-12,12]")
+ if arguments[0] == 0 and exponent < 0:
+ raise MathRouteError("zero to a negative power")
+ result = arguments[0] ** exponent
+ elif op == "mod":
+ left = _exact_integer(arguments[0], name=op)
+ right = _exact_integer(arguments[1], name=op)
+ if right == 0:
+ raise MathRouteError("modulo by zero")
+ result = sympy.Integer(left % right)
+ elif op == "choose":
+ n = _exact_integer(arguments[0], name=op)
+ k = _exact_integer(arguments[1], name=op)
+ if not 0 <= k <= n <= 10_000:
+ raise MathRouteError("choose inputs are outside 0 <= k <= n <= 10000")
+ result = sympy.binomial(n, k)
+ elif op == "permute":
+ n = _exact_integer(arguments[0], name=op)
+ k = _exact_integer(arguments[1], name=op)
+ if not 0 <= k <= n <= 1_000:
+ raise MathRouteError("permute inputs are outside 0 <= k <= n <= 1000")
+ result = sympy.factorial(n) / sympy.factorial(n - k)
+ elif op in {"gcd", "lcm"}:
+ integers = [_exact_integer(argument, name=op) for argument in arguments]
+ function = math.gcd if op == "gcd" else math.lcm
+ result = sympy.Integer(function(*integers))
+ elif op == "min":
+ if not all(argument.is_real is True for argument in arguments):
+ raise MathRouteError("min requires real arguments")
+ result = sympy.Min(*arguments)
+ elif op == "max":
+ if not all(argument.is_real is True for argument in arguments):
+ raise MathRouteError("max requires real arguments")
+ result = sympy.Max(*arguments)
+ elif op == "average":
+ result = sum(arguments, sympy.Integer(0)) / len(arguments)
+ else:
+ raise MathRouteError(f"unsupported route operation {op!r}")
+ return _bounded(result)
+
+
+def _signature(op: str, argument_signatures: tuple[str, ...]) -> str:
+ children = (
+ tuple(sorted(argument_signatures))
+ if op in _COMMUTATIVE_OPS
+ else argument_signatures
+ )
+ return f"{op}({','.join(children)})"
+
+
+def validate_math_route_trace(
+ trace: str | Mapping[str, Any],
+ problem: str,
+) -> MathRouteValidation | None:
+ """Parse and execute a grounded route trace; return ``None`` on any failure."""
+
+ try:
+ parsed = json.loads(trace) if isinstance(trace, str) else dict(trace)
+ if not isinstance(parsed, dict) or set(parsed) != _ALLOWED_TOP_LEVEL:
+ raise MathRouteError("route object has missing or extra fields")
+ if parsed["version"] != MATH_ROUTE_VERSION:
+ raise MathRouteError("unsupported route version")
+ raw_steps = parsed["steps"]
+ if not isinstance(raw_steps, list) or not 2 <= len(raw_steps) <= _MAX_STEPS:
+ raise MathRouteError("route has an invalid number of steps")
+ final_id = str(parsed["final"])
+ if _ID_RE.fullmatch(final_id) is None:
+ raise MathRouteError("invalid final node ID")
+
+ inventory = problem_number_inventory(problem)
+ values: dict[str, sympy.Expr] = {}
+ signatures: dict[str, str] = {}
+ dependencies: dict[str, tuple[str, ...]] = {}
+ operations: list[str] = []
+ source_count = 0
+ non_source_count = 0
+ for expected_index, raw_step in enumerate(raw_steps, start=1):
+ if not isinstance(raw_step, dict):
+ raise MathRouteError("route step must be an object")
+ if not set(raw_step).issubset(_ALLOWED_STEP_KEYS):
+ raise MathRouteError("route step has extra fields")
+ step_id = str(raw_step.get("id", ""))
+ if step_id != f"s{expected_index}" or step_id in values:
+ raise MathRouteError("route node IDs must be contiguous and ordered")
+ op = str(raw_step.get("op", ""))
+ if op == "source":
+ if set(raw_step) != {"id", "op", "value"}:
+ raise MathRouteError("source step schema is invalid")
+ value = _fraction(str(raw_step["value"]))
+ if inventory[value] <= 0:
+ raise MathRouteError("source value is not available in the problem")
+ inventory[value] -= 1
+ values[step_id] = sympy.Rational(value.numerator, value.denominator)
+ signatures[step_id] = "input"
+ dependencies[step_id] = ()
+ source_count += 1
+ operations.append(op)
+ continue
+
+ if set(raw_step) != {"id", "op", "args"}:
+ raise MathRouteError("operation step schema is invalid")
+ raw_args = raw_step["args"]
+ if not isinstance(raw_args, list):
+ raise MathRouteError("operation args must be a list")
+ argument_ids = tuple(str(argument) for argument in raw_args)
+ if (
+ not argument_ids
+ or len(argument_ids) > _MAX_ARGS
+ or len(set(argument_ids)) != len(argument_ids)
+ or any(argument_id not in values for argument_id in argument_ids)
+ ):
+ raise MathRouteError("operation dependencies are invalid")
+ arguments = tuple(values[argument_id] for argument_id in argument_ids)
+ values[step_id] = _execute_operation(op, arguments)
+ signatures[step_id] = _signature(
+ op,
+ tuple(signatures[argument_id] for argument_id in argument_ids),
+ )
+ dependencies[step_id] = argument_ids
+ operations.append(op)
+ non_source_count += 1
+
+ if final_id not in values or non_source_count < 1 or source_count < 1:
+ raise MathRouteError("route lacks a computed terminal value")
+ reachable = {final_id}
+ frontier = [final_id]
+ while frontier:
+ node = frontier.pop()
+ for dependency in dependencies[node]:
+ if dependency not in reachable:
+ reachable.add(dependency)
+ frontier.append(dependency)
+ if reachable != set(values):
+ raise MathRouteError("every route node must contribute to the final value")
+ return MathRouteValidation(
+ route_signature=(
+ f"math-route:{MATH_ROUTE_VERSION}:{signatures[final_id]}"
+ ),
+ terminal_value=values[final_id],
+ operations=tuple(operations),
+ step_count=len(values),
+ source_count=source_count,
+ )
+ except Exception:
+ return None
+
+
+def validate_math_route_rpn(
+ trace: str,
+ problem: str,
+) -> MathRouteValidation | None:
+ """Execute the compact v2 reverse-Polish route language.
+
+ Numeric tokens are grounded problem leaves. Unary and binary operator
+ tokens consume the stack, so a successful one-item terminal stack also
+ proves that every emitted token contributes to the final value. The
+ language has no result literals, variable names, prose, or code execution.
+ """
+
+ try:
+ compact = str(trace).strip()
+ if not compact or len(compact) > 1_024:
+ raise MathRouteError("RPN route is empty or too long")
+ tokens = compact.split()
+ if not tokens or tokens[0] not in {
+ "v2",
+ MATH_ROUTE_RPN_VERSION,
+ }:
+ raise MathRouteError("unsupported RPN route version")
+ tokens = tokens[1:]
+ if not 2 <= len(tokens) <= 40:
+ raise MathRouteError("RPN route has an invalid token count")
+
+ inventory = problem_number_inventory(problem)
+ stack: list[tuple[sympy.Expr, str]] = []
+ operations: list[str] = []
+ source_count = 0
+ operation_count = 0
+ for token in tokens:
+ if token in _RPN_UNARY_OPS:
+ if len(stack) < 1:
+ raise MathRouteError("RPN unary operation underflow")
+ argument_value, argument_signature = stack.pop()
+ op = _RPN_UNARY_OPS[token]
+ stack.append(
+ (
+ _execute_operation(op, (argument_value,)),
+ _signature(op, (argument_signature,)),
+ )
+ )
+ operations.append(op)
+ operation_count += 1
+ continue
+ if token in _RPN_BINARY_OPS:
+ if len(stack) < 2:
+ raise MathRouteError("RPN binary operation underflow")
+ right_value, right_signature = stack.pop()
+ left_value, left_signature = stack.pop()
+ op = _RPN_BINARY_OPS[token]
+ stack.append(
+ (
+ _execute_operation(op, (left_value, right_value)),
+ _signature(op, (left_signature, right_signature)),
+ )
+ )
+ operations.append(op)
+ operation_count += 1
+ continue
+
+ value = _fraction(token)
+ if inventory[value] <= 0:
+ raise MathRouteError("RPN source value is not available in the problem")
+ inventory[value] -= 1
+ stack.append(
+ (
+ sympy.Rational(value.numerator, value.denominator),
+ "input",
+ )
+ )
+ operations.append("source")
+ source_count += 1
+
+ if len(stack) != 1 or source_count < 1 or operation_count < 1:
+ raise MathRouteError("RPN route lacks one computed terminal value")
+ terminal_value, terminal_signature = stack[0]
+ return MathRouteValidation(
+ route_signature=(
+ f"math-route:{MATH_ROUTE_RPN_VERSION}:{terminal_signature}"
+ ),
+ terminal_value=terminal_value,
+ operations=tuple(operations),
+ step_count=len(tokens),
+ source_count=source_count,
+ )
+ except Exception:
+ return None
+
+
+def validate_math_route_response(
+ model_response: str,
+ problem: str,
+) -> MathRouteValidation | None:
+ """Extract the response's unique route block and execute it."""
+
+ block = extract_math_route_block(model_response)
+ if block is None:
+ return None
+ if block.lstrip().startswith("{"):
+ return validate_math_route_trace(block, problem)
+ return validate_math_route_rpn(block, problem)
diff --git a/code/oat_drgrpo/mathir.py b/code/oat_drgrpo/mathir.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8ae33d19bcc57f45ae54419f18df65794fb2cd6
--- /dev/null
+++ b/code/oat_drgrpo/mathir.py
@@ -0,0 +1,906 @@
+"""A small, fail-closed executable algebra language for canonical search.
+
+MathIR linear v0 is intentionally narrower than ordinary mathematical text.
+The model emits a semicolon-separated sequence of equation transformations,
+for example ``sub(b);div(a)``. Every command is applied to both sides of the
+current equation and exact rational normalization happens after every step.
+
+The validator and canonicalizer share one execution path: a canonical strategy
+key is produced only from the normalized states created by a successful
+execution. There is no parser for prose, LaTeX derivations, Python, or a
+model-supplied final answer.
+"""
+
+from __future__ import annotations
+
+from collections import Counter
+from dataclasses import dataclass
+from fractions import Fraction
+from itertools import permutations
+import re
+from typing import Any, Iterable, Mapping
+
+import sympy
+
+
+MATHIR_VERIFIER = "mathir_algebra"
+MATHIR_VERSION = "linear-v0"
+MATHIR_MENU_VERIFIER = "mathir_action_menu"
+MATHIR_MENU_VERSION = "linear-menu-v1"
+MATHIR_ROUTE_VERSION = "linear-route-v1"
+_MAX_REFERENCE_SYMBOLS = 6
+_MAX_PROGRAM_STEPS = 4
+_MAX_PROGRAM_CHARS = 160
+_MAX_ARGUMENT_NODES = 11
+_MAX_ARGUMENT_DEPTH = 6
+_MAX_MENU_ACTIONS = 8
+_MODEL_OPERATORS = frozenset({"add", "sub", "mul", "div", "neg"})
+_COMMANDS = frozenset({"add", "sub", "mul", "div"})
+_TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_]*|[(),;]")
+_MENU_ACTION_RE = re.compile(r"[A-H]")
+
+
+@dataclass(frozen=True)
+class Expr:
+ """A bounded MathIR expression.
+
+ ``const`` nodes are interpreter-internal exact rationals. The model-side
+ parser never accepts numeric literals.
+ """
+
+ op: str
+ args: tuple["Expr", ...] = ()
+ value: str | Fraction | None = None
+
+
+@dataclass(frozen=True)
+class Command:
+ op: str
+ argument: Expr
+
+
+@dataclass(frozen=True)
+class EquationState:
+ lhs: Expr
+ rhs: Expr
+
+
+@dataclass(frozen=True)
+class MathIRValidation:
+ canonical_key: str
+ solution: Fraction
+ commands: tuple[Command, ...]
+ states: tuple[EquationState, ...]
+ action_ids: tuple[str, ...] = ()
+ route_signature: str = ""
+
+
+class MathIRError(ValueError):
+ """Raised for a malformed or invalid MathIR program."""
+
+
+class _ExpressionParser:
+ def __init__(
+ self,
+ tokens: list[str],
+ *,
+ allowed_symbols: frozenset[str],
+ allow_constants: bool,
+ ) -> None:
+ self.tokens = tokens
+ self.index = 0
+ self.allowed_symbols = allowed_symbols
+ self.allow_constants = bool(allow_constants)
+
+ def _take(self, expected: str | None = None) -> str:
+ if self.index >= len(self.tokens):
+ raise MathIRError("unexpected end of expression")
+ token = self.tokens[self.index]
+ if expected is not None and token != expected:
+ raise MathIRError(f"expected {expected!r}")
+ self.index += 1
+ return token
+
+ def parse(self, *, depth: int = 0) -> Expr:
+ if depth > _MAX_ARGUMENT_DEPTH:
+ raise MathIRError("expression nesting is too deep")
+ token = self._take()
+ if token in {"(", ")", ",", ";"}:
+ raise MathIRError("expected a symbol or operator")
+ if self.index < len(self.tokens) and self.tokens[self.index] == "(":
+ if token not in _MODEL_OPERATORS:
+ raise MathIRError(f"unsupported operator {token!r}")
+ self._take("(")
+ first = self.parse(depth=depth + 1)
+ if token == "neg":
+ self._take(")")
+ return Expr("neg", (first,))
+ self._take(",")
+ second = self.parse(depth=depth + 1)
+ self._take(")")
+ return Expr(token, (first, second))
+ if token in self.allowed_symbols:
+ return Expr("symbol", value=token)
+ if self.allow_constants and re.fullmatch(r"-?\d+(?:/\d+)?", token):
+ return Expr("const", value=Fraction(token))
+ raise MathIRError(f"unknown symbol {token!r}")
+
+
+def _tokenize(text: str) -> list[str]:
+ compact = re.sub(r"\s+", "", str(text))
+ if not compact:
+ raise MathIRError("empty MathIR text")
+ tokens = _TOKEN_RE.findall(compact)
+ if "".join(tokens) != compact:
+ raise MathIRError("unsupported MathIR character or numeric literal")
+ return tokens
+
+
+def parse_mathir_expression(
+ text: str,
+ *,
+ allowed_symbols: Iterable[str],
+) -> Expr:
+ """Parse one model-authored expression without using Python evaluation."""
+
+ tokens = _tokenize(text)
+ parser = _ExpressionParser(
+ tokens,
+ allowed_symbols=frozenset(str(symbol) for symbol in allowed_symbols),
+ allow_constants=False,
+ )
+ expression = parser.parse()
+ if parser.index != len(tokens):
+ raise MathIRError("trailing expression tokens")
+ if _expr_node_count(expression) > _MAX_ARGUMENT_NODES:
+ raise MathIRError("expression is too large")
+ return expression
+
+
+def _parse_trusted_expression(
+ text: str,
+ *,
+ allowed_symbols: Iterable[str],
+) -> Expr:
+ """Parse a dataset-owned formal expression.
+
+ Dataset expressions currently use no constants, but this separate entry
+ point makes the trust boundary explicit and permits exact rationals if a
+ later, versioned reference schema needs them.
+ """
+
+ tokens = _tokenize(text)
+ parser = _ExpressionParser(
+ tokens,
+ allowed_symbols=frozenset(str(symbol) for symbol in allowed_symbols),
+ allow_constants=True,
+ )
+ expression = parser.parse()
+ if parser.index != len(tokens):
+ raise MathIRError("trailing trusted-expression tokens")
+ if _expr_node_count(expression) > 31:
+ raise MathIRError("trusted expression is too large")
+ return expression
+
+
+def parse_mathir_program(
+ text: str,
+ *,
+ allowed_symbols: Iterable[str],
+ max_steps: int,
+) -> tuple[Command, ...]:
+ """Parse a bounded sequence such as ``sub(b);div(a)``."""
+
+ compact = re.sub(r"\s+", "", str(text))
+ if not compact or len(compact) > _MAX_PROGRAM_CHARS:
+ raise MathIRError("program is empty or too long")
+ # A final statement terminator is surface formatting, not a new action.
+ compact = compact[:-1] if compact.endswith(";") else compact
+ if not compact or compact.startswith(";") or ";;" in compact:
+ raise MathIRError("empty program command")
+ command_texts = compact.split(";")
+ if not 1 <= len(command_texts) <= int(max_steps):
+ raise MathIRError("program has an invalid number of commands")
+ commands: list[Command] = []
+ for command_text in command_texts:
+ match = re.fullmatch(r"([A-Za-z][A-Za-z0-9_]*)\((.*)\)", command_text)
+ if match is None:
+ raise MathIRError("commands must use op(expression) syntax")
+ op, argument_text = match.groups()
+ if op not in _COMMANDS:
+ raise MathIRError(f"unsupported command {op!r}")
+ argument = parse_mathir_expression(
+ argument_text,
+ allowed_symbols=allowed_symbols,
+ )
+ commands.append(Command(op, argument))
+ return tuple(commands)
+
+
+def _expr_node_count(expression: Expr) -> int:
+ return 1 + sum(_expr_node_count(argument) for argument in expression.args)
+
+
+def _expr_symbols(expression: Expr) -> set[str]:
+ if expression.op == "symbol":
+ assert isinstance(expression.value, str)
+ return {expression.value}
+ return set().union(*(_expr_symbols(argument) for argument in expression.args), set())
+
+
+def _fraction_from_reference(value: Any) -> Fraction:
+ if isinstance(value, bool):
+ raise MathIRError("boolean binding")
+ if isinstance(value, int):
+ return Fraction(value, 1)
+ if isinstance(value, str) and re.fullmatch(r"-?\d+(?:/[1-9]\d*)?", value.strip()):
+ return Fraction(value.strip())
+ raise MathIRError("bindings must be exact integers or rational strings")
+
+
+def _expr_to_sympy(expression: Expr) -> sympy.Expr:
+ if expression.op == "symbol":
+ assert isinstance(expression.value, str)
+ return sympy.Symbol(expression.value)
+ if expression.op == "const":
+ assert isinstance(expression.value, Fraction)
+ return sympy.Rational(expression.value.numerator, expression.value.denominator)
+ converted = tuple(_expr_to_sympy(argument) for argument in expression.args)
+ if expression.op == "add":
+ return converted[0] + converted[1]
+ if expression.op == "sub":
+ return converted[0] - converted[1]
+ if expression.op == "mul":
+ return converted[0] * converted[1]
+ if expression.op == "div":
+ return converted[0] / converted[1]
+ if expression.op == "neg":
+ return -converted[0]
+ if expression.op == "inv":
+ return sympy.Integer(1) / converted[0]
+ raise MathIRError(f"unsupported internal expression {expression.op!r}")
+
+
+def _fold(op: str, arguments: tuple[Expr, ...]) -> Expr:
+ if not arguments:
+ return Expr("const", value=Fraction(0 if op == "add" else 1, 1))
+ result = arguments[0]
+ for argument in arguments[1:]:
+ result = Expr(op, (result, argument))
+ return result
+
+
+def _expr_from_sympy(expression: sympy.Expr) -> Expr:
+ if expression.is_Symbol:
+ return Expr("symbol", value=str(expression))
+ if expression.is_Rational:
+ return Expr(
+ "const",
+ value=Fraction(int(expression.p), int(expression.q)),
+ )
+ if expression.is_Add:
+ return _fold(
+ "add",
+ tuple(_expr_from_sympy(argument) for argument in expression.args),
+ )
+ if expression.is_Mul:
+ return _fold(
+ "mul",
+ tuple(_expr_from_sympy(argument) for argument in expression.args),
+ )
+ if expression.is_Pow and expression.exp == -1:
+ return Expr("inv", (_expr_from_sympy(expression.base),))
+ raise MathIRError(f"normalizer produced unsupported expression {expression!r}")
+
+
+def _normalize_expr(expression: Expr) -> Expr:
+ symbolic = _expr_to_sympy(expression)
+ normalized = sympy.cancel(symbolic)
+ return _expr_from_sympy(normalized)
+
+
+def _canonical_parts(expression: Expr) -> tuple[str, ...]:
+ if expression.op not in {"add", "mul"}:
+ return (_canonical_expr(expression),)
+ parts: list[str] = []
+ for argument in expression.args:
+ converted = _canonicalized_expr(argument)
+ if converted.op == expression.op:
+ parts.extend(_canonical_parts(converted))
+ else:
+ parts.append(_canonical_expr(converted))
+ return tuple(sorted(parts))
+
+
+def _canonicalized_expr(expression: Expr) -> Expr:
+ if expression.op == "sub":
+ return Expr(
+ "add",
+ (
+ _canonicalized_expr(expression.args[0]),
+ Expr("neg", (_canonicalized_expr(expression.args[1]),)),
+ ),
+ )
+ if expression.op == "div":
+ return Expr(
+ "mul",
+ (
+ _canonicalized_expr(expression.args[0]),
+ Expr("inv", (_canonicalized_expr(expression.args[1]),)),
+ ),
+ )
+ return Expr(
+ expression.op,
+ tuple(_canonicalized_expr(argument) for argument in expression.args),
+ expression.value,
+ )
+
+
+def _canonical_expr(expression: Expr) -> str:
+ expression = _canonicalized_expr(expression)
+ if expression.op == "symbol":
+ assert isinstance(expression.value, str)
+ return expression.value
+ if expression.op == "const":
+ assert isinstance(expression.value, Fraction)
+ if expression.value.denominator == 1:
+ return str(expression.value.numerator)
+ return f"rat({expression.value.numerator},{expression.value.denominator})"
+ if expression.op in {"add", "mul"}:
+ return f"{expression.op}({','.join(_canonical_parts(expression))})"
+ if expression.op in {"neg", "inv"}:
+ return f"{expression.op}({_canonical_expr(expression.args[0])})"
+ raise MathIRError(f"cannot canonicalize {expression.op!r}")
+
+
+def _canonical_state(state: EquationState) -> str:
+ return f"eq({_canonical_expr(state.lhs)},{_canonical_expr(state.rhs)})"
+
+
+def _rename_expr_symbols(
+ expression: Expr,
+ symbol_map: Mapping[str, str],
+) -> Expr:
+ if expression.op == "symbol":
+ assert isinstance(expression.value, str)
+ return Expr(
+ "symbol",
+ value=symbol_map.get(expression.value, expression.value),
+ )
+ return Expr(
+ expression.op,
+ tuple(
+ _rename_expr_symbols(argument, symbol_map)
+ for argument in expression.args
+ ),
+ expression.value,
+ )
+
+
+def _alpha_canonical_route(
+ initial_state: EquationState,
+ commands: tuple[Command, ...],
+) -> str:
+ """Canonicalize a verified route independently of coefficient names.
+
+ At most six coefficient symbols are allowed by the reference schema, so a
+ small exhaustive alpha-renaming is simpler and safer than relying on
+ symbol-name or traversal-order heuristics. Numeric binding values never
+ enter this representation.
+ """
+
+ symbols = sorted(
+ (
+ _expr_symbols(initial_state.lhs)
+ | _expr_symbols(initial_state.rhs)
+ | set().union(
+ *(_expr_symbols(command.argument) for command in commands),
+ set(),
+ )
+ )
+ - {"x"}
+ )
+ roles = tuple(f"c{index}" for index in range(len(symbols)))
+ candidates: list[str] = []
+ for assigned_symbols in permutations(symbols):
+ symbol_map = {
+ symbol: role for symbol, role in zip(assigned_symbols, roles)
+ }
+ renamed_initial = EquationState(
+ _rename_expr_symbols(initial_state.lhs, symbol_map),
+ _rename_expr_symbols(initial_state.rhs, symbol_map),
+ )
+ command_parts = []
+ for command in commands:
+ renamed_argument = _rename_expr_symbols(
+ command.argument,
+ symbol_map,
+ )
+ command_parts.append(
+ f"{command.op}({_canonical_expr(renamed_argument)})"
+ )
+ candidates.append(
+ f"init={_canonical_state(renamed_initial)}"
+ f"|commands={'>'.join(command_parts)}"
+ )
+ if not candidates:
+ candidates.append(
+ f"init={_canonical_state(initial_state)}"
+ f"|commands={'>'.join(command.op for command in commands)}"
+ )
+ return f"mathir-route:{MATHIR_ROUTE_VERSION}:{min(candidates)}"
+
+
+def _validate_denominators(
+ expression: Expr,
+ *,
+ bindings: Mapping[str, Fraction],
+) -> None:
+ if expression.op == "div":
+ denominator = expression.args[1]
+ if "x" in _expr_symbols(denominator):
+ raise MathIRError("x-dependent denominators are not supported")
+ if _eval_fraction(denominator, bindings) == 0:
+ raise MathIRError("division by zero in command expression")
+ for argument in expression.args:
+ _validate_denominators(argument, bindings=bindings)
+
+
+def _eval_fraction(
+ expression: Expr,
+ bindings: Mapping[str, Fraction],
+) -> Fraction:
+ if expression.op == "symbol":
+ assert isinstance(expression.value, str)
+ if expression.value not in bindings:
+ raise MathIRError("cannot evaluate an expression containing x")
+ return bindings[expression.value]
+ if expression.op == "const":
+ assert isinstance(expression.value, Fraction)
+ return expression.value
+ values = tuple(_eval_fraction(argument, bindings) for argument in expression.args)
+ if expression.op == "add":
+ return values[0] + values[1]
+ if expression.op == "sub":
+ return values[0] - values[1]
+ if expression.op == "mul":
+ return values[0] * values[1]
+ if expression.op == "div":
+ if values[1] == 0:
+ raise MathIRError("division by zero")
+ return values[0] / values[1]
+ if expression.op == "neg":
+ return -values[0]
+ if expression.op == "inv":
+ if values[0] == 0:
+ raise MathIRError("division by zero")
+ return Fraction(1, 1) / values[0]
+ raise MathIRError(f"cannot evaluate {expression.op!r}")
+
+
+def _initial_solution(
+ state: EquationState,
+ *,
+ bindings: Mapping[str, Fraction],
+) -> Fraction:
+ x = sympy.Symbol("x")
+ substitutions = {
+ sympy.Symbol(name): sympy.Rational(value.numerator, value.denominator)
+ for name, value in bindings.items()
+ }
+ equation = sympy.cancel(
+ (_expr_to_sympy(state.lhs) - _expr_to_sympy(state.rhs)).subs(substitutions)
+ )
+ numerator, denominator = sympy.together(equation).as_numer_denom()
+ if x in denominator.free_symbols:
+ raise MathIRError("initial equation has an x-dependent denominator")
+ polynomial = sympy.Poly(sympy.expand(numerator), x)
+ if polynomial.degree() != 1:
+ raise MathIRError("initial equation is not uniquely linear")
+ coefficient = polynomial.coeff_monomial(x)
+ constant = polynomial.coeff_monomial(1)
+ if coefficient == 0:
+ raise MathIRError("initial equation has no unique solution")
+ solution = sympy.cancel(-constant / coefficient)
+ if not solution.is_Rational:
+ raise MathIRError("initial solution is not rational")
+ return Fraction(int(solution.p), int(solution.q))
+
+
+def _apply_command(
+ state: EquationState,
+ command: Command,
+ *,
+ bindings: Mapping[str, Fraction],
+) -> EquationState:
+ _validate_denominators(command.argument, bindings=bindings)
+ argument_symbols = _expr_symbols(command.argument)
+ if command.op in {"mul", "div"}:
+ if "x" in argument_symbols:
+ raise MathIRError("multiplication and division by x are not reversible")
+ if _eval_fraction(command.argument, bindings) == 0:
+ raise MathIRError("multiplication and division require a nonzero argument")
+ if command.op == "add":
+ lhs = Expr("add", (state.lhs, command.argument))
+ rhs = Expr("add", (state.rhs, command.argument))
+ elif command.op == "sub":
+ lhs = Expr("sub", (state.lhs, command.argument))
+ rhs = Expr("sub", (state.rhs, command.argument))
+ elif command.op == "mul":
+ lhs = Expr("mul", (state.lhs, command.argument))
+ rhs = Expr("mul", (state.rhs, command.argument))
+ elif command.op == "div":
+ lhs = Expr("div", (state.lhs, command.argument))
+ rhs = Expr("div", (state.rhs, command.argument))
+ else:
+ raise MathIRError(f"unsupported command {command.op!r}")
+ # This exact normalizer is part of the interpreter semantics, rather than
+ # model-authored text which could claim a simplification without doing it.
+ return EquationState(_normalize_expr(lhs), _normalize_expr(rhs))
+
+
+def _validated_reference(
+ spec: Mapping[str, Any],
+) -> tuple[EquationState, dict[str, Fraction], int]:
+ if spec.get("verifier") != MATHIR_VERIFIER:
+ raise MathIRError("wrong verifier")
+ if spec.get("mathir_version") != MATHIR_VERSION:
+ raise MathIRError("unsupported MathIR version")
+ raw_bindings = spec.get("bindings")
+ if not isinstance(raw_bindings, dict):
+ raise MathIRError("missing bindings")
+ if not 1 <= len(raw_bindings) <= _MAX_REFERENCE_SYMBOLS:
+ raise MathIRError("invalid number of bindings")
+ bindings: dict[str, Fraction] = {}
+ for raw_name, raw_value in raw_bindings.items():
+ name = str(raw_name)
+ if not re.fullmatch(r"[a-wyz]", name) or name == "x":
+ raise MathIRError("binding names must be single lowercase coefficient symbols")
+ bindings[name] = _fraction_from_reference(raw_value)
+ if len(bindings) != len(raw_bindings):
+ raise MathIRError("duplicate binding names")
+ max_steps = int(spec.get("max_steps", _MAX_PROGRAM_STEPS))
+ if not 1 <= max_steps <= _MAX_PROGRAM_STEPS:
+ raise MathIRError("invalid max_steps")
+ allowed_symbols = frozenset(bindings) | {"x"}
+ lhs = _parse_trusted_expression(
+ str(spec["initial_lhs"]),
+ allowed_symbols=allowed_symbols,
+ )
+ rhs = _parse_trusted_expression(
+ str(spec["initial_rhs"]),
+ allowed_symbols=allowed_symbols,
+ )
+ referenced_coefficients = (_expr_symbols(lhs) | _expr_symbols(rhs)) - {"x"}
+ if referenced_coefficients != set(bindings):
+ raise MathIRError("bindings and initial equation symbols disagree")
+ state = EquationState(_normalize_expr(lhs), _normalize_expr(rhs))
+ _initial_solution(state, bindings=bindings)
+ return state, bindings, max_steps
+
+
+def _execute_mathir_commands(
+ *,
+ initial_state: EquationState,
+ bindings: Mapping[str, Fraction],
+ commands: tuple[Command, ...],
+ key_version: str,
+ action_ids: tuple[str, ...] = (),
+) -> MathIRValidation:
+ target_solution = _initial_solution(initial_state, bindings=bindings)
+ seen = {_canonical_state(initial_state)}
+ states: list[EquationState] = []
+ state = initial_state
+ for command in commands:
+ state = _apply_command(state, command, bindings=bindings)
+ state_key = _canonical_state(state)
+ if state_key in seen:
+ raise MathIRError("program revisits a previous equation state")
+ seen.add(state_key)
+ states.append(state)
+
+ if state.lhs == Expr("symbol", value="x"):
+ final_expression = state.rhs
+ elif state.rhs == Expr("symbol", value="x"):
+ final_expression = state.lhs
+ else:
+ raise MathIRError("program does not finish with x isolated")
+ if "x" in _expr_symbols(final_expression):
+ raise MathIRError("final expression still contains x")
+ solution = _eval_fraction(final_expression, bindings)
+ if solution != target_solution:
+ raise MathIRError("executed program has the wrong solution")
+ canonical_key = (
+ f"mathir:{key_version}:"
+ + ">".join(_canonical_state(executed_state) for executed_state in states)
+ )
+ route_signature = _alpha_canonical_route(initial_state, commands)
+ return MathIRValidation(
+ canonical_key=canonical_key,
+ solution=solution,
+ commands=commands,
+ states=tuple(states),
+ action_ids=action_ids,
+ route_signature=route_signature,
+ )
+
+
+def validate_mathir_algebra(
+ program_text: str,
+ spec: Mapping[str, Any],
+) -> MathIRValidation | None:
+ """Execute and validate a MathIR program, returning its canonical path.
+
+ All failures return ``None``. This function is the single admission
+ boundary used by both task reward and the online canonical bank.
+ """
+
+ try:
+ initial_state, bindings, max_steps = _validated_reference(spec)
+ allowed_symbols = frozenset(bindings) | {"x"}
+ commands = parse_mathir_program(
+ program_text,
+ allowed_symbols=allowed_symbols,
+ max_steps=max_steps,
+ )
+ return _execute_mathir_commands(
+ initial_state=initial_state,
+ bindings=bindings,
+ commands=commands,
+ key_version=MATHIR_VERSION,
+ )
+ except Exception:
+ return None
+
+
+def _validated_menu_reference(
+ spec: Mapping[str, Any],
+) -> tuple[
+ EquationState,
+ dict[str, Fraction],
+ int,
+ dict[str, Command],
+]:
+ if spec.get("verifier") != MATHIR_MENU_VERIFIER:
+ raise MathIRError("wrong menu verifier")
+ if spec.get("mathir_version") != MATHIR_MENU_VERSION:
+ raise MathIRError("unsupported menu MathIR version")
+ base_spec = dict(spec)
+ base_spec["verifier"] = MATHIR_VERIFIER
+ base_spec["mathir_version"] = MATHIR_VERSION
+ initial_state, bindings, max_steps = _validated_reference(base_spec)
+ raw_actions = spec.get("actions")
+ if not isinstance(raw_actions, dict):
+ raise MathIRError("missing action menu")
+ if not 2 <= len(raw_actions) <= _MAX_MENU_ACTIONS:
+ raise MathIRError("invalid action menu size")
+ expected_ids = [chr(ord("A") + index) for index in range(len(raw_actions))]
+ if list(raw_actions) != expected_ids:
+ raise MathIRError("action IDs must be contiguous and ordered")
+ allowed_symbols = frozenset(bindings) | {"x"}
+ actions: dict[str, Command] = {}
+ normalized_programs: set[str] = set()
+ for action_id, raw_program in raw_actions.items():
+ if _MENU_ACTION_RE.fullmatch(str(action_id)) is None:
+ raise MathIRError("invalid action ID")
+ program = re.sub(r"\s+", "", str(raw_program))
+ if program in normalized_programs:
+ raise MathIRError("duplicate action semantics")
+ parsed = parse_mathir_program(
+ program,
+ allowed_symbols=allowed_symbols,
+ max_steps=1,
+ )
+ if len(parsed) != 1:
+ raise MathIRError("each action must contain exactly one command")
+ normalized_programs.add(program)
+ actions[str(action_id)] = parsed[0]
+ return initial_state, bindings, max_steps, actions
+
+
+def parse_mathir_action_program(
+ text: str,
+ *,
+ action_ids: Iterable[str],
+ max_steps: int,
+) -> tuple[str, ...]:
+ """Parse a bounded sequence of prompt-local action IDs."""
+
+ compact = re.sub(r"\s+", "", str(text))
+ if not compact or len(compact) > _MAX_PROGRAM_CHARS:
+ raise MathIRError("action program is empty or too long")
+ compact = compact[:-1] if compact.endswith(";") else compact
+ if not compact or compact.startswith(";") or ";;" in compact:
+ raise MathIRError("empty action")
+ selected = tuple(compact.split(";"))
+ if not 1 <= len(selected) <= int(max_steps):
+ raise MathIRError("action program has an invalid number of steps")
+ allowed = frozenset(str(action_id) for action_id in action_ids)
+ if any(
+ _MENU_ACTION_RE.fullmatch(action_id) is None or action_id not in allowed
+ for action_id in selected
+ ):
+ raise MathIRError("unknown action ID")
+ return selected
+
+
+def validate_mathir_action_menu(
+ program_text: str,
+ spec: Mapping[str, Any],
+) -> MathIRValidation | None:
+ """Execute the exact prompt-local action sequence and key its state path."""
+
+ try:
+ initial_state, bindings, max_steps, actions = _validated_menu_reference(spec)
+ action_ids = parse_mathir_action_program(
+ program_text,
+ action_ids=actions,
+ max_steps=max_steps,
+ )
+ commands = tuple(actions[action_id] for action_id in action_ids)
+ return _execute_mathir_commands(
+ initial_state=initial_state,
+ bindings=bindings,
+ commands=commands,
+ key_version=MATHIR_MENU_VERSION,
+ action_ids=action_ids,
+ )
+ except Exception:
+ return None
+
+
+def enumerate_mathir_action_menu_keys(
+ spec: Mapping[str, Any],
+) -> set[str]:
+ """Exhaustively enumerate the bounded menu's distinct verified state paths."""
+
+ return {
+ validation.canonical_key
+ for validation in enumerate_mathir_action_menu_validations(spec)
+ }
+
+
+def _terminal_solution(
+ state: EquationState,
+ *,
+ bindings: Mapping[str, Fraction],
+ target_solution: Fraction,
+) -> Fraction | None:
+ if state.lhs == Expr("symbol", value="x"):
+ final_expression = state.rhs
+ elif state.rhs == Expr("symbol", value="x"):
+ final_expression = state.lhs
+ else:
+ return None
+ if "x" in _expr_symbols(final_expression):
+ return None
+ solution = _eval_fraction(final_expression, bindings)
+ return solution if solution == target_solution else None
+
+
+def enumerate_mathir_action_menu_validations(
+ spec: Mapping[str, Any],
+) -> tuple[MathIRValidation, ...]:
+ """Enumerate exact support while caching deterministic state transitions."""
+
+ initial_state, bindings, max_steps, actions = _validated_menu_reference(spec)
+ target_solution = _initial_solution(initial_state, bindings=bindings)
+ transition_cache: dict[
+ tuple[str, str], tuple[EquationState, str] | None
+ ] = {}
+ admitted: dict[str, MathIRValidation] = {}
+
+ def transition(
+ state: EquationState,
+ action_id: str,
+ ) -> tuple[EquationState, str] | None:
+ state_key = _canonical_state(state)
+ cache_key = (state_key, action_id)
+ if cache_key not in transition_cache:
+ try:
+ next_state = _apply_command(
+ state,
+ actions[action_id],
+ bindings=bindings,
+ )
+ transition_cache[cache_key] = (
+ next_state,
+ _canonical_state(next_state),
+ )
+ except Exception:
+ transition_cache[cache_key] = None
+ return transition_cache[cache_key]
+
+ def visit(
+ state: EquationState,
+ *,
+ seen: frozenset[str],
+ commands: tuple[Command, ...],
+ action_ids: tuple[str, ...],
+ states: tuple[EquationState, ...],
+ ) -> None:
+ if len(commands) >= max_steps:
+ return
+ for action_id in actions:
+ result = transition(state, action_id)
+ if result is None:
+ continue
+ next_state, next_state_key = result
+ if next_state_key in seen:
+ continue
+ next_commands = commands + (actions[action_id],)
+ next_action_ids = action_ids + (action_id,)
+ next_states = states + (next_state,)
+ solution = _terminal_solution(
+ next_state,
+ bindings=bindings,
+ target_solution=target_solution,
+ )
+ if solution is not None:
+ canonical_key = (
+ f"mathir:{MATHIR_MENU_VERSION}:"
+ + ">".join(
+ _canonical_state(executed_state)
+ for executed_state in next_states
+ )
+ )
+ admitted[canonical_key] = MathIRValidation(
+ canonical_key=canonical_key,
+ solution=solution,
+ commands=next_commands,
+ states=next_states,
+ action_ids=next_action_ids,
+ route_signature=_alpha_canonical_route(
+ initial_state,
+ next_commands,
+ ),
+ )
+ visit(
+ next_state,
+ seen=seen | {next_state_key},
+ commands=next_commands,
+ action_ids=next_action_ids,
+ states=next_states,
+ )
+
+ initial_key = _canonical_state(initial_state)
+ visit(
+ initial_state,
+ seen=frozenset({initial_key}),
+ commands=(),
+ action_ids=(),
+ states=(),
+ )
+ return tuple(admitted[key] for key in sorted(admitted))
+
+
+def enumerate_mathir_action_menu_route_signatures(
+ spec: Mapping[str, Any],
+) -> set[str]:
+ """Exhaustively enumerate the menu's verified cross-prompt route support."""
+
+ return {
+ validation.route_signature
+ for validation in enumerate_mathir_action_menu_validations(spec)
+ }
+
+
+def certified_mathir_strategy_keys(
+ spec: Mapping[str, Any],
+ programs: Iterable[str],
+) -> set[str]:
+ """Validate a finite audit list without treating it as exhaustive support."""
+
+ keys: set[str] = set()
+ for program in programs:
+ validation = validate_mathir_algebra(program, spec)
+ if validation is None:
+ raise MathIRError(f"certified program failed validation: {program}")
+ keys.add(validation.canonical_key)
+ return keys
+
+
+def mathir_command_histogram(validation: MathIRValidation) -> Counter[str]:
+ """Small diagnostic helper used by audits and tests."""
+
+ return Counter(command.op for command in validation.commands)
diff --git a/code/oat_drgrpo/maze_modebench.py b/code/oat_drgrpo/maze_modebench.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d0b73feb7aca3393ef72cd0c8efd86e17e53def
--- /dev/null
+++ b/code/oat_drgrpo/maze_modebench.py
@@ -0,0 +1,517 @@
+"""Verified language action programs for PointMaze and AntMaze.
+
+This module is deliberately simulator-independent. It validates the trusted
+episode specification, parses the LM's finite action alphabet, and derives a
+topological route key from a hash-bound successful execution record. MuJoCo
+remains outside the trainer process; its worker must return the trajectory and
+environment hashes consumed here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import json
+import math
+import re
+from typing import Any, Mapping, Sequence
+
+
+POINT_MAZE_VERIFIER = "point_maze_action_program"
+ANT_MAZE_VERIFIER = "ant_maze_action_program"
+MAZE_ACTION_VERSION = "maze-action-v1"
+ANT_MAZE_ACTION_VERSION = "maze-action-ant-v5"
+
+POINT_ACTIONS = ("N", "NE", "E", "SE", "S", "SW", "W", "NW", "COAST")
+ANT_ACTIONS = ("N", "NE", "E", "SE", "S", "SW", "W", "NW")
+
+_SHA256 = re.compile(r"[0-9a-f]{64}")
+_IDENTIFIER = re.compile(r"[A-Za-z0-9][A-Za-z0-9_.-]{0,95}")
+_GATE_ID = re.compile(r"[a-z][a-z0-9_-]{0,31}")
+_MAX_PROGRAM_CHARS = 4096
+
+
+class MazeModeBenchError(ValueError):
+ """Raised when a trusted maze spec or execution record is invalid."""
+
+
+@dataclass(frozen=True)
+class DirectedGate:
+ gate_id: str
+ axis: str
+ coordinate: float
+ span_min: float
+ span_max: float
+ hysteresis: float
+
+
+@dataclass(frozen=True)
+class MazeActionSpec:
+ verifier: str
+ environment_id: str
+ environment_sha256: str
+ controller_sha256: str | None
+ map_id: str
+ maze_map: tuple[tuple[int, ...], ...]
+ reset_cell: tuple[int, int]
+ goal_cell: tuple[int, int]
+ reset_seed: int
+ min_actions: int
+ max_actions: int
+ action_repeat: int
+ action_tokens: tuple[str, ...]
+ success_threshold: float
+ max_segment_length: float
+ bounds: tuple[tuple[float, float], tuple[float, float]]
+ gates: tuple[DirectedGate, ...]
+ spec_sha256: str
+
+
+@dataclass(frozen=True)
+class MazeExecutionRecord:
+ environment_sha256: str
+ controller_sha256: str | None
+ spec_sha256: str
+ reset_seed: int
+ action_tokens: tuple[str, ...]
+ success: bool
+ final_goal_distance: float
+ trajectory_xy: tuple[tuple[float, float], ...]
+ simulator_steps: int
+
+
+@dataclass(frozen=True)
+class MazeValidation:
+ canonical_key: str
+ directed_gates: tuple[str, ...]
+ action_tokens: tuple[str, ...]
+ simulator_steps: int
+
+
+def _finite_float(value: Any, *, label: str) -> float:
+ if isinstance(value, bool) or not isinstance(value, (int, float)):
+ raise MazeModeBenchError(f"{label} must be a finite number")
+ result = float(value)
+ if not math.isfinite(result):
+ raise MazeModeBenchError(f"{label} must be a finite number")
+ return result
+
+
+def _bounded_int(value: Any, *, label: str, minimum: int, maximum: int) -> int:
+ if isinstance(value, bool) or not isinstance(value, int):
+ raise MazeModeBenchError(f"{label} must be an integer")
+ result = int(value)
+ if not minimum <= result <= maximum:
+ raise MazeModeBenchError(f"{label} is outside [{minimum}, {maximum}]")
+ return result
+
+
+def _sha(value: Any, *, label: str) -> str:
+ result = str(value)
+ if _SHA256.fullmatch(result) is None:
+ raise MazeModeBenchError(f"{label} must be a lowercase SHA-256")
+ return result
+
+
+def _canonical_spec_sha256(spec: Mapping[str, Any]) -> str:
+ payload = dict(spec)
+ claimed = payload.pop("spec_sha256", None)
+ canonical = json.dumps(
+ payload,
+ allow_nan=False,
+ ensure_ascii=True,
+ separators=(",", ":"),
+ sort_keys=True,
+ ).encode("ascii")
+ observed = hashlib.sha256(canonical).hexdigest()
+ if claimed is not None and claimed != observed:
+ raise MazeModeBenchError("spec_sha256 does not match the canonical spec")
+ return observed
+
+
+def _parse_bounds(value: Any) -> tuple[tuple[float, float], tuple[float, float]]:
+ if not isinstance(value, list) or len(value) != 2:
+ raise MazeModeBenchError("bounds_xy must contain x and y bounds")
+ parsed: list[tuple[float, float]] = []
+ for index, raw in enumerate(value):
+ if not isinstance(raw, list) or len(raw) != 2:
+ raise MazeModeBenchError(f"bounds_xy[{index}] must contain two values")
+ low = _finite_float(raw[0], label=f"bounds_xy[{index}][0]")
+ high = _finite_float(raw[1], label=f"bounds_xy[{index}][1]")
+ if low >= high:
+ raise MazeModeBenchError("each coordinate bound must be increasing")
+ parsed.append((low, high))
+ return parsed[0], parsed[1]
+
+
+def _parse_gates(value: Any) -> tuple[DirectedGate, ...]:
+ if not isinstance(value, list) or not 1 <= len(value) <= 32:
+ raise MazeModeBenchError("route_gates must contain between 1 and 32 gates")
+ gates: list[DirectedGate] = []
+ observed: set[str] = set()
+ for index, raw in enumerate(value):
+ if not isinstance(raw, Mapping):
+ raise MazeModeBenchError(f"route gate {index} must be an object")
+ gate_id = str(raw.get("id", ""))
+ if _GATE_ID.fullmatch(gate_id) is None:
+ raise MazeModeBenchError(f"route gate {index} has an invalid id")
+ if gate_id in observed:
+ raise MazeModeBenchError(f"duplicate route gate id {gate_id!r}")
+ observed.add(gate_id)
+ axis = str(raw.get("axis", ""))
+ if axis not in {"x", "y"}:
+ raise MazeModeBenchError(f"route gate {gate_id!r} has invalid axis")
+ coordinate = _finite_float(raw.get("coordinate"), label=f"{gate_id}.coordinate")
+ raw_span = raw.get("span")
+ if not isinstance(raw_span, list) or len(raw_span) != 2:
+ raise MazeModeBenchError(f"route gate {gate_id!r} needs a span")
+ span_min = _finite_float(raw_span[0], label=f"{gate_id}.span[0]")
+ span_max = _finite_float(raw_span[1], label=f"{gate_id}.span[1]")
+ if span_min >= span_max:
+ raise MazeModeBenchError(f"route gate {gate_id!r} span is not increasing")
+ hysteresis = _finite_float(raw.get("hysteresis"), label=f"{gate_id}.hysteresis")
+ if not 0.0 < hysteresis <= 0.25:
+ raise MazeModeBenchError(f"route gate {gate_id!r} hysteresis is invalid")
+ gates.append(
+ DirectedGate(
+ gate_id=gate_id,
+ axis=axis,
+ coordinate=coordinate,
+ span_min=span_min,
+ span_max=span_max,
+ hysteresis=hysteresis,
+ )
+ )
+ return tuple(gates)
+
+
+def _parse_maze_map(
+ value: Any,
+ reset_cell_value: Any,
+ goal_cell_value: Any,
+) -> tuple[tuple[tuple[int, ...], ...], tuple[int, int], tuple[int, int]]:
+ if not isinstance(value, list) or not 5 <= len(value) <= 16:
+ raise MazeModeBenchError("maze_map must contain between 5 and 16 rows")
+ width = len(value[0]) if isinstance(value[0], list) else 0
+ if not 5 <= width <= 16:
+ raise MazeModeBenchError("maze_map must contain between 5 and 16 columns")
+ rows: list[tuple[int, ...]] = []
+ for index, raw_row in enumerate(value):
+ if (
+ not isinstance(raw_row, list)
+ or len(raw_row) != width
+ or any(isinstance(cell, bool) or cell not in {0, 1} for cell in raw_row)
+ ):
+ raise MazeModeBenchError(f"maze_map row {index} is malformed")
+ rows.append(tuple(int(cell) for cell in raw_row))
+ if any(cell != 1 for cell in rows[0] + rows[-1]):
+ raise MazeModeBenchError("maze_map top and bottom borders must be walls")
+ if any(row[0] != 1 or row[-1] != 1 for row in rows):
+ raise MazeModeBenchError("maze_map left and right borders must be walls")
+
+ def parse_cell(raw: Any, label: str) -> tuple[int, int]:
+ if (
+ not isinstance(raw, list)
+ or len(raw) != 2
+ or any(isinstance(item, bool) or not isinstance(item, int) for item in raw)
+ ):
+ raise MazeModeBenchError(f"{label} must be a [row, column] integer pair")
+ cell = int(raw[0]), int(raw[1])
+ if not (0 <= cell[0] < len(rows) and 0 <= cell[1] < width):
+ raise MazeModeBenchError(f"{label} is outside maze_map")
+ if rows[cell[0]][cell[1]] != 0:
+ raise MazeModeBenchError(f"{label} must select a free maze cell")
+ return cell
+
+ reset_cell = parse_cell(reset_cell_value, "reset_cell")
+ goal_cell = parse_cell(goal_cell_value, "goal_cell")
+ if reset_cell == goal_cell:
+ raise MazeModeBenchError("reset_cell and goal_cell must differ")
+ return tuple(rows), reset_cell, goal_cell
+
+
+def parse_maze_action_spec(spec: Mapping[str, Any]) -> MazeActionSpec:
+ """Parse one trusted PointMaze or AntMaze action-program specification."""
+
+ if not isinstance(spec, Mapping):
+ raise MazeModeBenchError("maze spec must be an object")
+ verifier = str(spec.get("verifier", ""))
+ if verifier not in {POINT_MAZE_VERIFIER, ANT_MAZE_VERIFIER}:
+ raise MazeModeBenchError("wrong maze action-program verifier")
+ expected_version = (
+ MAZE_ACTION_VERSION
+ if verifier == POINT_MAZE_VERIFIER
+ else ANT_MAZE_ACTION_VERSION
+ )
+ if spec.get("maze_action_version") != expected_version:
+ raise MazeModeBenchError("unsupported maze action-program version")
+ environment_id = str(spec.get("environment_id", ""))
+ if _IDENTIFIER.fullmatch(environment_id) is None:
+ raise MazeModeBenchError("environment_id is invalid")
+ expected_prefix = "PointMaze" if verifier == POINT_MAZE_VERIFIER else "AntMaze"
+ if not environment_id.startswith(expected_prefix):
+ raise MazeModeBenchError("environment_id does not match the verifier")
+ map_id = str(spec.get("map_id", ""))
+ if _IDENTIFIER.fullmatch(map_id) is None:
+ raise MazeModeBenchError("map_id is invalid")
+
+ expected_actions = POINT_ACTIONS if verifier == POINT_MAZE_VERIFIER else ANT_ACTIONS
+ raw_actions = spec.get("action_tokens")
+ if not isinstance(raw_actions, list) or tuple(raw_actions) != expected_actions:
+ raise MazeModeBenchError("action_tokens differ from the frozen verifier alphabet")
+
+ environment_sha256 = _sha(spec.get("environment_sha256"), label="environment_sha256")
+ raw_controller = spec.get("controller_sha256")
+ if verifier == ANT_MAZE_VERIFIER:
+ controller_sha256 = _sha(raw_controller, label="controller_sha256")
+ else:
+ if raw_controller is not None:
+ raise MazeModeBenchError("PointMaze cannot declare a locomotion controller")
+ controller_sha256 = None
+
+ min_actions = _bounded_int(
+ spec.get("min_actions"), label="min_actions", minimum=1, maximum=512
+ )
+ max_actions = _bounded_int(
+ spec.get("max_actions"), label="max_actions", minimum=min_actions, maximum=512
+ )
+ action_repeat = _bounded_int(
+ spec.get("action_repeat"),
+ label="action_repeat",
+ minimum=1,
+ maximum=100 if verifier == POINT_MAZE_VERIFIER else 400,
+ )
+ success_threshold = _finite_float(
+ spec.get("success_threshold"), label="success_threshold"
+ )
+ if not 0.0 < success_threshold <= 1.0:
+ raise MazeModeBenchError("success_threshold is outside (0, 1]")
+ max_segment_length = _finite_float(
+ spec.get("max_segment_length"), label="max_segment_length"
+ )
+ if not 0.0 < max_segment_length <= 10.0:
+ raise MazeModeBenchError("max_segment_length is outside (0, 10]")
+
+ maze_map, reset_cell, goal_cell = _parse_maze_map(
+ spec.get("maze_map"),
+ spec.get("reset_cell"),
+ spec.get("goal_cell"),
+ )
+
+ return MazeActionSpec(
+ verifier=verifier,
+ environment_id=environment_id,
+ environment_sha256=environment_sha256,
+ controller_sha256=controller_sha256,
+ map_id=map_id,
+ maze_map=maze_map,
+ reset_cell=reset_cell,
+ goal_cell=goal_cell,
+ reset_seed=_bounded_int(
+ spec.get("reset_seed"), label="reset_seed", minimum=0, maximum=2**31 - 1
+ ),
+ min_actions=min_actions,
+ max_actions=max_actions,
+ action_repeat=action_repeat,
+ action_tokens=expected_actions,
+ success_threshold=success_threshold,
+ max_segment_length=max_segment_length,
+ bounds=_parse_bounds(spec.get("bounds_xy")),
+ gates=_parse_gates(spec.get("route_gates")),
+ spec_sha256=_canonical_spec_sha256(spec),
+ )
+
+
+def parse_maze_action_program(candidate: str, spec: MazeActionSpec) -> tuple[str, ...]:
+ """Parse a bounded whitespace/comma-separated action program."""
+
+ text = str(candidate).strip()
+ if not text or len(text) > _MAX_PROGRAM_CHARS:
+ raise MazeModeBenchError("action program is empty or too long")
+ tokens = tuple(token for token in re.split(r"[\s,]+", text.upper()) if token)
+ if not spec.min_actions <= len(tokens) <= spec.max_actions:
+ raise MazeModeBenchError("action program length is outside the frozen bounds")
+ allowed = set(spec.action_tokens)
+ unknown = [token for token in tokens if token not in allowed]
+ if unknown:
+ raise MazeModeBenchError(f"unknown action token {unknown[0]!r}")
+ return tokens
+
+
+def _parse_execution_record(value: Mapping[str, Any]) -> MazeExecutionRecord:
+ if not isinstance(value, Mapping):
+ raise MazeModeBenchError("execution record must be an object")
+ raw_trajectory = value.get("trajectory_xy")
+ if not isinstance(raw_trajectory, list) or len(raw_trajectory) < 2:
+ raise MazeModeBenchError("execution trajectory must contain at least two points")
+ trajectory: list[tuple[float, float]] = []
+ for index, raw_point in enumerate(raw_trajectory):
+ if not isinstance(raw_point, list) or len(raw_point) != 2:
+ raise MazeModeBenchError(f"trajectory point {index} is malformed")
+ trajectory.append(
+ (
+ _finite_float(raw_point[0], label=f"trajectory[{index}].x"),
+ _finite_float(raw_point[1], label=f"trajectory[{index}].y"),
+ )
+ )
+ raw_success = value.get("success")
+ if not isinstance(raw_success, bool):
+ raise MazeModeBenchError("execution success must be boolean")
+ raw_tokens = value.get("action_tokens")
+ if not isinstance(raw_tokens, list) or not all(isinstance(x, str) for x in raw_tokens):
+ raise MazeModeBenchError("execution action_tokens must be a string list")
+ raw_controller = value.get("controller_sha256")
+ controller_sha256 = None if raw_controller is None else _sha(
+ raw_controller, label="execution.controller_sha256"
+ )
+ return MazeExecutionRecord(
+ environment_sha256=_sha(
+ value.get("environment_sha256"), label="execution.environment_sha256"
+ ),
+ controller_sha256=controller_sha256,
+ spec_sha256=_sha(value.get("spec_sha256"), label="execution.spec_sha256"),
+ reset_seed=_bounded_int(
+ value.get("reset_seed"),
+ label="execution.reset_seed",
+ minimum=0,
+ maximum=2**31 - 1,
+ ),
+ action_tokens=tuple(raw_tokens),
+ success=raw_success,
+ final_goal_distance=_finite_float(
+ value.get("final_goal_distance"), label="execution.final_goal_distance"
+ ),
+ trajectory_xy=tuple(trajectory),
+ simulator_steps=_bounded_int(
+ value.get("simulator_steps"),
+ label="execution.simulator_steps",
+ minimum=1,
+ maximum=1_000_000,
+ ),
+ )
+
+
+def _outside_side(value: float, gate: DirectedGate) -> int:
+ if value < gate.coordinate - gate.hysteresis:
+ return -1
+ if value > gate.coordinate + gate.hysteresis:
+ return 1
+ return 0
+
+
+def _gate_crossing(
+ first: tuple[float, float],
+ second: tuple[float, float],
+ gate: DirectedGate,
+) -> str | None:
+ axis_index = 0 if gate.axis == "x" else 1
+ span_index = 1 - axis_index
+ denominator = second[axis_index] - first[axis_index]
+ if denominator == 0.0:
+ return None
+ fraction = (gate.coordinate - first[axis_index]) / denominator
+ if not 0.0 <= fraction <= 1.0:
+ return None
+ span_value = first[span_index] + fraction * (
+ second[span_index] - first[span_index]
+ )
+ if not gate.span_min <= span_value <= gate.span_max:
+ return None
+ direction = "+" if second[axis_index] > first[axis_index] else "-"
+ return f"{gate.gate_id}{direction}"
+
+
+def extract_directed_gate_route(
+ trajectory_xy: Sequence[tuple[float, float]],
+ spec: MazeActionSpec,
+) -> tuple[str, ...]:
+ """Extract a hysteresis-stable ordered directed bottleneck sequence."""
+
+ if len(trajectory_xy) < 2:
+ raise MazeModeBenchError("trajectory is too short")
+ if len(trajectory_xy) > spec.max_actions * spec.action_repeat + 2:
+ raise MazeModeBenchError("trajectory exceeds the frozen execution horizon")
+ for index, point in enumerate(trajectory_xy):
+ if not (
+ spec.bounds[0][0] <= point[0] <= spec.bounds[0][1]
+ and spec.bounds[1][0] <= point[1] <= spec.bounds[1][1]
+ ):
+ raise MazeModeBenchError(f"trajectory point {index} is outside maze bounds")
+ if index:
+ previous = trajectory_xy[index - 1]
+ distance = math.hypot(point[0] - previous[0], point[1] - previous[1])
+ if distance > spec.max_segment_length:
+ raise MazeModeBenchError("trajectory contains a teleport-sized segment")
+
+ route: list[str] = []
+ state: dict[str, tuple[int, tuple[float, float]] | None] = {
+ gate.gate_id: None for gate in spec.gates
+ }
+ for point in trajectory_xy:
+ step_crossings: list[str] = []
+ for gate in spec.gates:
+ coordinate = point[0] if gate.axis == "x" else point[1]
+ side = _outside_side(coordinate, gate)
+ previous_state = state[gate.gate_id]
+ if side == 0:
+ continue
+ if previous_state is None:
+ state[gate.gate_id] = (side, point)
+ continue
+ previous_side, previous_point = previous_state
+ if side != previous_side:
+ crossing = _gate_crossing(previous_point, point, gate)
+ if crossing is not None:
+ step_crossings.append(crossing)
+ state[gate.gate_id] = (side, point)
+ if len(step_crossings) > 1:
+ raise MazeModeBenchError("one trajectory segment crosses multiple route gates")
+ if step_crossings and (not route or route[-1] != step_crossings[0]):
+ route.append(step_crossings[0])
+ if not route:
+ raise MazeModeBenchError("successful trajectory has no certified route gate")
+ gate_ids = [crossing[:-1] for crossing in route]
+ if len(gate_ids) != len(set(gate_ids)):
+ raise MazeModeBenchError("trajectory recrosses a route gate")
+ return tuple(route)
+
+
+def validate_maze_execution(
+ candidate: str,
+ raw_spec: Mapping[str, Any],
+ raw_execution: Mapping[str, Any],
+) -> MazeValidation:
+ """Bind one successful simulator execution to its semantic route key."""
+
+ spec = parse_maze_action_spec(raw_spec)
+ tokens = parse_maze_action_program(candidate, spec)
+ execution = _parse_execution_record(raw_execution)
+ if execution.environment_sha256 != spec.environment_sha256:
+ raise MazeModeBenchError("execution environment hash does not match the spec")
+ if execution.controller_sha256 != spec.controller_sha256:
+ raise MazeModeBenchError("execution controller hash does not match the spec")
+ if execution.spec_sha256 != spec.spec_sha256:
+ raise MazeModeBenchError("execution is bound to a different spec")
+ if execution.reset_seed != spec.reset_seed:
+ raise MazeModeBenchError("execution reset seed does not match the spec")
+ if execution.action_tokens != tokens:
+ raise MazeModeBenchError("execution action program does not match the candidate")
+ if not execution.success:
+ raise MazeModeBenchError("unsuccessful execution has no ModeBench identity")
+ if not 0.0 <= execution.final_goal_distance <= spec.success_threshold:
+ raise MazeModeBenchError("successful execution fails the frozen goal threshold")
+ if execution.simulator_steps > len(tokens) * spec.action_repeat:
+ raise MazeModeBenchError("execution exceeds the action-program step budget")
+ route = extract_directed_gate_route(execution.trajectory_xy, spec)
+ action_version = (
+ MAZE_ACTION_VERSION
+ if spec.verifier == POINT_MAZE_VERIFIER
+ else ANT_MAZE_ACTION_VERSION
+ )
+ key = f"{spec.verifier}:{action_version}:{spec.map_id}:" + ",".join(route)
+ return MazeValidation(
+ canonical_key=key,
+ directed_gates=route,
+ action_tokens=tokens,
+ simulator_steps=execution.simulator_steps,
+ )
diff --git a/code/oat_drgrpo/maze_modebench_process.py b/code/oat_drgrpo/maze_modebench_process.py
new file mode 100644
index 0000000000000000000000000000000000000000..761c637e121198e41e9bc33bbb1aaf81f523d136
--- /dev/null
+++ b/code/oat_drgrpo/maze_modebench_process.py
@@ -0,0 +1,168 @@
+"""Killable external MuJoCo boundary for maze language-action programs."""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+import select
+import subprocess
+import threading
+from typing import Any, Mapping
+
+from .maze_modebench import MazeValidation
+
+
+class MazeVerifierProcess:
+ """Execute maze action programs outside the trainer process."""
+
+ def __init__(
+ self,
+ *,
+ timeout_seconds: float = 25.0,
+ worker_python: str | Path | None = None,
+ ) -> None:
+ if timeout_seconds <= 0:
+ raise ValueError("timeout_seconds must be positive")
+ root = Path(
+ os.environ.get(
+ "OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[2]
+ )
+ ).resolve()
+ configured = worker_python or os.environ.get("OAT_ZERO_MAZE_WORKER_PYTHON")
+ self.worker_python = Path(configured) if configured else (
+ root / "var/maze_runtime/venv/bin/python"
+ )
+ self.timeout_seconds = float(timeout_seconds)
+ self._process: subprocess.Popen[str] | None = None
+ self._owner_pid = os.getpid()
+ self._lock = threading.Lock()
+
+ def _start(self) -> subprocess.Popen[str]:
+ if self._owner_pid != os.getpid():
+ self._process = None
+ self._owner_pid = os.getpid()
+ process = self._process
+ if process is not None and process.poll() is None:
+ return process
+ if not self.worker_python.is_file():
+ raise FileNotFoundError(f"maze worker Python is missing: {self.worker_python}")
+ source_root = str(Path(__file__).resolve().parents[1])
+ entrypoint = (
+ "import runpy,sys;"
+ f"sys.path.insert(0,{source_root!r});"
+ "runpy.run_module('oat_drgrpo.maze_modebench_worker',run_name='__main__')"
+ )
+ worker_env = os.environ.copy()
+ worker_env.setdefault("MUJOCO_GL", "egl")
+ self._process = subprocess.Popen(
+ [str(self.worker_python), "-B", "-I", "-c", entrypoint],
+ stdin=subprocess.PIPE,
+ stdout=subprocess.PIPE,
+ stderr=None,
+ text=True,
+ encoding="utf-8",
+ bufsize=1,
+ start_new_session=True,
+ env=worker_env,
+ )
+ return self._process
+
+ def _stop(self) -> None:
+ process, self._process = self._process, None
+ if process is None:
+ return
+ if process.poll() is None:
+ process.terminate()
+ try:
+ process.wait(timeout=1)
+ except subprocess.TimeoutExpired:
+ process.kill()
+ process.wait(timeout=1)
+ if process.stdin is not None:
+ process.stdin.close()
+ if process.stdout is not None:
+ process.stdout.close()
+
+ def close(self) -> None:
+ with self._lock:
+ self._stop()
+
+ def validate(
+ self,
+ candidate: str,
+ spec: Mapping[str, Any],
+ ) -> MazeValidation | None:
+ result = self.validate_with_execution(candidate, spec)
+ return result[0] if result is not None else None
+
+ def validate_with_execution(
+ self,
+ candidate: str,
+ spec: Mapping[str, Any],
+ ) -> tuple[MazeValidation, dict[str, Any]] | None:
+ """Return validation plus the trusted raw record for offline audits."""
+
+ request = json.dumps(
+ {"candidate": str(candidate), "spec": dict(spec)},
+ allow_nan=False,
+ )
+ with self._lock:
+ for attempt in range(2):
+ try:
+ process = self._start()
+ assert process.stdin is not None and process.stdout is not None
+ process.stdin.write(request + "\n")
+ process.stdin.flush()
+ readable, _, _ = select.select(
+ [process.stdout], [], [], self.timeout_seconds
+ )
+ if not readable:
+ self._stop()
+ return None
+ line = process.stdout.readline()
+ if not line:
+ raise BrokenPipeError("maze worker closed stdout")
+ payload = json.loads(line)
+ if not payload.get("valid"):
+ return None
+ validation = MazeValidation(
+ canonical_key=str(payload["canonical_key"]),
+ directed_gates=tuple(
+ str(value) for value in payload["directed_gates"]
+ ),
+ action_tokens=tuple(
+ str(value) for value in payload["action_tokens"]
+ ),
+ simulator_steps=int(payload["simulator_steps"]),
+ )
+ return validation, dict(payload["execution"])
+ except (
+ BrokenPipeError,
+ FileNotFoundError,
+ OSError,
+ ValueError,
+ json.JSONDecodeError,
+ ):
+ self._stop()
+ if attempt:
+ return None
+ return None
+
+ def __del__(self) -> None:
+ try:
+ self._stop()
+ except Exception:
+ pass
+
+
+_SHARED_VERIFIER = MazeVerifierProcess()
+
+
+def validate_maze_action_program_external(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> MazeValidation | None:
+ """Validate a candidate through the shared pinned MuJoCo worker."""
+
+ return _SHARED_VERIFIER.validate(candidate, spec)
diff --git a/code/oat_drgrpo/maze_modebench_worker.py b/code/oat_drgrpo/maze_modebench_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ca827edab0e61cf99553fa5adfe4e610a75f1b7
--- /dev/null
+++ b/code/oat_drgrpo/maze_modebench_worker.py
@@ -0,0 +1,179 @@
+"""Networkless JSON-lines worker for 0.5B maze action programs."""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import signal
+import sys
+from typing import Any, Mapping
+
+from .maze_modebench import (
+ ANT_MAZE_VERIFIER,
+ POINT_MAZE_VERIFIER,
+ parse_maze_action_program,
+ parse_maze_action_spec,
+ validate_maze_execution,
+)
+from .maze_runtime_identity import maze_runtime_identity
+from .ant_maze_worker_v5 import execute_ant_v5
+from .ant_maze_worker_v5 import RECEIPT_SHA256 as ANT_V5_RECEIPT_SHA256
+from .ant_maze_worker_v8 import execute_ant_v8
+from .ant_maze_worker_v8 import RECEIPT_SHA256 as ANT_V8_RECEIPT_SHA256
+from .ant_maze_worker_v9 import controller_receipt_sha256 as ant_v9_receipt_sha256
+from .ant_maze_worker_v9 import execute_ant_v9
+from .ant_maze_worker_v10 import controller_receipt_sha256 as ant_v10_receipt_sha256
+from .ant_maze_worker_v10 import execute_ant_v10
+from .ant_maze_worker_v11 import controller_receipt_sha256 as ant_v11_receipt_sha256
+from .ant_maze_worker_v11 import execute_ant_v11
+from .ant_maze_worker_v12 import controller_receipt_sha256 as ant_v12_receipt_sha256
+from .ant_maze_worker_v12 import execute_ant_v12
+
+
+WORKER_TIMEOUT_SECONDS = float(
+ os.environ.get("OAT_ZERO_MAZE_WORKER_TIMEOUT_SECONDS", "20.0")
+)
+if not math.isfinite(WORKER_TIMEOUT_SECONDS) or WORKER_TIMEOUT_SECONDS <= 0:
+ raise ValueError("maze worker timeout must be finite and positive")
+
+
+def _timeout(_signum, _frame) -> None:
+ raise TimeoutError("maze action-program execution timed out")
+
+
+def _point_action(token: str):
+ import numpy as np
+
+ diagonal = 1.0 / math.sqrt(2.0)
+ values = {
+ "N": (0.0, 1.0),
+ "NE": (diagonal, diagonal),
+ "E": (1.0, 0.0),
+ "SE": (diagonal, -diagonal),
+ "S": (0.0, -1.0),
+ "SW": (-diagonal, -diagonal),
+ "W": (-1.0, 0.0),
+ "NW": (-diagonal, diagonal),
+ "COAST": (0.0, 0.0),
+ }
+ return np.asarray(values[token], dtype=np.float32)
+
+
+def _execute_point(candidate: str, raw_spec: Mapping[str, Any]):
+ import gymnasium as gym
+ import gymnasium_robotics
+ import numpy as np
+
+ gym.register_envs(gymnasium_robotics)
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier != POINT_MAZE_VERIFIER:
+ raise ValueError("PointMaze executor received a non-PointMaze spec")
+ runtime = maze_runtime_identity()
+ if spec.environment_sha256 != runtime["point_environment_sha256"]:
+ raise ValueError("PointMaze environment hash differs from installed runtime")
+ tokens = parse_maze_action_program(candidate, spec)
+ env = gym.make(
+ spec.environment_id,
+ maze_map=[list(row) for row in spec.maze_map],
+ reward_type="sparse",
+ continuing_task=False,
+ reset_target=False,
+ max_episode_steps=len(tokens) * spec.action_repeat,
+ )
+ try:
+ # Gymnasium-Robotics 1.4.2 forwards this constructor argument to the
+ # nested PointEnv, so bind it on the MazeEnv before reset instead.
+ env.unwrapped.position_noise_range = 0.0
+ observation, info = env.reset(
+ seed=spec.reset_seed,
+ options={
+ "reset_cell": list(spec.reset_cell),
+ "goal_cell": list(spec.goal_cell),
+ },
+ )
+ trajectory = [observation["achieved_goal"].astype(float).tolist()]
+ success = bool(info.get("success", False))
+ simulator_steps = 0
+ for token in tokens:
+ action = _point_action(token)
+ for _ in range(spec.action_repeat):
+ observation, _reward, terminated, truncated, info = env.step(action)
+ simulator_steps += 1
+ achieved = observation["achieved_goal"].astype(float)
+ trajectory.append(achieved.tolist())
+ success = bool(info.get("success", False))
+ if success or terminated or truncated:
+ break
+ if success or terminated or truncated:
+ break
+ distance = float(
+ np.linalg.norm(
+ observation["achieved_goal"] - observation["desired_goal"]
+ )
+ )
+ finally:
+ env.close()
+ execution = {
+ "environment_sha256": spec.environment_sha256,
+ "controller_sha256": None,
+ "spec_sha256": spec.spec_sha256,
+ "reset_seed": spec.reset_seed,
+ "action_tokens": list(tokens),
+ "success": success,
+ "final_goal_distance": distance,
+ "trajectory_xy": trajectory,
+ "simulator_steps": simulator_steps,
+ }
+ return validate_maze_execution(candidate, raw_spec, execution), execution
+
+
+def execute(candidate: str, raw_spec: Mapping[str, Any]):
+ spec = parse_maze_action_spec(raw_spec)
+ if spec.verifier == POINT_MAZE_VERIFIER:
+ return _execute_point(candidate, raw_spec)
+ if spec.verifier == ANT_MAZE_VERIFIER:
+ if spec.controller_sha256 == ANT_V5_RECEIPT_SHA256:
+ return execute_ant_v5(candidate, raw_spec)
+ if spec.controller_sha256 == ANT_V8_RECEIPT_SHA256:
+ return execute_ant_v8(candidate, raw_spec)
+ # V12 route identities are intentionally incompatible with the older
+ # controller-specific route schemas. Resolve the newest bound
+ # controller before asking any older validator to inspect that identity.
+ if spec.controller_sha256 == ant_v12_receipt_sha256():
+ return execute_ant_v12(candidate, raw_spec)
+ if spec.controller_sha256 == ant_v9_receipt_sha256():
+ return execute_ant_v9(candidate, raw_spec)
+ if spec.controller_sha256 == ant_v10_receipt_sha256():
+ return execute_ant_v10(candidate, raw_spec)
+ if spec.controller_sha256 == ant_v11_receipt_sha256():
+ return execute_ant_v11(candidate, raw_spec)
+ raise ValueError("unsupported AntMaze controller identity")
+ raise ValueError("unsupported maze verifier")
+
+
+def main() -> None:
+ signal.signal(signal.SIGALRM, _timeout)
+ for line in sys.stdin:
+ try:
+ request = json.loads(line)
+ signal.setitimer(signal.ITIMER_REAL, WORKER_TIMEOUT_SECONDS)
+ validation, execution = execute(request["candidate"], request["spec"])
+ payload = {
+ "valid": True,
+ "canonical_key": validation.canonical_key,
+ "directed_gates": list(validation.directed_gates),
+ "action_tokens": list(validation.action_tokens),
+ "simulator_steps": validation.simulator_steps,
+ "execution": execution,
+ }
+ except Exception as error:
+ payload = {"valid": False, "error": type(error).__name__}
+ finally:
+ signal.setitimer(signal.ITIMER_REAL, 0.0)
+ sys.stdout.write(json.dumps(payload, allow_nan=False) + "\n")
+ sys.stdout.flush()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/oat_drgrpo/maze_runtime_identity.py b/code/oat_drgrpo/maze_runtime_identity.py
new file mode 100644
index 0000000000000000000000000000000000000000..be2625c3af1d2745bb09afd5a82a7dcba522f4bd
--- /dev/null
+++ b/code/oat_drgrpo/maze_runtime_identity.py
@@ -0,0 +1,87 @@
+"""Hash the exact installed runtime used by maze execution workers."""
+
+from __future__ import annotations
+
+import hashlib
+import importlib.metadata
+import json
+from pathlib import Path
+import platform
+from typing import Any
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def _canonical_sha256(value: Any) -> str:
+ encoded = json.dumps(
+ value,
+ allow_nan=False,
+ ensure_ascii=True,
+ separators=(",", ":"),
+ sort_keys=True,
+ ).encode("ascii")
+ return hashlib.sha256(encoded).hexdigest()
+
+
+def _file_receipt(path: Path, package_root: Path) -> dict[str, str]:
+ return {
+ "path": path.relative_to(package_root.parent).as_posix(),
+ "sha256": _sha256(path),
+ }
+
+
+def maze_runtime_identity() -> dict[str, Any]:
+ """Return source-bound PointMaze and AntMaze environment identities."""
+
+ import gymnasium
+ import gymnasium_robotics
+
+ robotics_root = Path(gymnasium_robotics.__file__).resolve().parent
+ site_packages = robotics_root.parent
+ gymnasium_root = Path(gymnasium.__file__).resolve().parent
+ common = {
+ "python": platform.python_version(),
+ "packages": {
+ package: importlib.metadata.version(package)
+ for package in ("gymnasium", "gymnasium-robotics", "mujoco", "numpy")
+ },
+ "maze_v4": _file_receipt(
+ robotics_root / "envs/maze/maze_v4.py", site_packages
+ ),
+ }
+ point_payload = {
+ "schema_version": "point-maze-runtime-v1",
+ "common": common,
+ "point_maze": _file_receipt(
+ robotics_root / "envs/maze/point_maze.py", site_packages
+ ),
+ "point_xml": _file_receipt(
+ robotics_root / "envs/assets/point/point.xml", site_packages
+ ),
+ }
+ ant_payload = {
+ "schema_version": "ant-maze-runtime-v1",
+ "common": common,
+ "ant_maze": _file_receipt(
+ robotics_root / "envs/maze/ant_maze_v5.py", site_packages
+ ),
+ "ant_v5": _file_receipt(
+ gymnasium_root / "envs/mujoco/ant_v5.py", site_packages
+ ),
+ "ant_xml": _file_receipt(
+ gymnasium_root / "envs/mujoco/assets/ant.xml", site_packages
+ ),
+ }
+ return {
+ "schema_version": "maze-runtime-identity-v1",
+ "point": point_payload,
+ "ant": ant_payload,
+ "point_environment_sha256": _canonical_sha256(point_payload),
+ "ant_environment_sha256": _canonical_sha256(ant_payload),
+ }
diff --git a/code/oat_drgrpo/modebench_guided.py b/code/oat_drgrpo/modebench_guided.py
new file mode 100644
index 0000000000000000000000000000000000000000..913ae043739916add64523d5b524e4539469c690
--- /dev/null
+++ b/code/oat_drgrpo/modebench_guided.py
@@ -0,0 +1,85 @@
+"""Target-blind legal-output grammars for the admitted Level-2 benchmark."""
+
+from __future__ import annotations
+
+import itertools
+import json
+import re
+
+
+PROFILES = frozenset({"none", "domain_legal_v1", "countdown_legal_v3"})
+
+
+def countdown_legal_regex(reference: str) -> str:
+ """Enumerate operand-exact expression syntax without consulting the target."""
+
+ numbers = tuple(str(int(value)) for value in json.loads(reference)["numbers"])
+ operator = r"[+*/-]"
+
+ def trees(values: tuple[str, ...]) -> tuple[str, ...]:
+ if len(values) == 1:
+ return (re.escape(values[0]),)
+ expressions = []
+ for split in range(1, len(values)):
+ for left in trees(values[:split]):
+ for right in trees(values[split:]):
+ expressions.append(r"\(" + left + " " + operator + " " + right + r"\)")
+ return tuple(expressions)
+
+ expressions = set()
+ for values in set(itertools.permutations(numbers)):
+ expressions.update(trees(values))
+ return r"\\boxed\{(?:" + "|".join(sorted(expressions)) + r")\}"
+
+
+def legal_regex(profile: str, domain: str, reference: str) -> str | None:
+ """Return the frozen legal syntax for one row; never inspect its target."""
+
+ if profile not in PROFILES:
+ raise ValueError(f"unknown ModeBench syntax profile: {profile}")
+ if profile == "none":
+ return None
+ if profile == "countdown_legal_v3":
+ if domain != "countdown":
+ raise ValueError("countdown_legal_v3 is Countdown-only")
+ return countdown_legal_regex(reference)
+ if domain == "pantry_plan":
+ spec = json.loads(reference)
+ alternatives = []
+ for ingredient in spec["ingredients"]:
+ identifier = str(ingredient["id"])
+ minimum = int(ingredient["min_if_used_g"])
+ available = int(ingredient["available_g"])
+ step = int(ingredient["step_g"])
+ alternatives.extend(
+ f"{identifier}={grams}"
+ for grams in range(minimum, available + 1, step)
+ )
+ atom = "(?:" + "|".join(re.escape(value) for value in alternatives) + ")"
+ return r"\\boxed\{" + atom + "(?:;" + atom + r"){1,3}\}"
+ fixed = {
+ "python_factors": r"\\boxed\{lambda n: [A-Za-z0-9 _%<>=!+*/().-]+\}",
+ "mathir": r"\\boxed\{[A-F](?:;[A-F]){0,3}\}",
+ }
+ if domain not in fixed:
+ raise ValueError(f"domain_legal_v1 has no grammar for {domain}")
+ return fixed[domain]
+
+
+def guided_sampling_params(base, profile: str, domain: str, references: list[str]):
+ """Copy one vLLM parameter object per row and attach its legal grammar."""
+
+ if profile == "none":
+ return base
+ import copy
+ from vllm.sampling_params import GuidedDecodingParams
+
+ params = []
+ for reference in references:
+ row = copy.copy(base)
+ regex = legal_regex(profile, domain, str(reference))
+ if regex is None:
+ raise RuntimeError("active syntax profile resolved no grammar")
+ row.guided_decoding = GuidedDecodingParams(regex=regex)
+ params.append(row)
+ return params
diff --git a/code/oat_drgrpo/pantry_plan.py b/code/oat_drgrpo/pantry_plan.py
new file mode 100644
index 0000000000000000000000000000000000000000..886b4a79de5cf0e05ab7a7482742c6b7e97c79d3
--- /dev/null
+++ b/code/oat_drgrpo/pantry_plan.py
@@ -0,0 +1,370 @@
+"""Executable nutrition-and-inventory plans for PantryPlan ModeBench.
+
+The model emits a compact allocation such as ``lentils=150;rice=100``. The
+trusted specification supplies a frozen pantry, exact per-100g attributes,
+dietary exclusions, and feasibility bounds. Correctness and semantic identity
+come from the same validation:
+
+* every used ingredient exists, is allowed, and respects its available,
+ minimum-serving, and quantity-step limits;
+* exact decimal arithmetic checks mass, nutrition, cost, and other registered
+ bounds; and
+* the canonical mode is the sorted set of ingredients actually used.
+
+Quantities affect correctness but not identity. This deliberately collapses
+nearby quantity variants of the same formulation instead of treating gram-level
+perturbations as new semantic modes.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from decimal import Decimal, InvalidOperation
+import re
+from typing import Any, Mapping
+
+
+PANTRY_PLAN_VERIFIER = "pantry_plan"
+PANTRY_PLAN_VERSION = "pantry-v1"
+
+_ID = re.compile(r"[a-z][a-z0-9_]{0,31}")
+_ATTRIBUTE = re.compile(r"[a-z][a-z0-9_]{0,31}")
+_TAG = re.compile(r"[a-z][a-z0-9_-]{0,31}")
+_MAX_INGREDIENTS = 16
+_MAX_CANDIDATE_CHARS = 512
+_ALLOWED_ATTRIBUTES = {
+ "energy_kcal",
+ "protein_g",
+ "fiber_g",
+ "fat_g",
+ "carbohydrate_g",
+ "sodium_mg",
+ "cost_cents",
+}
+
+
+class PantryPlanError(ValueError):
+ """Raised for an invalid trusted specification or candidate plan."""
+
+
+@dataclass(frozen=True)
+class PantryIngredient:
+ ingredient_id: str
+ available_g: int
+ step_g: int
+ min_if_used_g: int
+ attributes_per_100g: tuple[tuple[str, Decimal], ...]
+ tags: frozenset[str]
+
+ @property
+ def attributes(self) -> dict[str, Decimal]:
+ return dict(self.attributes_per_100g)
+
+
+@dataclass(frozen=True)
+class PantryTarget:
+ attribute: str
+ minimum: Decimal | None
+ maximum: Decimal | None
+
+
+@dataclass(frozen=True)
+class PantryPlanSpec:
+ ingredients: tuple[PantryIngredient, ...]
+ targets: tuple[PantryTarget, ...]
+ min_ingredients: int
+ max_ingredients: int
+ forbidden_tags: frozenset[str]
+ certified_mode_count: int
+
+ @property
+ def ingredient_by_id(self) -> dict[str, PantryIngredient]:
+ return {row.ingredient_id: row for row in self.ingredients}
+
+
+@dataclass(frozen=True)
+class PantryPlanValidation:
+ """One accepted allocation and its validator-derived semantic mode."""
+
+ canonical_key: str
+ allocations_g: tuple[tuple[str, int], ...]
+ totals: tuple[tuple[str, Decimal], ...]
+
+
+def _decimal(value: Any, *, label: str) -> Decimal:
+ if isinstance(value, bool) or not isinstance(value, (int, float, str, Decimal)):
+ raise PantryPlanError(f"{label} must be a finite decimal")
+ try:
+ result = Decimal(str(value))
+ except (InvalidOperation, ValueError) as error:
+ raise PantryPlanError(f"{label} must be a finite decimal") from error
+ if not result.is_finite() or result < 0:
+ raise PantryPlanError(f"{label} must be a nonnegative finite decimal")
+ return result
+
+
+def _bounded_int(
+ value: Any,
+ *,
+ label: str,
+ minimum: int,
+ maximum: int,
+) -> int:
+ if isinstance(value, bool) or not isinstance(value, int):
+ raise PantryPlanError(f"{label} must be an integer")
+ result = int(value)
+ if not minimum <= result <= maximum:
+ raise PantryPlanError(f"{label} is outside [{minimum}, {maximum}]")
+ return result
+
+
+def _tags(value: Any, *, label: str) -> frozenset[str]:
+ if not isinstance(value, list) or len(value) > 32:
+ raise PantryPlanError(f"{label} must be a bounded tag list")
+ result: set[str] = set()
+ for raw in value:
+ tag = str(raw)
+ if not _TAG.fullmatch(tag):
+ raise PantryPlanError(f"{label} contains invalid tag {tag!r}")
+ if tag in result:
+ raise PantryPlanError(f"{label} contains duplicate tag {tag!r}")
+ result.add(tag)
+ return frozenset(result)
+
+
+def parse_pantry_plan_spec(spec: Mapping[str, Any]) -> PantryPlanSpec:
+ """Validate a trusted PantryPlan specification."""
+
+ if spec.get("verifier") != PANTRY_PLAN_VERIFIER:
+ raise PantryPlanError("wrong PantryPlan verifier")
+ if spec.get("pantry_version") != PANTRY_PLAN_VERSION:
+ raise PantryPlanError("unsupported PantryPlan version")
+
+ raw_ingredients = spec.get("ingredients")
+ if (
+ not isinstance(raw_ingredients, list)
+ or not 2 <= len(raw_ingredients) <= _MAX_INGREDIENTS
+ ):
+ raise PantryPlanError("ingredients must contain between 2 and 16 rows")
+
+ ingredients: list[PantryIngredient] = []
+ observed_ids: set[str] = set()
+ for index, raw in enumerate(raw_ingredients):
+ if not isinstance(raw, Mapping):
+ raise PantryPlanError(f"ingredient {index} must be an object")
+ ingredient_id = str(raw.get("id", ""))
+ if not _ID.fullmatch(ingredient_id):
+ raise PantryPlanError(f"ingredient {index} has an invalid id")
+ if ingredient_id in observed_ids:
+ raise PantryPlanError(f"duplicate ingredient id {ingredient_id!r}")
+ observed_ids.add(ingredient_id)
+
+ available_g = _bounded_int(
+ raw.get("available_g"),
+ label=f"{ingredient_id}.available_g",
+ minimum=1,
+ maximum=10_000,
+ )
+ step_g = _bounded_int(
+ raw.get("step_g"),
+ label=f"{ingredient_id}.step_g",
+ minimum=1,
+ maximum=1_000,
+ )
+ min_if_used_g = _bounded_int(
+ raw.get("min_if_used_g"),
+ label=f"{ingredient_id}.min_if_used_g",
+ minimum=step_g,
+ maximum=available_g,
+ )
+ if available_g % step_g or min_if_used_g % step_g:
+ raise PantryPlanError(
+ f"{ingredient_id} availability and minimum must align to step_g"
+ )
+
+ raw_attributes = raw.get("attributes_per_100g")
+ if not isinstance(raw_attributes, Mapping) or not raw_attributes:
+ raise PantryPlanError(
+ f"{ingredient_id}.attributes_per_100g must be nonempty"
+ )
+ attributes: list[tuple[str, Decimal]] = []
+ for raw_name, raw_value in raw_attributes.items():
+ name = str(raw_name)
+ if (
+ not _ATTRIBUTE.fullmatch(name)
+ or name not in _ALLOWED_ATTRIBUTES
+ ):
+ raise PantryPlanError(
+ f"{ingredient_id} has unsupported attribute {name!r}"
+ )
+ attributes.append(
+ (
+ name,
+ _decimal(
+ raw_value,
+ label=f"{ingredient_id}.attributes_per_100g.{name}",
+ ),
+ )
+ )
+ ingredients.append(
+ PantryIngredient(
+ ingredient_id=ingredient_id,
+ available_g=available_g,
+ step_g=step_g,
+ min_if_used_g=min_if_used_g,
+ attributes_per_100g=tuple(sorted(attributes)),
+ tags=_tags(raw.get("tags", []), label=f"{ingredient_id}.tags"),
+ )
+ )
+
+ min_ingredients = _bounded_int(
+ spec.get("min_ingredients"),
+ label="min_ingredients",
+ minimum=1,
+ maximum=len(ingredients),
+ )
+ max_ingredients = _bounded_int(
+ spec.get("max_ingredients"),
+ label="max_ingredients",
+ minimum=min_ingredients,
+ maximum=len(ingredients),
+ )
+ certified_mode_count = _bounded_int(
+ spec.get("certified_mode_count"),
+ label="certified_mode_count",
+ minimum=2,
+ maximum=1_000_000,
+ )
+ forbidden_tags = _tags(spec.get("forbidden_tags", []), label="forbidden_tags")
+
+ raw_targets = spec.get("targets")
+ if not isinstance(raw_targets, Mapping) or not raw_targets:
+ raise PantryPlanError("targets must be a nonempty object")
+ targets: list[PantryTarget] = []
+ for raw_name, raw_bounds in raw_targets.items():
+ name = str(raw_name)
+ if name != "mass_g" and name not in _ALLOWED_ATTRIBUTES:
+ raise PantryPlanError(f"unsupported target attribute {name!r}")
+ if not isinstance(raw_bounds, Mapping):
+ raise PantryPlanError(f"target {name!r} must be an object")
+ unknown_bounds = set(raw_bounds) - {"min", "max"}
+ if unknown_bounds:
+ raise PantryPlanError(f"target {name!r} has unknown bounds")
+ minimum = (
+ _decimal(raw_bounds["min"], label=f"targets.{name}.min")
+ if "min" in raw_bounds
+ else None
+ )
+ maximum = (
+ _decimal(raw_bounds["max"], label=f"targets.{name}.max")
+ if "max" in raw_bounds
+ else None
+ )
+ if minimum is None and maximum is None:
+ raise PantryPlanError(f"target {name!r} has no bound")
+ if minimum is not None and maximum is not None and minimum > maximum:
+ raise PantryPlanError(f"target {name!r} has min greater than max")
+ if name != "mass_g":
+ for ingredient in ingredients:
+ if name not in ingredient.attributes:
+ raise PantryPlanError(
+ f"ingredient {ingredient.ingredient_id!r} lacks target "
+ f"attribute {name!r}"
+ )
+ targets.append(PantryTarget(name, minimum, maximum))
+
+ return PantryPlanSpec(
+ ingredients=tuple(ingredients),
+ targets=tuple(sorted(targets, key=lambda target: target.attribute)),
+ min_ingredients=min_ingredients,
+ max_ingredients=max_ingredients,
+ forbidden_tags=forbidden_tags,
+ certified_mode_count=certified_mode_count,
+ )
+
+
+def parse_pantry_plan_candidate(candidate: str) -> tuple[tuple[str, int], ...]:
+ """Parse ``ingredient_id=grams`` entries without executing model text."""
+
+ text = str(candidate).strip()
+ if not text or len(text) > _MAX_CANDIDATE_CHARS:
+ raise PantryPlanError("candidate is empty or too long")
+ if text.startswith("{") and text.endswith("}"):
+ text = text[1:-1].strip()
+ pieces = [piece.strip() for piece in re.split(r"[;,]", text)]
+ if not pieces or any(not piece for piece in pieces):
+ raise PantryPlanError("candidate contains an empty allocation")
+
+ allocations: dict[str, int] = {}
+ for piece in pieces:
+ match = re.fullmatch(r"([a-z][a-z0-9_]{0,31})\s*=\s*([0-9]+)", piece)
+ if match is None:
+ raise PantryPlanError(f"invalid allocation {piece!r}")
+ ingredient_id, raw_grams = match.groups()
+ if ingredient_id in allocations:
+ raise PantryPlanError(f"duplicate allocation for {ingredient_id!r}")
+ grams = int(raw_grams)
+ if not 1 <= grams <= 10_000:
+ raise PantryPlanError(f"allocation for {ingredient_id!r} is out of range")
+ allocations[ingredient_id] = grams
+ return tuple(sorted(allocations.items()))
+
+
+def validate_pantry_plan(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> PantryPlanValidation | None:
+ """Return the exact verified formulation mode, or ``None`` on rejection."""
+
+ try:
+ parsed_spec = parse_pantry_plan_spec(spec)
+ allocations = parse_pantry_plan_candidate(candidate)
+ if not (
+ parsed_spec.min_ingredients
+ <= len(allocations)
+ <= parsed_spec.max_ingredients
+ ):
+ raise PantryPlanError("candidate uses the wrong number of ingredients")
+
+ by_id = parsed_spec.ingredient_by_id
+ totals: dict[str, Decimal] = {"mass_g": Decimal(0)}
+ for ingredient_id, grams in allocations:
+ ingredient = by_id.get(ingredient_id)
+ if ingredient is None:
+ raise PantryPlanError(f"unknown ingredient {ingredient_id!r}")
+ if ingredient.tags & parsed_spec.forbidden_tags:
+ raise PantryPlanError(f"ingredient {ingredient_id!r} is forbidden")
+ if (
+ grams < ingredient.min_if_used_g
+ or grams > ingredient.available_g
+ or grams % ingredient.step_g
+ ):
+ raise PantryPlanError(
+ f"allocation for {ingredient_id!r} violates pantry bounds"
+ )
+ grams_decimal = Decimal(grams)
+ totals["mass_g"] += grams_decimal
+ for name, per_100g in ingredient.attributes_per_100g:
+ totals[name] = (
+ totals.get(name, Decimal(0))
+ + per_100g * grams_decimal / Decimal(100)
+ )
+
+ for target in parsed_spec.targets:
+ value = totals.get(target.attribute, Decimal(0))
+ if target.minimum is not None and value < target.minimum:
+ raise PantryPlanError(f"{target.attribute} is below its minimum")
+ if target.maximum is not None and value > target.maximum:
+ raise PantryPlanError(f"{target.attribute} exceeds its maximum")
+
+ support = tuple(ingredient_id for ingredient_id, _grams in allocations)
+ canonical_key = f"{PANTRY_PLAN_VERIFIER}:{PANTRY_PLAN_VERSION}:" + "+".join(
+ support
+ )
+ return PantryPlanValidation(
+ canonical_key=canonical_key,
+ allocations_g=allocations,
+ totals=tuple(sorted(totals.items())),
+ )
+ except (PantryPlanError, ArithmeticError, ValueError):
+ return None
diff --git a/code/oat_drgrpo/pantry_support_action.py b/code/oat_drgrpo/pantry_support_action.py
new file mode 100644
index 0000000000000000000000000000000000000000..7326a50ad0f6142ee9d2f5fb70952ff46a990678
--- /dev/null
+++ b/code/oat_drgrpo/pantry_support_action.py
@@ -0,0 +1,180 @@
+"""Development adapter for support-first PantryPlan actions.
+
+The language policy chooses only the ingredient support. A deterministic,
+trusted environment transition searches the registered quantity grid for the
+lexicographically first feasible allocation on exactly that support. It never
+consults the certified support catalogue or a reference allocation.
+"""
+
+from __future__ import annotations
+
+from itertools import product
+import re
+from typing import Any, Mapping
+
+from .pantry_plan import (
+ PantryPlanError,
+ PantryPlanValidation,
+ parse_pantry_plan_spec,
+ validate_pantry_plan,
+)
+
+
+PANTRY_SUPPORT_ACTION_VERSION = "pantry-support-action-dev-v1"
+PANTRY_SUPPORT_MASK_TASK = "pantry_support_mask"
+PANTRY_SUPPORT_MASK_WIDTH = 6
+PANTRY_SUPPORT_MASK_INVALID = "__INVALID_PANTRY_SUPPORT_MASK__"
+_MAX_SUPPORT_CHARS = 256
+
+
+def parse_pantry_support_action(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> tuple[str, ...]:
+ """Parse a support-only action against the prompt-local ingredient IDs."""
+
+ parsed_spec = parse_pantry_plan_spec(spec)
+ text = str(candidate).strip()
+ if not text or len(text) > _MAX_SUPPORT_CHARS:
+ raise PantryPlanError("support action is empty or too long")
+ if text.startswith("{") and text.endswith("}"):
+ text = text[1:-1].strip()
+ text = re.sub(r"^(?:support|ingredients?)\s*[:=]\s*", "", text, flags=re.I)
+ pieces = tuple(
+ piece for piece in re.split(r"[\s,;+]+", text.lower()) if piece
+ )
+ if not parsed_spec.min_ingredients <= len(pieces) <= parsed_spec.max_ingredients:
+ raise PantryPlanError("support action uses the wrong number of ingredients")
+ if len(set(pieces)) != len(pieces):
+ raise PantryPlanError("support action contains a duplicate ingredient")
+ allowed = parsed_spec.ingredient_by_id
+ unknown = [piece for piece in pieces if piece not in allowed]
+ if unknown:
+ raise PantryPlanError(f"unknown ingredient {unknown[0]!r}")
+ return tuple(sorted(pieces))
+
+
+def project_pantry_support(
+ support: tuple[str, ...],
+ spec: Mapping[str, Any],
+) -> PantryPlanValidation | None:
+ """Search only the selected support's registered quantity lattice."""
+
+ parsed_spec = parse_pantry_plan_spec(spec)
+ by_id = parsed_spec.ingredient_by_id
+ if (
+ not parsed_spec.min_ingredients <= len(support) <= parsed_spec.max_ingredients
+ or len(set(support)) != len(support)
+ or any(ingredient_id not in by_id for ingredient_id in support)
+ ):
+ return None
+ grids = []
+ for ingredient_id in support:
+ ingredient = by_id[ingredient_id]
+ grids.append(
+ range(
+ ingredient.min_if_used_g,
+ ingredient.available_g + 1,
+ ingredient.step_g,
+ )
+ )
+ for quantities in product(*grids):
+ allocation = ";".join(
+ f"{ingredient_id}={grams}"
+ for ingredient_id, grams in zip(support, quantities)
+ )
+ validation = validate_pantry_plan(allocation, spec)
+ if validation is not None:
+ return validation
+ return None
+
+
+def validate_pantry_support_action(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> PantryPlanValidation | None:
+ """Return a verifier-bound mode after deterministic quantity projection."""
+
+ try:
+ support = parse_pantry_support_action(candidate, spec)
+ except (PantryPlanError, ValueError):
+ return None
+ return project_pantry_support(support, spec)
+
+def pantry_support_from_mask(
+ mask: str,
+ spec: Mapping[str, Any],
+) -> tuple[str, ...] | None:
+ """Decode one fixed-width inclusion mask using the public pantry row order."""
+
+ parsed_spec = parse_pantry_plan_spec(spec)
+ text = str(mask).strip()
+ if (
+ len(parsed_spec.ingredients) != PANTRY_SUPPORT_MASK_WIDTH
+ or len(text) != PANTRY_SUPPORT_MASK_WIDTH
+ or any(bit not in {"0", "1"} for bit in text)
+ ):
+ return None
+ support = tuple(
+ ingredient.ingredient_id
+ for bit, ingredient in zip(text, parsed_spec.ingredients)
+ if bit == "1"
+ )
+ if not parsed_spec.min_ingredients <= len(support) <= parsed_spec.max_ingredients:
+ return None
+ return tuple(sorted(support))
+
+
+def pantry_support_mask_from_allocation(
+ allocation: str,
+ spec: Mapping[str, Any],
+) -> str:
+ """Project a verifier-valid allocation onto its public-row support mask.
+
+ This is the inverse representation adapter needed when an offline replay
+ pool stores PantryPlan witnesses but the policy acts on the registered
+ support-mask surface. It validates the original witness first and changes
+ only its serialization; no replay row is dropped or deduplicated.
+ """
+
+ parsed_spec = parse_pantry_plan_spec(spec)
+ if len(parsed_spec.ingredients) != PANTRY_SUPPORT_MASK_WIDTH:
+ raise PantryPlanError(
+ "Pantry support masks require exactly "
+ f"{PANTRY_SUPPORT_MASK_WIDTH} public ingredient rows"
+ )
+ validation = validate_pantry_plan(allocation, spec)
+ if validation is None:
+ raise PantryPlanError("replayed Pantry allocation fails its verifier")
+ selected = {ingredient_id for ingredient_id, _grams in validation.allocations_g}
+ mask = "".join(
+ "1" if ingredient.ingredient_id in selected else "0"
+ for ingredient in parsed_spec.ingredients
+ )
+ decoded_support = pantry_support_from_mask(mask, spec)
+ if decoded_support != tuple(sorted(selected)):
+ raise PantryPlanError("Pantry allocation cannot be represented by its mask")
+ return mask
+
+
+def decode_pantry_support_mask(
+ mask: str,
+ spec: Mapping[str, Any],
+) -> str:
+ """Project a six-bit policy action into the exact allocation grader surface.
+
+ Every bit string is a syntactically valid fixed-horizon policy action.
+ Masks with an invalid support width or no feasible quantity assignment map
+ to a fail-closed sentinel that the ordinary PantryPlan verifier rejects.
+ """
+
+ support = pantry_support_from_mask(mask, spec)
+ if support is None:
+ return PANTRY_SUPPORT_MASK_INVALID
+ validation = project_pantry_support(support, spec)
+ if validation is None:
+ return PANTRY_SUPPORT_MASK_INVALID
+ return ";".join(
+ f"{ingredient_id}={grams}"
+ for ingredient_id, grams in validation.allocations_g
+ )
diff --git a/code/oat_drgrpo/python_modebench.py b/code/oat_drgrpo/python_modebench.py
new file mode 100644
index 0000000000000000000000000000000000000000..05a89956f9e8246b598719fd88bc865f6b9b8f45
--- /dev/null
+++ b/code/oat_drgrpo/python_modebench.py
@@ -0,0 +1,238 @@
+"""Restricted executable Python tasks for ModeBench.
+
+The model writes one pure ``lambda n: ...`` expression. A separate worker
+process evaluates the function on a frozen prompt-local test suite. A response
+is correct when every returned integer is a proper divisor of its input. The
+semantic outcome is the complete vector returned by that same execution.
+
+This module owns the syntax and task contract. The actual call to model code
+is made only by :mod:`oat_drgrpo.python_modebench_worker`.
+"""
+
+from __future__ import annotations
+
+import ast
+from dataclasses import dataclass
+from math import prod
+from typing import Any, Mapping
+
+
+PYTHON_FACTOR_VERIFIER = "python_factor_function"
+PYTHON_FACTOR_VERSION = "factor-v1"
+_MAX_CANDIDATE_CHARS = 240
+_MAX_AST_NODES = 64
+_MAX_CASES = 8
+
+_ALLOWED_NODE_TYPES = (
+ ast.Expression,
+ ast.Lambda,
+ ast.arguments,
+ ast.arg,
+ ast.IfExp,
+ ast.BoolOp,
+ ast.And,
+ ast.Or,
+ ast.Compare,
+ ast.Eq,
+ ast.NotEq,
+ ast.Lt,
+ ast.LtE,
+ ast.Gt,
+ ast.GtE,
+ ast.BinOp,
+ ast.Add,
+ ast.Sub,
+ ast.Mult,
+ ast.FloorDiv,
+ ast.Mod,
+ ast.UnaryOp,
+ ast.UAdd,
+ ast.USub,
+ ast.Not,
+ ast.Name,
+ ast.Load,
+ ast.Constant,
+)
+
+
+class PythonModeBenchError(ValueError):
+ """Raised for a malformed task or candidate program."""
+
+
+@dataclass(frozen=True)
+class PythonFactorValidation:
+ """Result of one successful external execution."""
+
+ canonical_key: str
+ outputs: tuple[int, ...]
+
+
+def _canonical_python_route_ast(node: ast.AST) -> str:
+ """Return a literal-abstracted strategy skeleton for restricted Python."""
+
+ if isinstance(node, ast.Expression):
+ return _canonical_python_route_ast(node.body)
+ if isinstance(node, ast.Lambda):
+ return f"lambda({_canonical_python_route_ast(node.body)})"
+ if isinstance(node, ast.Name):
+ return "input"
+ if isinstance(node, ast.Constant):
+ return "literal"
+ if isinstance(node, ast.UnaryOp):
+ return (
+ f"{type(node.op).__name__.lower()}"
+ f"({_canonical_python_route_ast(node.operand)})"
+ )
+ if isinstance(node, ast.BinOp):
+ op = type(node.op).__name__.lower()
+ children = [
+ _canonical_python_route_ast(node.left),
+ _canonical_python_route_ast(node.right),
+ ]
+ if isinstance(node.op, (ast.Add, ast.Mult)):
+ children.sort()
+ return f"{op}({','.join(children)})"
+ if isinstance(node, ast.Compare):
+ return "compare(" + ",".join(
+ [
+ _canonical_python_route_ast(node.left),
+ *(type(op).__name__.lower() for op in node.ops),
+ *(
+ _canonical_python_route_ast(comparator)
+ for comparator in node.comparators
+ ),
+ ]
+ ) + ")"
+ if isinstance(node, ast.BoolOp):
+ values = [_canonical_python_route_ast(value) for value in node.values]
+ values.sort()
+ return f"{type(node.op).__name__.lower()}({','.join(values)})"
+ if isinstance(node, ast.IfExp):
+ return (
+ f"if({_canonical_python_route_ast(node.test)},"
+ f"{_canonical_python_route_ast(node.body)},"
+ f"{_canonical_python_route_ast(node.orelse)})"
+ )
+ raise PythonModeBenchError(
+ f"cannot canonicalize Python route node {type(node).__name__}"
+ )
+
+
+def python_factor_route_signature(candidate: str) -> str:
+ """Return code-structure identity after the restricted parser accepts it."""
+
+ parsed = parse_python_factor_candidate(candidate)
+ return "python-factor-route:v1:" + _canonical_python_route_ast(parsed)
+
+
+def proper_divisors(value: int) -> tuple[int, ...]:
+ """Return all positive proper divisors other than one."""
+
+ value = int(value)
+ if value < 4:
+ return ()
+ return tuple(divisor for divisor in range(2, value) if value % divisor == 0)
+
+
+def python_factor_mode_count(cases: tuple[int, ...] | list[int]) -> int:
+ """Return the exact number of valid behavior vectors for a task."""
+
+ divisor_sets = [proper_divisors(value) for value in cases]
+ if any(not divisors for divisors in divisor_sets):
+ return 0
+ return prod(len(divisors) for divisors in divisor_sets)
+
+
+def parse_python_factor_spec(spec: Mapping[str, Any]) -> tuple[int, ...]:
+ """Validate a trusted dataset specification and return its tool inputs."""
+
+ if spec.get("verifier") != PYTHON_FACTOR_VERIFIER:
+ raise PythonModeBenchError("wrong Python ModeBench verifier")
+ if spec.get("python_version") != PYTHON_FACTOR_VERSION:
+ raise PythonModeBenchError("unsupported Python ModeBench version")
+ raw_cases = spec.get("cases")
+ if not isinstance(raw_cases, list) or not 2 <= len(raw_cases) <= _MAX_CASES:
+ raise PythonModeBenchError("cases must contain between two and eight inputs")
+ if any(isinstance(value, bool) or not isinstance(value, int) for value in raw_cases):
+ raise PythonModeBenchError("case inputs must be integers")
+ cases = tuple(int(value) for value in raw_cases)
+ if len(set(cases)) != len(cases):
+ raise PythonModeBenchError("case inputs must be unique")
+ if any(value < 4 or value > 1_000 for value in cases):
+ raise PythonModeBenchError("case inputs are outside the bounded domain")
+ if python_factor_mode_count(cases) < 2:
+ raise PythonModeBenchError("task does not have multiple valid modes")
+ return cases
+
+
+def parse_python_factor_candidate(candidate: str) -> ast.Expression:
+ """Parse the bounded, call-free lambda language."""
+
+ text = str(candidate).strip()
+ if not text or len(text) > _MAX_CANDIDATE_CHARS:
+ raise PythonModeBenchError("candidate is empty or too long")
+ try:
+ parsed = ast.parse(text, mode="eval")
+ except (SyntaxError, ValueError) as error:
+ raise PythonModeBenchError("candidate is not one Python expression") from error
+ if not isinstance(parsed.body, ast.Lambda):
+ raise PythonModeBenchError("candidate must be a lambda")
+ arguments = parsed.body.args
+ if (
+ len(arguments.args) != 1
+ or arguments.args[0].arg != "n"
+ or arguments.posonlyargs
+ or arguments.kwonlyargs
+ or arguments.vararg is not None
+ or arguments.kwarg is not None
+ or arguments.defaults
+ or arguments.kw_defaults
+ ):
+ raise PythonModeBenchError("lambda must have the exact signature lambda n")
+ nodes = list(ast.walk(parsed))
+ if len(nodes) > _MAX_AST_NODES:
+ raise PythonModeBenchError("candidate AST is too large")
+ for node in nodes:
+ if not isinstance(node, _ALLOWED_NODE_TYPES):
+ raise PythonModeBenchError(
+ f"unsupported Python syntax: {type(node).__name__}"
+ )
+ if isinstance(node, ast.Name) and node.id != "n":
+ raise PythonModeBenchError(f"unknown name: {node.id}")
+ if isinstance(node, ast.Constant):
+ if isinstance(node.value, bool) or not isinstance(node.value, int):
+ raise PythonModeBenchError("only integer literals are allowed")
+ if abs(int(node.value)) > 10_000:
+ raise PythonModeBenchError("integer literal is too large")
+ return parsed
+
+
+def execute_python_factor_candidate(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> PythonFactorValidation:
+ """Execute one already restricted candidate.
+
+ This entry point is worker-only. Callers in the trainer and evaluator use
+ ``validate_python_factor_function_external`` so model code never executes
+ in their process.
+ """
+
+ cases = parse_python_factor_spec(spec)
+ parsed = parse_python_factor_candidate(candidate)
+ code = compile(parsed, "", "eval")
+ function = eval(code, {"__builtins__": {}}, {}) # noqa: S307
+ outputs: list[int] = []
+ for value in cases:
+ output = function(value)
+ if isinstance(output, bool) or not isinstance(output, int):
+ raise PythonModeBenchError("function output must be an integer")
+ output = int(output)
+ if output <= 1 or output >= value or value % output != 0:
+ raise PythonModeBenchError(
+ f"function returned {output}, not a proper divisor of {value}"
+ )
+ outputs.append(output)
+ output_tuple = tuple(outputs)
+ key = "python_factor:" + ",".join(str(value) for value in output_tuple)
+ return PythonFactorValidation(canonical_key=key, outputs=output_tuple)
diff --git a/code/oat_drgrpo/python_modebench_process.py b/code/oat_drgrpo/python_modebench_process.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f3f3741ee282225fb6678c8342376a6275acea5
--- /dev/null
+++ b/code/oat_drgrpo/python_modebench_process.py
@@ -0,0 +1,135 @@
+"""Killable external execution boundary for ModeBench Python functions."""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+import select
+import subprocess
+import sys
+import threading
+from typing import Any, Mapping
+
+from .python_modebench import PythonFactorValidation
+
+
+class PythonFactorVerifierProcess:
+ """Execute restricted candidate functions outside the trainer process."""
+
+ def __init__(self, *, timeout_seconds: float = 1.0) -> None:
+ if timeout_seconds <= 0:
+ raise ValueError("timeout_seconds must be positive")
+ self.timeout_seconds = float(timeout_seconds)
+ self._process: subprocess.Popen[str] | None = None
+ self._owner_pid = os.getpid()
+ self._lock = threading.Lock()
+
+ def _start(self) -> subprocess.Popen[str]:
+ if self._owner_pid != os.getpid():
+ self._process = None
+ self._owner_pid = os.getpid()
+ process = self._process
+ if process is not None and process.poll() is None:
+ return process
+ worker_env = os.environ.copy()
+ source_root = str(Path(__file__).resolve().parents[1])
+ inherited_pythonpath = worker_env.get("PYTHONPATH")
+ worker_env["PYTHONPATH"] = (
+ source_root
+ if not inherited_pythonpath
+ else source_root + os.pathsep + inherited_pythonpath
+ )
+ isolated_entrypoint = (
+ "import runpy,sys;"
+ f"sys.path.insert(0,{source_root!r});"
+ "runpy.run_module('oat_drgrpo.python_modebench_worker',run_name='__main__')"
+ )
+ self._process = subprocess.Popen(
+ [sys.executable, "-I", "-c", isolated_entrypoint],
+ stdin=subprocess.PIPE,
+ stdout=subprocess.PIPE,
+ stderr=None,
+ text=True,
+ encoding="utf-8",
+ bufsize=1,
+ start_new_session=True,
+ env=worker_env,
+ )
+ return self._process
+
+ def _stop(self) -> None:
+ process, self._process = self._process, None
+ if process is None:
+ return
+ if process.poll() is None:
+ process.terminate()
+ try:
+ process.wait(timeout=1)
+ except subprocess.TimeoutExpired:
+ process.kill()
+ process.wait(timeout=1)
+ if process.stdin is not None:
+ process.stdin.close()
+ if process.stdout is not None:
+ process.stdout.close()
+
+ def close(self) -> None:
+ with self._lock:
+ self._stop()
+
+ def validate(
+ self,
+ candidate: str,
+ spec: Mapping[str, Any],
+ ) -> PythonFactorValidation | None:
+ request = json.dumps(
+ {"candidate": str(candidate), "spec": dict(spec)},
+ allow_nan=False,
+ )
+ with self._lock:
+ for attempt in range(2):
+ process = self._start()
+ try:
+ assert process.stdin is not None and process.stdout is not None
+ process.stdin.write(request + "\n")
+ process.stdin.flush()
+ readable, _, _ = select.select(
+ [process.stdout], [], [], self.timeout_seconds
+ )
+ if not readable:
+ self._stop()
+ return None
+ line = process.stdout.readline()
+ if not line:
+ raise BrokenPipeError("Python ModeBench worker closed stdout")
+ payload = json.loads(line)
+ if not payload.get("valid"):
+ return None
+ return PythonFactorValidation(
+ canonical_key=str(payload["canonical_key"]),
+ outputs=tuple(int(value) for value in payload["outputs"]),
+ )
+ except (BrokenPipeError, OSError, ValueError, json.JSONDecodeError):
+ self._stop()
+ if attempt:
+ return None
+ return None
+
+ def __del__(self) -> None:
+ try:
+ self._stop()
+ except Exception:
+ pass
+
+
+_SHARED_VERIFIER = PythonFactorVerifierProcess()
+
+
+def validate_python_factor_function_external(
+ candidate: str,
+ spec: Mapping[str, Any],
+) -> PythonFactorValidation | None:
+ """Validate a candidate through the shared external Python worker."""
+
+ return _SHARED_VERIFIER.validate(candidate, spec)
diff --git a/code/oat_drgrpo/python_modebench_worker.py b/code/oat_drgrpo/python_modebench_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..738d25f77fabf9570432efccadba7ac4e55a4de6
--- /dev/null
+++ b/code/oat_drgrpo/python_modebench_worker.py
@@ -0,0 +1,43 @@
+"""JSON-lines worker that executes restricted ModeBench Python functions."""
+
+from __future__ import annotations
+
+import json
+import signal
+import sys
+
+from .python_modebench import execute_python_factor_candidate
+
+
+def _timeout(_signum, _frame) -> None:
+ raise TimeoutError("Python ModeBench candidate timed out")
+
+
+def main() -> None:
+ signal.signal(signal.SIGALRM, _timeout)
+ for line in sys.stdin:
+ try:
+ request = json.loads(line)
+ signal.setitimer(signal.ITIMER_REAL, 0.25)
+ validation = execute_python_factor_candidate(
+ request["candidate"],
+ request["spec"],
+ )
+ payload = {
+ "valid": True,
+ "canonical_key": validation.canonical_key,
+ "outputs": list(validation.outputs),
+ }
+ except Exception as error:
+ payload = {
+ "valid": False,
+ "error": type(error).__name__,
+ }
+ finally:
+ signal.setitimer(signal.ITIMER_REAL, 0)
+ sys.stdout.write(json.dumps(payload, allow_nan=False) + "\n")
+ sys.stdout.flush()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/oat_drgrpo/templates.py b/code/oat_drgrpo/templates.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d136b4ce41c9dcd45653db8e474ac6f6e1f9643
--- /dev/null
+++ b/code/oat_drgrpo/templates.py
@@ -0,0 +1,492 @@
+"""Prompt templates and response-token mask helpers."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+
+import torch
+
+# Chat surfaces keep one benchmark contract portable across model families.
+# A surface supplies only the role markers; the system instruction and the user
+# body below are shared verbatim, so a Qwen prompt and its Falcon counterpart
+# differ in nothing but the tokens the base model was instruction-tuned on.
+# The Falcon markers reproduce tiiuae/Falcon3-*-Instruct's published chat
+# template byte for byte (``<|system|>\n...\n<|user|>\n...\n<|assistant|>\n``);
+# unlike Qwen's single-token ``<|im_start|>`` they encode as ordinary tokens,
+# which costs a few prompt tokens but leaves the response surface untouched.
+CHAT_SURFACES: dict[str, tuple[str, str, str]] = {
+ "qwen": (
+ "<|im_start|>system\n",
+ "<|im_end|>\n<|im_start|>user\n",
+ "<|im_end|>\n<|im_start|>assistant\n",
+ ),
+ "falcon": ("<|system|>\n", "\n<|user|>\n", "\n<|assistant|>\n"),
+}
+
+
+def render_chat_prompt(surface: str, system: str, user: str) -> str:
+ """Render one system/user turn onto a named chat surface."""
+
+ system_marker, user_marker, assistant_marker = CHAT_SURFACES[surface]
+ return system_marker + system + user_marker + user + assistant_marker
+
+
+_MATH_SYSTEM = (
+ "Please reason step by step, and put your final answer within \\boxed{}."
+)
+
+
+def apply_qwen_math_template(question: str) -> str:
+ return render_chat_prompt("qwen", _MATH_SYSTEM, question)
+
+
+def apply_falcon_math_template(question: str) -> str:
+ return render_chat_prompt("falcon", _MATH_SYSTEM, question)
+
+
+def apply_qwen_math_route_json_v1_template(question: str) -> str:
+ """Reproduce the frozen JSON-v1 Gate 1 prompt exactly."""
+
+ return (
+ "<|im_start|>system\n"
+ "Solve the problem step by step and put the final answer within "
+ "\\boxed{}. After the boxed answer, also emit exactly one compact "
+ "JSON block when the computation fits this schema: "
+ '{"version":"math-route-v1","steps":[...],"final":"sN"}. '
+ "Step IDs are contiguous s1,s2,... . A source step is "
+ '{"id":"s1","op":"source","value":"a number copied from the problem"}. '
+ "Other steps contain only id, op, and earlier args; never write a "
+ "result field. Allowed ops are neg, abs, square, cube, sqrt, factorial, "
+ "percent, add, mul, sub, div, pow, mod, choose, permute, gcd, lcm, min, "
+ "max, and average. Every step must contribute to final. If this exact "
+ "numeric trace language cannot express the solution, omit the route "
+ "block but still give the boxed answer."
+ "<|im_end|>\n<|im_start|>user\n"
+ + question
+ + "<|im_end|>\n<|im_start|>assistant\n"
+ )
+
+
+_MATH_ROUTE_SYSTEM = (
+ "Solve the problem step by step and put the final answer within "
+ "\\boxed{}. When the answer is computed from numbers in the problem, "
+ "end with exactly one compact reverse-Polish program between "
+ " and . Start the program with v2. Write copied input "
+ "numbers and operation words separated only by spaces. For example, "
+ "'v2 2 5 mul 1 sub square 1 add' computes "
+ "(2*5-1)^2+1. Unary operations are neg abs square cube sqrt factorial "
+ "percent. Binary operations are add mul sub div pow mod choose permute "
+ "gcd lcm min max average. A binary operation consumes the previous two "
+ "values in left-to-right order. Copy every numeric input from the "
+ "problem; never put a computed result, JSON, equations, labels, or "
+ "prose inside . If this numeric stack language cannot express "
+ "the solution, omit but still give the boxed answer."
+)
+
+
+def apply_qwen_math_route_template(question: str) -> str:
+ """Request a boxed answer plus a compact executable numeric route."""
+
+ return render_chat_prompt("qwen", _MATH_ROUTE_SYSTEM, question)
+
+
+def apply_falcon_math_route_template(question: str) -> str:
+ """Falcon-surface twin of :func:`apply_qwen_math_route_template`."""
+
+ return render_chat_prompt("falcon", _MATH_ROUTE_SYSTEM, question)
+
+
+_BOXED_SYSTEM = "Return only the final answer inside \\boxed{}. Do not explain."
+
+
+def apply_qwen_boxed_template(question: str) -> str:
+ return render_chat_prompt("qwen", _BOXED_SYSTEM, question)
+
+
+def apply_falcon_boxed_template(question: str) -> str:
+ return render_chat_prompt("falcon", _BOXED_SYSTEM, question)
+
+
+_LEVEL2_HYBRID_SYSTEMS = {
+ "countdown": (
+ "Solve the executable constraint problem carefully. You may reason briefly, "
+ "but end with exactly one final answer inside \\boxed{}. Systematically combine "
+ "every supplied number exactly once using +, -, *, /, and parentheses. Check "
+ "the exact target before answering. Output exactly the boxed expression."
+ ),
+ "python_factors": (
+ "Solve the executable constraint problem carefully. You may reason briefly, "
+ "but end with exactly one final answer inside \\boxed{}. Construct one allowed "
+ "lambda expression. Test small divisors with nested conditional expressions, "
+ "for example 2 if n % 2 == 0 else 3 if n % 3 == 0 else 5, but adapt the "
+ "tests to every listed case. You may instead dispatch on each listed value. "
+ "Output exactly the boxed lambda."
+ ),
+ "mathir": (
+ "Solve the executable constraint problem carefully. You may reason briefly, "
+ "but end with exactly one final answer inside \\boxed{}. Use algebraic isolation: "
+ "move the right-side x term left, remove the left constant, then divide by the "
+ "combined coefficient. Match those operations to the shuffled menu IDs."
+ ),
+ "pantry": (
+ "Solve the executable constraint problem carefully. You may reason briefly, "
+ "but end with exactly one final answer inside \\boxed{}. Prefer allowed "
+ "high-energy/protein, very-low-sodium ingredients, especially seeds or oats. "
+ "Choose stepped amounts, check every bound, and output 2 to 4 "
+ "ingredient_id=grams pairs."
+ ),
+}
+
+
+def apply_qwen_level2_countdown_template(question: str) -> str:
+ return render_chat_prompt("qwen", _LEVEL2_HYBRID_SYSTEMS["countdown"], question)
+
+
+def apply_qwen_level2_python_factors_template(question: str) -> str:
+ return render_chat_prompt("qwen", _LEVEL2_HYBRID_SYSTEMS["python_factors"], question)
+
+
+def apply_qwen_level2_mathir_template(question: str) -> str:
+ return render_chat_prompt("qwen", _LEVEL2_HYBRID_SYSTEMS["mathir"], question)
+
+
+def apply_qwen_level2_pantry_template(question: str) -> str:
+ return render_chat_prompt("qwen", _LEVEL2_HYBRID_SYSTEMS["pantry"], question)
+
+
+_GRAPH_DIGITS_SYSTEM = (
+ "Return only the requested bare sequence of digits. Do not explain, "
+ "add punctuation, or use LaTeX."
+)
+
+
+def _graph_digits_user(question: str, template_name: str) -> str:
+ boxed_suffix = "inside \\boxed{}."
+ if question.count(boxed_suffix) != 1 or not question.endswith(boxed_suffix):
+ raise ValueError(
+ f"{template_name} requires a graph prompt ending in a boxed answer"
+ )
+ return (
+ question[: -len(boxed_suffix)]
+ + "as one bare digit string with no spaces, punctuation, or other text."
+ )
+
+
+def apply_qwen_graph_digits_template(question: str) -> str:
+ """Request the benchmark's canonical bare graph-color action vector."""
+
+ return render_chat_prompt(
+ "qwen",
+ _GRAPH_DIGITS_SYSTEM,
+ _graph_digits_user(question, "qwen_graph_digits"),
+ )
+
+
+def apply_falcon_graph_digits_template(question: str) -> str:
+ """Falcon-surface twin of :func:`apply_qwen_graph_digits_template`."""
+
+ return render_chat_prompt(
+ "falcon",
+ _GRAPH_DIGITS_SYSTEM,
+ _graph_digits_user(question, "falcon_graph_digits"),
+ )
+
+
+_COUNTDOWN_DIGITS_SYSTEM = (
+ "Return only three digits and no other text. The given numbers are "
+ "n1,n2,n3 in dataset order. Digit 2 selects singleton s: 1=n1, "
+ "2=n2, 3=n3; a,b are the other two numbers in their original order. "
+ "Digit 3 makes pair: 1=a+b, 2=a*b, 3=a-b, 4=b-a, 5=a/b, 6=b/a. "
+ "Digit 1 combines pair and s: 1=pair+s, 2=pair*s, 3=pair-s, "
+ "4=s-pair, 5=pair/s, 6=s/pair."
+)
+
+
+def _countdown_digits_user(question: str, template_name: str) -> str:
+ boxed_suffix = "the expression inside \\boxed{}."
+ if question.count(boxed_suffix) != 1 or not question.endswith(boxed_suffix):
+ raise ValueError(
+ f"{template_name} requires an easy3 Countdown prompt ending "
+ "in the boxed-expression instruction"
+ )
+ return (
+ question[: -len(boxed_suffix)]
+ + "a three-digit canonical action code using the scheme above."
+ )
+
+
+def apply_qwen_countdown_digits_template(question: str) -> str:
+ """Request the audited three-digit Countdown expression code."""
+
+ return render_chat_prompt(
+ "qwen",
+ _COUNTDOWN_DIGITS_SYSTEM,
+ _countdown_digits_user(question, "qwen_countdown_digits"),
+ )
+
+
+def apply_falcon_countdown_digits_template(question: str) -> str:
+ """Falcon-surface twin of :func:`apply_qwen_countdown_digits_template`."""
+
+ return render_chat_prompt(
+ "falcon",
+ _COUNTDOWN_DIGITS_SYSTEM,
+ _countdown_digits_user(question, "falcon_countdown_digits"),
+ )
+
+
+_PANTRY_SUPPORT_MASK_SYSTEM = (
+ "Return only six binary digits and no other text. Each digit maps to "
+ "the corresponding Pantry row in printed order. A trusted environment "
+ "will choose exact quantities on precisely the selected rows."
+)
+
+
+def _pantry_support_mask_user(question: str, template_name: str) -> str:
+ suffix = (
+ "Return only ingredient_id=grams pairs separated by semicolons inside "
+ "\\boxed{}. Do not add a recipe name or preparation prose."
+ )
+ if question.count(suffix) != 1 or not question.endswith(suffix):
+ raise ValueError(
+ f"{template_name} requires the frozen PantryPlan answer suffix"
+ )
+ return (
+ question[: -len(suffix)]
+ + "Choose the ingredient support as a six-bit mask in the exact Pantry "
+ "row order. Bit 1 includes that row and bit 0 excludes it."
+ )
+
+
+def apply_qwen_pantry_support_mask_template(question: str) -> str:
+ """Request a six-bit ingredient-support action in printed pantry order."""
+
+ return render_chat_prompt(
+ "qwen",
+ _PANTRY_SUPPORT_MASK_SYSTEM,
+ _pantry_support_mask_user(question, "qwen_pantry_support_mask"),
+ )
+
+
+def apply_falcon_pantry_support_mask_template(question: str) -> str:
+ """Falcon-surface twin of :func:`apply_qwen_pantry_support_mask_template`."""
+
+ return render_chat_prompt(
+ "falcon",
+ _PANTRY_SUPPORT_MASK_SYSTEM,
+ _pantry_support_mask_user(question, "falcon_pantry_support_mask"),
+ )
+
+
+# Every canonical-action task admits exactly one prompt template per chat
+# surface. Binding the task to that closed set keeps a Falcon run from
+# silently training against Qwen role markers the base model never saw.
+CANONICAL_TASK_PROMPT_TEMPLATES: dict[str, frozenset[str]] = {
+ "graph_coloring": frozenset({"qwen_graph_digits", "falcon_graph_digits"}),
+ "countdown": frozenset(
+ {"qwen_countdown_digits", "falcon_countdown_digits"}
+ ),
+ "pantry_support_mask": frozenset(
+ {"qwen_pantry_support_mask", "falcon_pantry_support_mask"}
+ ),
+}
+
+
+def validate_canonical_prompt_materialization(
+ prompt_template: str,
+ raw_questions: Sequence[str],
+ formatted_prompts: Sequence[str],
+) -> None:
+ """Fail closed if dataset mapping did not render the canonical template.
+
+ The exact string comparison deliberately detects stale Hugging Face map
+ caches from older prompt templates as well as dropped or reordered rows.
+ Re-applying ``TEMPLATE_FACTORY[prompt_template]`` rather than a hardcoded
+ function makes the check family-agnostic: a Falcon-surface run is held to
+ its own template and cannot pass on Qwen-rendered rows.
+ """
+
+ if len(raw_questions) != len(formatted_prompts):
+ raise RuntimeError(
+ "canonical prompt materialization changed the row count: "
+ f"raw={len(raw_questions)} formatted={len(formatted_prompts)}"
+ )
+ apply_template = TEMPLATE_FACTORY[prompt_template]
+ for row_index, (question, observed) in enumerate(
+ zip(raw_questions, formatted_prompts)
+ ):
+ if observed != apply_template(question):
+ raise RuntimeError(
+ f"canonical {prompt_template} prompt materialization mismatch "
+ f"at row {row_index}; stale or foreign template cache detected"
+ )
+
+
+def validate_qwen_graph_digits_materialization(
+ raw_questions: Sequence[str], formatted_prompts: Sequence[str]
+) -> None:
+ """Fail closed on stale or foreign canonical graph prompt rows."""
+
+ validate_canonical_prompt_materialization(
+ "qwen_graph_digits", raw_questions, formatted_prompts
+ )
+
+
+def validate_qwen_countdown_digits_materialization(
+ raw_questions: Sequence[str], formatted_prompts: Sequence[str]
+) -> None:
+ """Fail closed on stale or foreign canonical Countdown prompt rows."""
+
+ validate_canonical_prompt_materialization(
+ "qwen_countdown_digits", raw_questions, formatted_prompts
+ )
+
+
+def validate_qwen_pantry_support_mask_materialization(
+ raw_questions: Sequence[str], formatted_prompts: Sequence[str]
+) -> None:
+ """Fail closed on stale or foreign Pantry support-mask prompt rows."""
+
+ validate_canonical_prompt_materialization(
+ "qwen_pantry_support_mask", raw_questions, formatted_prompts
+ )
+
+
+def apply_r1_template(question: str) -> str:
+ return (
+ "A conversation between User and Assistant. The User asks a question, "
+ "and the Assistant solves it. The Assistant first thinks about the "
+ "reasoning process in the mind and then provides the User with the "
+ "answer. The reasoning process is enclosed within "
+ "and answer is enclosed within tags, respectively, "
+ "i.e., reasoning process here answer here "
+ ".\nUser: " + question + "\nAssistant: "
+ )
+
+
+def apply_no_template(question: str) -> str:
+ return question
+
+
+TEMPLATE_FACTORY: dict[str, Callable[[str], str]] = {
+ "qwen_boxed": apply_qwen_boxed_template,
+ "qwen_countdown_digits": apply_qwen_countdown_digits_template,
+ "qwen_graph_digits": apply_qwen_graph_digits_template,
+ "qwen_pantry_support_mask": apply_qwen_pantry_support_mask_template,
+ "qwen_math": apply_qwen_math_template,
+ "qwen_math_route": apply_qwen_math_route_template,
+ "qwen_level2_countdown": apply_qwen_level2_countdown_template,
+ "qwen_level2_python_factors": apply_qwen_level2_python_factors_template,
+ "qwen_level2_mathir": apply_qwen_level2_mathir_template,
+ "qwen_level2_pantry": apply_qwen_level2_pantry_template,
+ "falcon_boxed": apply_falcon_boxed_template,
+ "falcon_countdown_digits": apply_falcon_countdown_digits_template,
+ "falcon_graph_digits": apply_falcon_graph_digits_template,
+ "falcon_pantry_support_mask": apply_falcon_pantry_support_mask_template,
+ "falcon_math": apply_falcon_math_template,
+ "falcon_math_route": apply_falcon_math_route_template,
+ "r1": apply_r1_template,
+ "no": apply_no_template,
+}
+
+# The Qwen and Falcon families must stay a pure surface swap of one contract.
+PROMPT_TEMPLATE_FAMILY_TWINS: dict[str, str] = {
+ "qwen_boxed": "falcon_boxed",
+ "qwen_countdown_digits": "falcon_countdown_digits",
+ "qwen_graph_digits": "falcon_graph_digits",
+ "qwen_pantry_support_mask": "falcon_pantry_support_mask",
+ "qwen_math": "falcon_math",
+ "qwen_math_route": "falcon_math_route",
+}
+
+# A template's *role* is the training contract it encodes; the chat surface is
+# an implementation detail of the base model. Argument validation keys off the
+# role so a Falcon run is held to exactly the Qwen run's contract.
+PROMPT_TEMPLATE_ROLES: dict[str, str] = {
+ "qwen_boxed": "boxed",
+ "falcon_boxed": "boxed",
+ "qwen_countdown_digits": "countdown_digits",
+ "falcon_countdown_digits": "countdown_digits",
+ "qwen_graph_digits": "graph_digits",
+ "falcon_graph_digits": "graph_digits",
+ "qwen_pantry_support_mask": "pantry_support_mask",
+ "falcon_pantry_support_mask": "pantry_support_mask",
+ "qwen_math": "math",
+ "falcon_math": "math",
+ "qwen_math_route": "math_route",
+ "qwen_level2_countdown": "boxed",
+ "qwen_level2_python_factors": "boxed",
+ "qwen_level2_mathir": "boxed",
+ "qwen_level2_pantry": "boxed",
+ "falcon_math_route": "math_route",
+ "r1": "r1",
+ "no": "no",
+}
+
+CANONICAL_DIGIT_TEMPLATE_ROLES: frozenset[str] = frozenset(
+ {"graph_digits", "countdown_digits", "pantry_support_mask"}
+)
+
+
+def prompt_template_role(prompt_template: str) -> str:
+ """Return the surface-independent training contract a template encodes."""
+
+ return PROMPT_TEMPLATE_ROLES[prompt_template]
+
+
+def prompt_template_surface(prompt_template: str) -> str:
+ """Return the chat surface family a template renders onto."""
+
+ for surface in CHAT_SURFACES:
+ if prompt_template.startswith(f"{surface}_"):
+ return surface
+ return "none"
+
+
+def apply_prompt_template_to_example(
+ example: dict,
+ *,
+ input_key: str,
+ prompt_template: str,
+) -> dict:
+ """Apply a configured prompt template to one dataset row."""
+
+ problem = example[input_key]
+ example[input_key] = TEMPLATE_FACTORY[prompt_template](problem)
+ return example
+
+
+def collate_eval_prompt_items(
+ item_list: list[dict],
+ *,
+ prompt_template: str,
+) -> tuple[list[str], list[str], list[str]]:
+ """Collate eval rows into templated prompts, raw problems, and answers."""
+
+ problems = []
+ formatted_problems = []
+ answers = []
+ for item in item_list:
+ problems.append(item["problem"])
+ formatted_problems.append(TEMPLATE_FACTORY[prompt_template](item["problem"]))
+ answers.append(item["answer"])
+ return formatted_problems, problems, answers
+
+
+def build_response_token_prefix_mask(
+ response_masks: torch.Tensor,
+ token_counts: torch.Tensor,
+) -> torch.Tensor:
+ """Select the first ``token_counts`` response tokens in each row."""
+
+ if response_masks.ndim != 2:
+ raise ValueError("response_masks must have shape [batch, seq].")
+ if token_counts.ndim != 1 or token_counts.shape[0] != response_masks.shape[0]:
+ raise ValueError(
+ "token_counts must have shape [batch] matching response_masks."
+ )
+ safe_counts = token_counts.to(
+ device=response_masks.device, dtype=torch.int64
+ ).clamp(min=0)
+ response_positions = response_masks.to(torch.int64).cumsum(dim=1)
+ return response_masks.to(torch.bool) & (response_positions <= safe_counts[:, None])
diff --git a/data/level1_countdown/eval.parquet b/data/level1_countdown/eval.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..269639302bc066326882cfd2fbd83fc19c4d6e6f
--- /dev/null
+++ b/data/level1_countdown/eval.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1da95c1b5997a0c8d804c8bf7b607817b11bb8c7982b3d04bd4c345da7b9c33
+size 8432
diff --git a/data/level1_countdown/train.parquet b/data/level1_countdown/train.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..11b56dfd1de92d5cc41494dfe39a9b5d47cf8951
--- /dev/null
+++ b/data/level1_countdown/train.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00054c212d28ccb103b982cb56de7c381691b91a8ee3af7d984c5e0d57fe74f1
+size 13725
diff --git a/data/level1_graph_coloring/eval.parquet b/data/level1_graph_coloring/eval.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..86ce5d4a2cd9c3dede22c3c572eaaee857bd8395
--- /dev/null
+++ b/data/level1_graph_coloring/eval.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c009576b979ac78b9bf73c80790e0048a75f4c27c400569907f096e18a220fb
+size 13112
diff --git a/data/level1_graph_coloring/train.parquet b/data/level1_graph_coloring/train.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..72697761e47ee5103093980b7f72c7a15f0179dd
--- /dev/null
+++ b/data/level1_graph_coloring/train.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56266083798c5e86223e7705d3ed2b97d22dc8aba56814402a0c09afc9eddf30
+size 22522
diff --git a/data/level1_graph_coloring_unique_answer/eval.parquet b/data/level1_graph_coloring_unique_answer/eval.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..3fdedd706a05b994a07f3efb729e297336a0d5b4
--- /dev/null
+++ b/data/level1_graph_coloring_unique_answer/eval.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:759ba48dbcd3fcd4f000449eed1b9e0ea55319dc9677f5150842180ea95893bf
+size 13015
diff --git a/data/level1_mathir/eval.parquet b/data/level1_mathir/eval.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..f40a5e9033908b3f4e399d5f8044110091c572eb
--- /dev/null
+++ b/data/level1_mathir/eval.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc0280c144e5c9c54624303cd508612dbbb3a0ef130dfee9a42fb8f9413e9902
+size 20812
diff --git a/data/level1_mathir/train.parquet b/data/level1_mathir/train.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..ac95d636e0b9ae0408e3f8cc647b8616c177ec6d
--- /dev/null
+++ b/data/level1_mathir/train.parquet
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diff --git a/provenance/confirmation_report.json b/provenance/confirmation_report.json
new file mode 100644
index 0000000000000000000000000000000000000000..6269793f92edd108cbab8bc7f994eae80028d269
--- /dev/null
+++ b/provenance/confirmation_report.json
@@ -0,0 +1,9950 @@
+{
+ "all_attempts_regraded_with_original_grader": true,
+ "all_five_domains_complete": true,
+ "attempts": 8192,
+ "batches": 128,
+ "completed_development_audit": {
+ "path": "/n/fs/similarity/maxent-grpo/var/artifacts/modebench_level3_v3/development/completed_development_audit.json",
+ "sha256": "ac0facc97db3f0e7ffee9ff536b958893e02c8b5c4eee862ed3ff39161b61b89"
+ },
+ "confirmation_match_verified": true,
+ "created_at": "2026-09-09T14:44:45.630134+00:00",
+ "dataset": {
+ "dataset_root": "/n/fs/similarity/maxent-grpo/var/data/modebench_level3_matched_v3",
+ "fresh_candidate_domains": [
+ "graph_coloring",
+ "python_factors"
+ ],
+ "frozen_recipe_bundle_path": "/n/fs/similarity/maxent-grpo/var/data/modebench_level3_matched_v3/frozen_recipes.json",
+ "frozen_recipe_bundle_sha256": "acad4298da776476717753fab0ab8e97f106c8fcafeea08e5ad8af028fe7247c",
+ "path": "/n/fs/similarity/maxent-grpo/var/data/modebench_level3_matched_v3/identity.json",
+ "retained_domains": [
+ "countdown",
+ "mathir",
+ "pantry"
+ ],
+ "sha256": "890d7697af7789e0ae53c803586ec7f722685b7eb2239643175a1933fa45650d",
+ "split_sizes": {
+ "dev": 128,
+ "eval": 128,
+ "train": 384
+ }
+ },
+ "directory_files": {
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diff --git a/provenance/e119_level2_qwen05b_factorial_20260901.md b/provenance/e119_level2_qwen05b_factorial_20260901.md
new file mode 100644
index 0000000000000000000000000000000000000000..1cb5aaaef42fe9bf623444fb0e8d678870802242
--- /dev/null
+++ b/provenance/e119_level2_qwen05b_factorial_20260901.md
@@ -0,0 +1,82 @@
+# E119: matched Level-2 Qwen-0.5B Dr.GRPO × MaxRL × replay factorial
+
+Date frozen: 2026-09-01, after the second independent frozen-base admission
+run and before any E119 training submission or treatment outcome.
+
+## Question
+
+On the admitted matched Level-2 benchmark, what are the separate and joint
+effects of the finite-rollout binary MaxRL estimator and verified canonical
+replay on correctness and retained semantic support?
+
+## Design
+
+Train Qwen2.5-0.5B-Instruct on all five Level-2 r5 domains under a complete
+two-by-two factorial:
+
+- `drgrpo`: ordinary Dr.GRPO task estimator; compute-matched replay traversal
+ with an exactly zero replay derivative.
+- `replay_drgrpo`: ordinary Dr.GRPO plus the established
+ `verified_likelihood_per_rollout` derivative at alpha 0.10.
+- `maxrl`: binary MaxRL task estimator; compute-matched replay traversal with
+ an exactly zero replay derivative.
+- `replay_maxrl`: binary MaxRL plus the same verified replay derivative.
+
+The cohort is 5 domains × 4 arms × 5 paired seeds (43--47) = 100 new runs.
+No Level-1 endpoint is reused as a Level-2 treatment comparator.
+
+## Frozen benchmark and interface
+
+- Data root: `var/data/modebench_harder_v2_matched_r5`.
+- Identity SHA-256:
+ `2af80f4d31a44482574b314cc37ef84bde48c53ecc2ff78dadf97571f0d73fb2`.
+- Training uses each domain's 384-row `train` split; evaluation uses its
+ untouched 128-row `eval` split. Development rows are never loaded by E119.
+- Submission requires the final and independent-repeat admission reports to
+ admit every domain under both frozen base models.
+- Qwen prompt profiles and target-blind legal syntax match the admitted
+ Qwen-0.5B interface: boxed-direct/no grammar for Graph Coloring;
+ hybrid/domain-legal-v1 for Python Factors, MathIR, and PantryPlan; and
+ hybrid/countdown-legal-v3 for Countdown.
+- The Countdown grammar depends only on the supplied operands, never the
+ target or verifier outcome. Other grammars constrain syntax, not correctness.
+- The identical prompt, grammar, response budget, and verifier apply to every
+ arm within a domain and seed.
+
+## E78/E118-matched schedule
+
+- Frozen Qwen2.5-0.5B-Instruct checkpoint used by E78/E118.
+- 384 training prompts, exactly 8 passes, 3,072 optimizer updates.
+- Group size 16, one PPO epoch, learning rate 2e-7, beta_KL 0,
+ temperature 1, top-p 1.
+- Evaluation and resumable checkpoints every 192 updates, yielding the fixed
+ pass grid 0, 0.5, 1.0, ..., 8.0.
+- Sampled evaluation uses pass@8/mode coverage with four registered draws.
+- Replay capacity 16 and one deterministic global replay group per update.
+- Semantic-MaxEnt, canonical-bank entropy, token entropy, xDr, DAPO, RLEP,
+ DIAYN, and counterfactual proposal objectives are disabled in all arms.
+
+## Estimands
+
+Within each domain and seed:
+
+1. `replay_drgrpo - drgrpo`: replay effect under Dr.GRPO.
+2. `maxrl - drgrpo`: task-estimator effect without replay.
+3. `replay_maxrl - maxrl`: replay effect under MaxRL.
+4. Difference in differences: replay/MaxRL interaction.
+
+Primary terminal endpoints are sampled pass@8 and distinct correct modes@8.
+Secondary endpoints are greedy accuracy, sampled mean correctness@8, excess
+multiplicity, retained discovered support, and complete pass-0--8 trajectory
+AUC. Report all five paired seeds per domain; do not select checkpoints or
+pool domains into one confirmatory effect.
+
+## Integrity and stopping
+
+All 100 jobs are submitted held from one hash-bound source/ops snapshot. They
+are released only after exact scheduler-environment audit and immutable-ledger
+publication. Existing run directories, malformed data identities, non-admitted
+reports, objective leakage, missing syntax profiles, non-finite losses, or
+missing pass-8 endpoints fail closed. Infrastructure interruption may resume
+only the same scientific cell from its exact checkpoint and replay state.
+
diff --git a/provenance/e122_level3_factorial_20260909.md b/provenance/e122_level3_factorial_20260909.md
new file mode 100644
index 0000000000000000000000000000000000000000..2d401ed9328f62690878a1fc47ad4ba852b1f5d6
--- /dev/null
+++ b/provenance/e122_level3_factorial_20260909.md
@@ -0,0 +1,142 @@
+# E122: Level-3 Dr.GRPO / ReplayDr.GRPO / MaxRL / ReplayMaxRL factorial
+
+Prepared September 9, 2026, before E122 treatment training or treatment outcomes.
+E122 uses Qwen2.5-0.5B-Instruct for all 100 cells, frozen revision
+7ae557604adf67be50417f59c2c2f167def9a775. This preserves the model in the user's
+latest request to repeat E119 under the same conditions on Level3. It records
+the literal-request interpretation stated before training, not a separate
+explicit model-choice answer. Qwen2.5-3B remains the Level3 calibration model.
+No treatment outcome informs model selection. The CLI requires an explicit
+model argument, and preparation/submission/verification enforce the registered
+0.5B choice; the generic 3B interface remains available for read-only previews.
+
+## Scientific design
+
+This is a new complete factorial: Graph Coloring, Countdown, Python Factors,
+MathIR and PantryPlan × four arms × paired seeds 43–47 = 100 new runs.
+Every arm starts from the same base model. No prior treatment endpoint is reused.
+
+The arms are ordinary Dr.GRPO, ReplayDr.GRPO, binary finite-rollout MaxRL and
+ReplayMaxRL. Replay uses verified_likelihood_per_rollout at alpha 0.10,
+capacity 16 and one deterministic global group per update, with no bootstrap.
+Both arms without a replay derivative traverse the same replay machinery in
+compute-only mode. Semantic-MaxEnt, canonical-bank entropy, token entropy, xDr,
+DAPO, RLEP, DIAYN and counterfactual proposal objectives are disabled.
+
+The schedule matches the **effective** E119 runtime: 384 training prompts,
+eight passes, 3,072 optimizer updates; 16 fresh samples per prompt, one PPO
+epoch, constant learning rate 2e-7, zero warmup, Adam betas (0.9, 0.95), weight
+decay zero, gradient norm bound 1, KL beta zero, temperature 1 and top-p 1.
+Rollout batch is one prompt, train batch 16 responses; learner microbatch is
+one response except Pantry's inherited four. Evaluation batch remains 64.
+Model scaling does not import E80's different learning rate or cosine schedule.
+
+Input, response and model-length limits remain 1024, 192 and 2048 tokens.
+Native Qwen Level-2 r5 prompt profiles and target-blind legal syntax are the
+same interfaces used by Level-3 calibration: Graph uses qwen_boxed/no grammar;
+Countdown uses qwen_level2_countdown/countdown_legal_v3; Python, MathIR and
+Pantry use their qwen_level2 profiles/domain_legal_v1. Ordinary generation is
+explicit in all domains: canonical_action_task=none and canonical graph,
+learner action and fixed-shape sampling are disabled. This incorporates E119's
+documented correction of an accidentally inherited Pantry support-mask policy.
+
+Evaluation uses greedy accuracy plus pass@8 and correct semantic modes@8 with
+four fixed draws, temperature/top-p 1. Evaluation seeds, held constant across
+paired training seeds, are graph 610100, Countdown 610200, Python 610300,
+MathIR 610400 and Pantry 76299. Evaluations occur every 96 updates on the
+pass grid 0, 0.25, 0.5, …, 8. E119's written launcher requested 192, but its
+runtime capped that interval to 96; E122 preserves the realized cadence.
+
+Optimizer/model recovery checkpoints occur every 192 updates except Pantry's
+operationally amended 96. Retain one complete rolling checkpoint, preserving
+its predecessor until replacement finishes. Export one terminal model and
+retire resumable state normally after successful completion. Evaluation is
+independent of storage; no checkpoint or outcome selection is permitted.
+
+Primary terminal endpoints are sampled pass@8 and distinct correct modes@8.
+Report all five paired seeds separately by domain. Contrasts are ReplayDr.GRPO
+minus Dr.GRPO, MaxRL minus Dr.GRPO, ReplayMaxRL minus MaxRL, and the factorial
+difference in differences. Secondary endpoints and full-trajectory AUC retain
+E119's definitions. Do not pool domains into one confirmatory effect.
+
+## Benchmark provenance and admission
+
+Use only train and eval under var/data/modebench_level3_matched_v3; never load
+development rows for training or treatment evaluation. Identity SHA-256 is
+890d7697af7789e0ae53c803586ec7f722685b7eb2239643175a1933fa45650d.
+The original identity remains byte-identical with its historical
+pending_fresh_candidate_confirmation decision. A separate completed canonical
+V3 confirmation report supplies admission; no metadata is relabeled.
+
+Preparation and submission require the canonical auditor to authenticate the
+complete all-five report, its underlying receipts, original-grader replay,
+registration, dataset inventory and frozen files. Every domain must pass the
+registered absolute pass@1/pass@8 tolerances of 0.04/0.08.
+
+V3 is adaptive second-round matching against fixed, measured historical
+Level-1 benchmark values, not five fresh comparisons from one round. Graph
+and Python use fresh V3 candidate development fits and fresh confirmation;
+Countdown, MathIR and Pantry retain V2 split bytes and completed evidence.
+Historical L1 measurements remain fixed references. Passing these numerical
+gates does not establish statistical equivalence or remove reference sampling
+uncertainty. No E122 outcome informs admission or model selection.
+
+## Operational resources, integrity and release
+
+All arms share the same resource route: non-PVL A6000 nodes205/206/207 and
+compatible A100 node302, account allcs/partition lowprio, one GPU, 36-hour
+walltime and nice=0. The site-assigned QoS is medium and is audited; E122
+does not request a QoS override. Node208 is excluded while drained for overheating.
+The lower-priority partition permits owner preemption and same-job requeue;
+no uninterrupted allocation or predicted start time is guaranteed. The same
+checkpoint, replay-state and watchdog recovery contract remains enabled.
+The release controller reserves a slot and full peak storage for every released
+unfinished cell, including preempted, requeued and failed cells, until verified
+successful completion or explicit reconciliation. Failed cells pause new release.
+
+Read-only sbatch --test-only comparisons on September 9 accepted this exact
+route and predicted September 10 for both model resource profiles, compared
+with September 17–18 on cs. These are scheduler estimates, not submissions.
+The site submission hook routes allcs jobs longer than 60 minutes to cs unless
+exactly lowprio is requested. The earlier owner-borrowing amendment
+(paper/preregistration/campaign_owner_borrowing_20260908.md) remains unapplied:
+its multipartition request is unsupported; E122 uses the supported single
+lowprio route. This prospective operational choice changes no model, objective,
+batch, learning rate, evaluation cadence, checkpoint interval or endpoint.
+The registered Qwen-0.5B campaign uses eight CPUs, 128 GiB for Countdown,
+96 GiB for Pantry and 64 GiB otherwise. These resource choices incorporate
+observed E119 host-memory throttling and Pantry failures on 24-GB GPUs; they
+change no objective or batch. The generic read-only 3B preview uses 16 CPUs,
+128 GiB and optimizer/activation offload, while preserving evaluation batch 64;
+it does not authorize a 3B E122 submission.
+
+A reviewed draft binds current learner/source bytes and E119's healthy frozen
+operational scripts. Preparation publishes a new snapshot, changing only its
+operational E122 run guards and correct job-specific watchdog log path. Existing
+snapshots, calibration files and campaigns remain untouched. Two-hour inactivity,
+one-hour startup grace and a twelve-restart watchdog budget are preserved.
+
+Submission is separate from release. Each of 100 jobs is submitted held, with
+an exclusive claim and per-cell intent recorded before sbatch. Every result is
+preserved before checking it. An ambiguous result or interruption forbids
+automatic retry; known jobs remain held for evidence-based reconciliation.
+The full environment, scheduler resources and identity are audited twice before
+the actual-job ledger is published. This launcher never releases a job.
+
+The separately armed controller must authenticate admission, the registered
+0.5B model, immutable plan/snapshot, all 100 held allocations and a live storage
+budget. Recorded 0.5B model/optimizer files total approximately 6.44 GiB per
+rolling checkpoint and 12.89 GiB during atomic replacement. Reserve 16 GiB
+peak per unfinished released cell and 1.25 GiB per terminal run, or 125 GiB
+for all 100 final runs. The controller cap is four released unfinished cells,
+with 64 GiB additional filesystem headroom. Pending/requeued/failed released
+cells retain their peak reservation and slot until verified success or explicit
+reconciliation. Account for concurrent campaign writes before each release.
+
+The size measurements are immutable recorded observations, not persistent pins
+to old checkpoint files; prior campaigns may retire their own checkpoints
+normally. The generic 3B preview records 84 GiB peak and 6.25 GiB terminal
+reservations, but no 3B campaign is authorized here. No existing artifact
+cleanup is authorized by this protocol. Normal successful-run retention
+remains enabled; resource admission must never change scientific stopping
+or select on efficacy.
diff --git a/provenance/pantry_ingredient_source.json b/provenance/pantry_ingredient_source.json
new file mode 100644
index 0000000000000000000000000000000000000000..5971aafb29ce14ec3ae59331c5772ea52ecd2b7f
--- /dev/null
+++ b/provenance/pantry_ingredient_source.json
@@ -0,0 +1,1847 @@
+{
+ "decision_boundary": "Nutrient extraction, units, precedence, source rows, and hashes are complete. Dietary tags and named food states require final manual review before policy sampling.",
+ "generated_at": "2026-07-29T19:23:27.699226+00:00",
+ "ingredient_count": 16,
+ "ingredient_table_sha256": "f49560f417cf6b1e01f901aba4b3fa4e73744b3f6d0d8a50b7042afd950678e2",
+ "ingredients": [
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "229",
+ "fiber_g": "5.4",
+ "protein_g": "7.35",
+ "sodium_mg": "438"
+ },
+ "curated_row_sha256": "ff52ff78ff7a3a049701a300d621e3b5d9fa619ffa3ca706178bd6d40717efc0",
+ "description": "Hummus, commercial",
+ "fdc_id": 321358,
+ "id": "hummus",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "229",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "229",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "7.35",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "7.35",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "5.4",
+ "data_points": 6,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "5.4",
+ "data_points": 6,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "438",
+ "data_points": 11,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "438",
+ "data_points": 11,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "4/1/2019",
+ "source_row_sha256": "afc2edce700ce20f01c653806cc6a6baaf11d9361e4f9f7696265fb2fb3a3b5b",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "27.0",
+ "fiber_g": "2.1",
+ "protein_g": "0.83",
+ "sodium_mg": "6.0"
+ },
+ "curated_row_sha256": "19c3b165dc80dce6888c21f8530690a0b721130b48cbccc644f23432143a6db6",
+ "description": "Tomatoes, grape, raw",
+ "fdc_id": 321360,
+ "id": "grape_tomatoes",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "27.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "27.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "0.83",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "0.83",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "2.1",
+ "data_points": 3,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "2.1",
+ "data_points": 3,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "6.0",
+ "data_points": 6,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "6.0",
+ "data_points": 6,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "4/1/2019",
+ "source_row_sha256": "8a897aa2957fe7f97f85c16638702b18044fa51fb3183099f99a7e4b8e785576",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "35.0",
+ "fiber_g": "4.1",
+ "protein_g": "2.92",
+ "sodium_mg": "53.0"
+ },
+ "curated_row_sha256": "24bbf113304c598fb907f646e4661101f90153a13fc1b39d622c7518be545378",
+ "description": "Kale, raw",
+ "fdc_id": 323505,
+ "id": "kale",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "35.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "35.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "2.92",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "2.92",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "4.1",
+ "data_points": 2,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "4.1",
+ "data_points": 2,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "53.0",
+ "data_points": 5,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "53.0",
+ "data_points": 5,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "4/1/2019",
+ "source_row_sha256": "885cb1260eb2567e590d35dfdd3f72433ce57d856df02d1dac9249eb68ca5e87",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
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+ "fiber_g": "1.56",
+ "protein_g": "2.85",
+ "sodium_mg": "111"
+ },
+ "curated_row_sha256": "ed9d7dc1fd4ea3524664cbcbe5ee3ab9150e59dd4c2f90f5cc2a31aa225a8f68",
+ "description": "Spinach, baby",
+ "fdc_id": 1999632,
+ "id": "baby_spinach",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "20.7",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ },
+ {
+ "amount": "26.6",
+ "data_points": null,
+ "name": "Energy (Atwater General Factors)",
+ "number": "957",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "20.7",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "2.85",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "2.85",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "1.56",
+ "data_points": 8,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "1.56",
+ "data_points": 8,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "111",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "111",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "10/28/2021",
+ "source_row_sha256": "dcd4afd0c1edbb13a977ed84bf93b5b776aa6d0068bed04fca4b948ef60c7c8f",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "45.0",
+ "fiber_g": "3.1",
+ "protein_g": "0.941",
+ "sodium_mg": "86.6"
+ },
+ "curated_row_sha256": "b7bea8ebf8ba9d5345d8e014503e11f9525ea3b25037c4110d84f9e2ec4c9b0a",
+ "description": "Carrots, mature, raw",
+ "fdc_id": 2258586,
+ "id": "carrots",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "45.0",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ },
+ {
+ "amount": "48.0",
+ "data_points": null,
+ "name": "Energy (Atwater General Factors)",
+ "number": "957",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "45.0",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "0.941",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "0.941",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "3.1",
+ "data_points": 8,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "3.1",
+ "data_points": 8,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "86.6",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "86.6",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "4/28/2022",
+ "source_row_sha256": "0666032b8a45daa701d3504bc102c13ad93ed47574f9764519d80fe838e72ef6",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "31.0",
+ "fiber_g": "2.4",
+ "protein_g": "2.57",
+ "sodium_mg": "36.0"
+ },
+ "curated_row_sha256": "d7244e0f7283d859f5deecc295ae4f09b1f82285701ecd26b35a47673a2b05c0",
+ "description": "Broccoli, raw",
+ "fdc_id": 747447,
+ "id": "broccoli",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "31.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "31.0",
+ "data_points": null,
+ "name": "Energy",
+ "number": "208",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "2.57",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "2.57",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "2.4",
+ "data_points": 4,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "2.4",
+ "data_points": 4,
+ "name": "Fiber, total dietary",
+ "number": "291",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "36.0",
+ "data_points": 7,
+ "name": "Sodium, Na",
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+ "observed_candidates": [
+ {
+ "amount": "0.0",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "0.0",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "4/20/2023",
+ "source_row_sha256": "fc4878f32b22b8332f6f2707be685520777d67066ea2ea36ed222e63c76f1174",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "115",
+ "fiber_g": "6.69",
+ "protein_g": "6.91",
+ "sodium_mg": "218"
+ },
+ "curated_row_sha256": "bcbbb802002a0ac32542a1ac97650991dd1163e426b9a4fe2ceb07d5f9d4741b",
+ "description": "Beans, black, canned, sodium added, drained and rinsed",
+ "fdc_id": 2644285,
+ "id": "black_beans_canned",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "115",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ },
+ {
+ "amount": "118",
+ "data_points": null,
+ "name": "Energy (Atwater General Factors)",
+ "number": "957",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "115",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "6.91",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "6.91",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "6.69",
+ "data_points": 8,
+ "name": "Total dietary fiber (AOAC 2011.25)",
+ "number": "293",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "6.69",
+ "data_points": 8,
+ "name": "Total dietary fiber (AOAC 2011.25)",
+ "number": "293",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "218",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "218",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "10/26/2023",
+ "source_row_sha256": "b2e32066eeb463137d35b84670e3c26504c5fa62f32734946a8b59e404d4e2e6",
+ "tags": []
+ },
+ {
+ "attributes_per_100g": {
+ "energy_kcal": "133",
+ "fiber_g": "5.92",
+ "protein_g": "7.02",
+ "sodium_mg": "202"
+ },
+ "curated_row_sha256": "2b1662f6b2ba95f08c176c1cb052216eea282e122f981dc88fb7a796997cbd2b",
+ "description": "Chickpeas (garbanzo beans, bengal gram), canned, sodium added, drained and rinsed",
+ "fdc_id": 2644288,
+ "id": "chickpeas_canned",
+ "nutrient_audit": [
+ {
+ "attribute": "energy_kcal",
+ "observed_candidates": [
+ {
+ "amount": "133",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ },
+ {
+ "amount": "137",
+ "data_points": null,
+ "name": "Energy (Atwater General Factors)",
+ "number": "957",
+ "unit": "kcal"
+ }
+ ],
+ "precedence": [
+ "208",
+ "958",
+ "957"
+ ],
+ "selected": {
+ "amount": "133",
+ "data_points": null,
+ "name": "Energy (Atwater Specific Factors)",
+ "number": "958",
+ "unit": "kcal"
+ }
+ },
+ {
+ "attribute": "protein_g",
+ "observed_candidates": [
+ {
+ "amount": "7.02",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "203"
+ ],
+ "selected": {
+ "amount": "7.02",
+ "data_points": null,
+ "name": "Protein",
+ "number": "203",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "fiber_g",
+ "observed_candidates": [
+ {
+ "amount": "5.92",
+ "data_points": 8,
+ "name": "Total dietary fiber (AOAC 2011.25)",
+ "number": "293",
+ "unit": "g"
+ }
+ ],
+ "precedence": [
+ "293",
+ "291"
+ ],
+ "selected": {
+ "amount": "5.92",
+ "data_points": 8,
+ "name": "Total dietary fiber (AOAC 2011.25)",
+ "number": "293",
+ "unit": "g"
+ }
+ },
+ {
+ "attribute": "sodium_mg",
+ "observed_candidates": [
+ {
+ "amount": "202",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ ],
+ "precedence": [
+ "307"
+ ],
+ "selected": {
+ "amount": "202",
+ "data_points": 8,
+ "name": "Sodium, Na",
+ "number": "307",
+ "unit": "mg"
+ }
+ }
+ ],
+ "publication_date": "10/26/2023",
+ "source_row_sha256": "d369e999d09bc5e73742bcfd466350874a3671f6c96f9d916c6cecff5787f08f",
+ "tags": []
+ }
+ ],
+ "nutrient_precedence": {
+ "energy_kcal": [
+ "208",
+ "958",
+ "957"
+ ],
+ "fiber_g": [
+ "293",
+ "291"
+ ],
+ "protein_g": [
+ "203"
+ ],
+ "sodium_mg": [
+ "307"
+ ]
+ },
+ "schema_version": "pantry-plan-ingredient-curation-v1",
+ "source": {
+ "archive_name": "FoodData_Central_foundation_food_json_2026-04-30.zip",
+ "archive_sha256": "186e988ec542e913f51ef62b86a47758e8cdd0d1dc3889e7b055581f3c09c77a",
+ "dataset": "USDA FoodData Central Foundation Foods",
+ "food_object_rows": 363,
+ "license": "CC0 1.0 / U.S. public-domain data",
+ "null_placeholder_rows": 32,
+ "physical_rows": 395,
+ "release": "April 2026",
+ "url": "https://fdc.nal.usda.gov/download-datasets/"
+ },
+ "status": "machine_audit_complete_manual_review_pending"
+}
diff --git a/provenance/source_code/ops/check_paper_domain_prompts.py b/provenance/source_code/ops/check_paper_domain_prompts.py
new file mode 100644
index 0000000000000000000000000000000000000000..89910c7e049b6d80a276bf3eaa98c9c125a18969
--- /dev/null
+++ b/provenance/source_code/ops/check_paper_domain_prompts.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""Bind the paper's domain-prompt appendix to the frozen ModeBench splits.
+
+The appendix in ``paper/main.tex`` reproduces, verbatim, the exact prompt the
+policy receives in every reported domain: the dataset ``problem`` string of the
+first row of that cohort's frozen evaluation split, wrapped by the same chat
+template the launcher exported. This script re-renders those prompts from the
+datasets and fails closed when the typeset appendix has drifted from them.
+
+Run ``--emit`` to print the LaTeX block instead of checking it.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+import sys
+import textwrap
+
+ROOT = Path(os.environ.get("OAT_ZERO_REPO_ROOT", Path(__file__).resolve().parents[1]))
+SRC = Path(os.environ.get("OAT_ZERO_SOURCE_ROOT", ROOT / "src"))
+if str(SRC) not in sys.path:
+ sys.path.insert(0, str(SRC))
+
+from oat_drgrpo.templates import TEMPLATE_FACTORY # noqa: E402
+
+PAPER = ROOT / "paper/main.tex"
+
+# (label, latex title, data root, prompt template, launcher that exported it)
+DOMAINS = (
+ (
+ "graph",
+ "Graph coloring",
+ "var/data/graph_coloring_modebench_v2",
+ "qwen_boxed",
+ "ops/exp_scaling/launch_e71_scale384_05b.sh",
+ ),
+ (
+ "countdown",
+ "Countdown",
+ "var/data/exact_countdown_easy3_probe",
+ "qwen_boxed",
+ "ops/exp_scaling/launch_e70_clean_stage_a_05b.sh",
+ ),
+ (
+ "python",
+ "Python factors",
+ "var/data/python_factor_modebench_v1",
+ "qwen_boxed",
+ "ops/exp_scaling/launch_e70_clean_stage_a_05b.sh",
+ ),
+ (
+ "mathir",
+ "MathIR action menu",
+ "var/data/mathir_action_menu_v1",
+ "qwen_boxed",
+ "ops/exp_scaling/launch_e70_clean_stage_a_05b.sh",
+ ),
+ (
+ "pantry",
+ "PantryPlan",
+ "var/data/pantry_plan_modebench_v2",
+ "qwen_pantry_support_mask",
+ "ops/exp_scaling/launch_e71_scale384_05b.sh",
+ ),
+)
+
+BEGIN_MARKER = "% BEGIN generated by ops/check_paper_domain_prompts.py"
+END_MARKER = "% END generated by ops/check_paper_domain_prompts.py"
+
+# The vendored fancyvrb (v3.7) predates breaklines=true, so wrapping happens
+# here. Continuation lines carry a two-space indent and are recoverable: a
+# wrapped logical line is exactly the continuation pieces rejoined by single
+# spaces. The round trip is asserted below, so no prompt character is lost.
+WRAP_COLUMNS = 90
+CONTINUATION_INDENT = " "
+
+
+def _wrap(line: str) -> list[str]:
+ if len(line) <= WRAP_COLUMNS:
+ return [line]
+ pieces = textwrap.wrap(
+ line,
+ width=WRAP_COLUMNS,
+ subsequent_indent=CONTINUATION_INDENT,
+ break_long_words=False,
+ break_on_hyphens=False,
+ replace_whitespace=False,
+ drop_whitespace=True,
+ )
+ rejoined = " ".join(
+ piece[len(CONTINUATION_INDENT) :] if index else piece
+ for index, piece in enumerate(pieces)
+ )
+ if rejoined != line:
+ raise ValueError(f"prompt line is not recoverable after wrapping: {line!r}")
+ return pieces
+
+
+def _first_eval_prompt(root: Path, template: str) -> tuple[str, int, int]:
+ from datasets import load_from_disk
+
+ evaluation = load_from_disk(str(root / "eval"))
+ split = next(iter(evaluation.keys()))
+ train = load_from_disk(str(root / "train"))
+ train_split = next(iter(train.keys()))
+ problem = evaluation[split][0]["problem"]
+ return (
+ TEMPLATE_FACTORY[template](problem),
+ len(train[train_split]),
+ len(evaluation[split]),
+ )
+
+
+def render() -> str:
+ lines: list[str] = [BEGIN_MARKER]
+ for label, title, root, template, launcher in DOMAINS:
+ rendered, train_rows, eval_rows = _first_eval_prompt(ROOT / root, template)
+ if rendered.endswith("\n"):
+ rendered = rendered[:-1]
+ lines.append("")
+ lines.append(f"\\subsection{{{title}}}")
+ lines.append(f"\\label{{app:prompt-{label}}}")
+ lines.append("")
+ lines.append(
+ f"\\noindent Template \\texttt{{{template.replace('_', chr(92) + '_')}}}; "
+ f"{train_rows} training and {eval_rows} evaluation\\linebreak rows in "
+ f"\\path{{{root}}}."
+ )
+ lines.append("")
+ lines.append(
+ "\\begin{Verbatim}[fontsize=\\scriptsize,frame=single,framesep=4pt,"
+ "rulecolor=\\color{black!25}]"
+ )
+ for source_line in rendered.split("\n"):
+ lines.extend(_wrap(source_line))
+ lines.append("\\end{Verbatim}")
+ lines.append("")
+ lines.append(END_MARKER)
+ return "\n".join(lines) + "\n"
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument(
+ "--emit",
+ action="store_true",
+ help="print the LaTeX block instead of checking the typeset appendix",
+ )
+ args = parser.parse_args()
+
+ block = render()
+ if args.emit:
+ sys.stdout.write(block)
+ return 0
+
+ text = PAPER.read_text(encoding="utf-8")
+ if BEGIN_MARKER not in text or END_MARKER not in text:
+ print(f"[prompts] missing generated block markers in {PAPER}", file=sys.stderr)
+ return 1
+ start = text.index(BEGIN_MARKER)
+ end = text.index(END_MARKER) + len(END_MARKER) + 1
+ typeset = text[start:end]
+ if typeset != block:
+ print(
+ "[prompts] paper/main.tex domain prompts drifted from the frozen "
+ "splits; regenerate with --emit",
+ file=sys.stderr,
+ )
+ return 1
+ print(f"[prompts] {len(DOMAINS)} domain prompts match the frozen splits")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/provenance/source_code/ops/evaluate_modebench_level2_viability.py b/provenance/source_code/ops/evaluate_modebench_level2_viability.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b5b4ddffb6a074f08ab9ed5ae824aaebd3aaf10
--- /dev/null
+++ b/provenance/source_code/ops/evaluate_modebench_level2_viability.py
@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+"""Paired frozen-model Level-1/Level-2 ModeBench pass@8 evaluation."""
+from __future__ import annotations
+import argparse,hashlib,itertools,json,os,re,sys,tempfile
+from datetime import datetime,timezone
+from pathlib import Path
+from typing import Any
+ROOT=Path(__file__).resolve().parents[1]
+sys.path.insert(0,str(ROOT/'src'))
+
+def sha(value:Any)->str:
+ return hashlib.sha256(json.dumps(value,sort_keys=True,separators=(',',':')).encode()).hexdigest()
+def file_sha(path:Path)->str:
+ h=hashlib.sha256()
+ with path.open('rb') as f:
+ for chunk in iter(lambda:f.read(1<<20),b''): h.update(chunk)
+ return h.hexdigest()
+def atomic(path:Path,payload:Any)->None:
+ path.parent.mkdir(parents=True,exist_ok=True)
+ fd,tmp=tempfile.mkstemp(prefix='.'+path.name+'.',dir=path.parent)
+ try:
+ with os.fdopen(fd,'w') as f: json.dump(payload,f,indent=2,sort_keys=True); f.write('\n')
+ os.replace(tmp,path)
+ except BaseException:
+ os.unlink(tmp); raise
+
+def args():
+ ap=argparse.ArgumentParser(description=__doc__)
+ ap.add_argument('--domain',required=True)
+ ap.add_argument('--model',type=Path,required=True)
+ ap.add_argument('--model-label',required=True,choices=('qwen-0.5b','falcon-1b'))
+ ap.add_argument('--level2-root',type=Path,default=ROOT/'var/data/modebench_harder_v2_matched')
+ ap.add_argument('--output',type=Path,required=True)
+ ap.add_argument('--seed',type=int,required=True)
+ ap.add_argument('--temperature',type=float,default=1.0)
+ ap.add_argument('--top-p',type=float,default=1.0)
+ ap.add_argument('--max-tokens',type=int,default=192)
+ ap.add_argument('--max-model-len',type=int,default=1024)
+ ap.add_argument('--batch-size',type=int,default=8)
+ ap.add_argument('--dtype',choices=('float16','bfloat16'),default='float16')
+ ap.add_argument('--prompt-profile',choices=('boxed_direct_v1','deliberate_domain_v2','structured_solver_v3','hybrid_solver_v4','countdown_fewshot_v5','countdown_shallow_v6'),default='boxed_direct_v1')
+ ap.add_argument('--row-limit',type=int,default=0,help='development-only calibration limit; zero means all 128 rows')
+ ap.add_argument('--syntax-profile',choices=('none','pantry_legal_v1','domain_legal_v1','domain_legal_v2','countdown_legal_v3'),default='none')
+ return ap.parse_args()
+
+def prompt_messages(domain:str,problem:str,profile:str)->list[dict[str,str]]:
+ if profile=='countdown_shallow_v6':
+ if domain!='countdown':
+ raise ValueError('countdown_shallow_v6 is Countdown-only')
+ system=('Use every supplied number exactly once and hit the target exactly. '
+ 'First test permutations of paired products a*b+c*d or a*b-c*d, '
+ 'paired sums (a+b)*(c+d) or (a+b)*(c-d), and one product '
+ 'a*b followed by adding or subtracting c and d. '
+ 'Return only one fully parenthesized expression inside \\boxed{}.')
+ return [{'role':'system','content':system},{'role':'user','content':problem}]
+ if profile=='countdown_fewshot_v5':
+ if domain!='countdown':
+ raise ValueError('countdown_fewshot_v5 is Countdown-only')
+ system=('Solve the arithmetic target exactly. Use every supplied number exactly once. '
+ 'Return only one fully parenthesized expression inside \\boxed{}.')
+ return [
+ {'role':'system','content':system},
+ {'role':'user','content':'Using the numbers [2, 3, 4], create an arithmetic expression that equals 14. Use each given number exactly once.'},
+ {'role':'assistant','content':'\\boxed{(2 + (3 * 4))}'},
+ {'role':'user','content':'Using the numbers [2, 3, 4, 5], create an arithmetic expression that equals 26. Use each given number exactly once.'},
+ {'role':'assistant','content':'\\boxed{((2 * 3) + (4 * 5))}'},
+ {'role':'user','content':problem},
+ ]
+ if profile=='boxed_direct_v1':
+ system='Return only the final answer inside \\boxed{}. Do not explain.'
+ return [{'role':'system','content':system},{'role':'user','content':problem}]
+ deliberate={
+ 'countdown':'Systematically combine every supplied number exactly once using +, -, *, /, and parentheses. Check the exact target before answering.',
+ 'python_factors':'Construct one allowed lambda expression. Test small divisors with nested conditional expressions, for example 2 if n % 2 == 0 else 3 if n % 3 == 0 else 5, but adapt the tests to every listed case.',
+ 'mathir':'Execute candidate menu actions exactly on both sides, simplify after each action, and check that the final state isolates x. Return action IDs, not x.',
+ 'pantry':'Silently enumerate allowed stepped quantities for 2 to 4 non-forbidden ingredients, total every nutrient exactly, and check all bounds.',
+ 'graph_coloring':'Check every edge after assigning the hidden vertices.',
+ }
+ structured={
+ 'countdown':'Search systematically over pairwise combinations until every number is used exactly once. Verify the arithmetic and output exactly the boxed expression.',
+ 'python_factors':'Output exactly a boxed lambda. A reliable form is d1 if n == c1 else d2 if n == c2 else d3 if n == c3 else d4, choosing each di as a proper divisor of ci.',
+ 'mathir':'Use algebraic isolation: move the right-side x term left, remove the left constant, then divide by the combined coefficient. Match those operations to the shuffled menu IDs.',
+ 'pantry':'Prefer allowed high-energy/protein, very-low-sodium ingredients, especially seeds or oats. Choose stepped amounts, check every bound, and output 2 to 4 ingredient_id=grams pairs.',
+ 'graph_coloring':'Check every edge after assigning the hidden vertices.',
+ }
+ if profile=='deliberate_domain_v2':
+ guidance=deliberate[domain]
+ elif profile=='structured_solver_v3':
+ guidance=structured[domain]
+ else:
+ guidance={
+ 'countdown':deliberate['countdown']+' Output exactly the boxed expression.',
+ 'python_factors':deliberate['python_factors']+' You may instead dispatch on each listed value. Output exactly the boxed lambda.',
+ 'mathir':structured['mathir'],
+ 'pantry':structured['pantry'],
+ 'graph_coloring':deliberate['graph_coloring'],
+ }[domain]
+ system=('Solve the executable constraint problem carefully. You may reason briefly, but end with exactly one final answer inside \\boxed{}. '+guidance)
+ return [{'role':'system','content':system},{'role':'user','content':problem}]
+
+def countdown_legal_choices(row:dict)->list[str]:
+ """Enumerate syntax-legal expressions without consulting the target or verifier."""
+ numbers=tuple(str(int(x)) for x in json.loads(str(row['answer']))['numbers'])
+ operators=('+','-','*','/')
+ choices=set()
+ def trees(values,ops):
+ if len(values)==1:
+ return (values[0],)
+ out=[]
+ for split in range(1,len(values)):
+ for left in trees(values[:split],ops[:split-1]):
+ for right in trees(values[split:],ops[split:]):
+ out.append(f'({left} {ops[split-1]} {right})')
+ return tuple(out)
+ for values in set(itertools.permutations(numbers)):
+ for ops in itertools.product(operators,repeat=len(values)-1):
+ choices.update(f'\\boxed{{{expression}}}' for expression in trees(values,ops))
+ return sorted(choices)
+
+def countdown_legal_regex(row:dict)->str:
+ """Compact target-blind regex for every operand permutation and tree shape."""
+ numbers=tuple(str(int(x)) for x in json.loads(str(row['answer']))['numbers'])
+ operator=r'[+*/-]'
+ def trees(values):
+ if len(values)==1:
+ return (re.escape(values[0]),)
+ out=[]
+ for split in range(1,len(values)):
+ for left in trees(values[:split]):
+ for right in trees(values[split:]):
+ out.append(r'\('+left+' '+operator+' '+right+r'\)')
+ return tuple(out)
+ expressions=set()
+ for values in set(itertools.permutations(numbers)):
+ expressions.update(trees(values))
+ return r'\\boxed\{(?:'+'|'.join(sorted(expressions))+r')\}'
+
+def sampling_params(a,domain:str,row:dict):
+ import vllm
+ guided=None
+ if a.syntax_profile=='countdown_legal_v3' and domain=='countdown':
+ from vllm.sampling_params import GuidedDecodingParams
+ guided=GuidedDecodingParams(regex=countdown_legal_regex(row))
+ elif a.syntax_profile in ('pantry_legal_v1','domain_legal_v1','domain_legal_v2') and domain=='pantry':
+ from vllm.sampling_params import GuidedDecodingParams
+ spec=json.loads(str(row['answer']))
+ alternatives=[]
+ for ingredient in spec['ingredients']:
+ ident=str(ingredient['id'])
+ minimum=int(ingredient['min_if_used_g'])
+ available=int(ingredient['available_g'])
+ step=int(ingredient['step_g'])
+ for grams in range(minimum,available+1,step):
+ alternatives.append(f'{ident}={grams}')
+ atom='(?:'+'|'.join(re.escape(x) for x in alternatives)+')'
+ regex=r'\\boxed\{'+atom+'(?:;'+atom+r'){1,3}\}'
+ guided=GuidedDecodingParams(regex=regex)
+ elif a.syntax_profile in ('domain_legal_v1','domain_legal_v2'):
+ from vllm.sampling_params import GuidedDecodingParams
+ boxed={
+ 'countdown':r'\\boxed\{[0-9 +*/().-]+\}',
+ 'python_factors':r'\\boxed\{lambda n: [A-Za-z0-9 _%<>=!+*/().-]+\}',
+ 'mathir':r'\\boxed\{[A-F](?:;[A-F]){0,3}\}',
+ }
+ optional={
+ 'countdown':r'(?:\\boxed\{[0-9 +*/().-]+\}|[0-9][0-9 +*/().-]*)',
+ 'python_factors':boxed['python_factors'],
+ 'mathir':r'(?:\\boxed\{[A-F](?:;[A-F]){0,3}\}|[A-F](?:;[A-F]){0,3})',
+ }
+ regex=(boxed if a.syntax_profile=='domain_legal_v1' else optional).get(domain)
+ if regex is not None:
+ guided=GuidedDecodingParams(regex=regex)
+ return vllm.SamplingParams(n=8,temperature=a.temperature,top_p=a.top_p,max_tokens=a.max_tokens,seed=a.seed,guided_decoding=guided)
+
+def main():
+ a=args()
+ if a.output.exists(): raise FileExistsError(f'fresh receipt required: {a.output}')
+ import vllm
+ from datasets import load_from_disk
+ from oat_drgrpo.math_grader import validated_modebench_outcome_key
+ identity=json.loads((a.level2_root/'identity.json').read_text())
+ if identity.get('decision')!='structurally_admitted_pending_frozen_base_model_viability':
+ raise RuntimeError('Level-2 structural admission is not frozen and passing')
+ if a.domain not in identity['domains']: raise ValueError(a.domain)
+ level1=ROOT/identity['level1_reference'][a.domain]['dev']
+ rows_by_level={
+ 'level1':[dict(x) for x in load_from_disk(str(level1))['multi_answer']],
+ 'level2':[dict(x) for x in load_from_disk(str(a.level2_root/a.domain/'dev'))['multi_answer']],
+ }
+ if a.row_limit < 0: raise ValueError('row-limit must be nonnegative')
+ if a.row_limit: rows_by_level={level:rows[:a.row_limit] for level,rows in rows_by_level.items()}
+ llm=vllm.LLM(model=str(a.model.resolve()),dtype=a.dtype,max_model_len=a.max_model_len,gpu_memory_utilization=.82,swap_space=16.0,enable_prefix_caching=True)
+ tokenizer=llm.get_tokenizer()
+ system='Return only the final answer inside \\boxed{}. Do not explain.'
+ results={}
+ for level,rows in rows_by_level.items():
+ prompts=[tokenizer.apply_chat_template(prompt_messages(a.domain,str(row['problem']),a.prompt_profile),tokenize=False,add_generation_prompt=True) for row in rows]
+ outputs=[]
+ for start in range(0,len(prompts),a.batch_size):
+ batch_rows=rows[start:start+a.batch_size]
+ batch_params=[sampling_params(a,a.domain,row) for row in batch_rows]
+ outputs.extend(llm.generate(prompts[start:start+a.batch_size],batch_params))
+ if len(outputs)!=len(rows): raise RuntimeError('vLLM output count mismatch')
+ prompt_results=[]
+ for index,(row,out) in enumerate(zip(rows,outputs)):
+ if len(out.outputs)!=8: raise RuntimeError(f'{level}/{index}: expected 8 samples')
+ attempts=[]
+ for sample in out.outputs:
+ text=str(sample.text); key=validated_modebench_outcome_key(text,row['answer'])
+ attempts.append({'text':text,'verified':key is not None,'canonical_key':key,'token_count':len(sample.token_ids)})
+ prompt_results.append({'row_index':index,'passed':any(x['verified'] for x in attempts),'verified_count':sum(x['verified'] for x in attempts),'attempts':attempts})
+ success=sum(x['passed'] for x in prompt_results)
+ results[level]={'rows':len(rows),'success_prompts':success,'pass_at_8':success/len(rows),'rows_sha256':sha(rows),'prompt_results':prompt_results}
+ l1=results['level1']['pass_at_8']; l2=results['level2']['pass_at_8']
+ admitted=.10<=l2<=.90 and l2=.10,'level2_not_too_easy':l2<=.90,'level2_harder_than_level1':l2 str:
+ return hashlib.sha256(json.dumps(value, sort_keys=True, separators=(',', ':'),
+ allow_nan=False).encode()).hexdigest()
+
+
+def file_sha(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open('rb') as handle:
+ for chunk in iter(lambda: handle.read(1 << 20), b''):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def atomic_new(path: Path, payload: Any) -> None:
+ """Publish a complete JSON file atomically, without replacing any receipt."""
+ path.parent.mkdir(parents=True, exist_ok=True)
+ fd, temporary = tempfile.mkstemp(prefix='.' + path.name + '.', dir=path.parent)
+ try:
+ with os.fdopen(fd, 'w') as handle:
+ json.dump(payload, handle, sort_keys=True, indent=2, allow_nan=False)
+ handle.write('\n')
+ handle.flush()
+ os.fsync(handle.fileno())
+ os.link(temporary, path)
+ finally:
+ os.unlink(temporary)
+
+
+def frozen_interface(domain: str, profile: str = 'original_level1') -> dict[str, Any]:
+ if domain not in DOMAINS:
+ raise ValueError(f'unknown domain: {domain}')
+ if profile not in ('original_level1', 'level2_qwen_r5'):
+ raise ValueError(f'unknown frozen interface: {profile}')
+ original = profile == 'original_level1'
+ return {
+ 'name': profile,
+ 'prompt_profile': 'boxed_direct_v1' if original or domain == 'graph_coloring' else 'hybrid_solver_v4',
+ 'syntax_profile': 'none' if original else {'graph_coloring': 'none', 'countdown': 'countdown_legal_v3'}.get(domain, 'domain_legal_v1'),
+ 'sample_count': 8, 'temperature': 1.0, 'top_p': 1.0,
+ 'max_tokens': 192, 'max_model_len': 1024 if original or domain == 'graph_coloring' else 2048,
+ 'dtype': 'float16', 'prompt_template': 'model_native_chat_template',
+ 'seed_policy': 'same_explicit_seed_for_each_prompt_independent_n8_draw',
+ }
+
+
+def model_identity(model: Path, label: str) -> dict[str, Any]:
+ model = model.resolve()
+ if not (model / 'config.json').is_file():
+ raise ValueError(f'local model config missing: {model}')
+ small_names = ('config.json', 'generation_config.json', 'tokenizer.json',
+ 'tokenizer_config.json', 'special_tokens_map.json',
+ 'model.safetensors.index.json', 'pytorch_model.bin.index.json')
+ weights = sorted(set(model.glob('*.safetensors')) | set(model.glob('pytorch_model*.bin')))
+ if not weights:
+ raise ValueError(f'local model weights missing: {model}')
+ return {
+ 'label': label, 'path': str(model),
+ 'configuration_sha256': {name: file_sha(model / name) for name in small_names if (model / name).is_file()},
+ 'weight_file_manifest': [{'name': p.name, 'bytes': p.stat().st_size,
+ 'mtime_ns': p.stat().st_mtime_ns, 'resolved_path': str(p.resolve())} for p in weights],
+ 'weight_identity_method': 'resolved_snapshot_paths_and_size_mtime_manifest; configuration_files_sha256',
+ }
+
+
+def code_identity() -> dict[str, str]:
+ paths = [Path(__file__), ROOT / 'ops/evaluate_modebench_level2_viability.py']
+ paths += sorted((ROOT / 'src/oat_drgrpo').glob('*.py'))
+ return {str(p.relative_to(ROOT)): file_sha(p) for p in paths}
+
+
+def validate_task(task: dict[str, Any], confirm_eval: bool) -> None:
+ frozen_interface(task['domain'], task.get('interface', 'original_level1'))
+ if task['domain'] not in DOMAINS:
+ raise ValueError(f"unknown domain: {task['domain']}")
+ if task['level'] not in ('level1', 'level2', 'level3'):
+ raise ValueError('level must be level1, level2, or level3')
+ if task.get('split', 'dev') not in ('dev', 'eval'):
+ raise ValueError('split must be dev or eval')
+ if task.get('split', 'dev') == 'eval' and not confirm_eval:
+ raise ValueError('confirmation prompts require --confirm-eval')
+ if task.get('split', 'dev') == 'eval' and (task.get('row_limit', 0) or task.get('row_offset', 0)):
+ raise ValueError('confirmation must score the full split, without row slicing')
+ if task.get('row_limit', 0) < 0 or task.get('row_offset', 0) < 0:
+ raise ValueError('row_limit and row_offset must be nonnegative')
+ if task.get('rows_jsonl') and task.get('dataset'):
+ raise ValueError('choose rows_jsonl or dataset')
+ if not (task.get('rows_jsonl') or task.get('dataset')) and task['level'] != 'level1':
+ raise ValueError('level2/level3 require rows_jsonl or dataset')
+ seeds = task['seeds']
+ if not seeds or len(seeds) != len(set(seeds)) or any(not isinstance(s, int) or s < 0 for s in seeds):
+ raise ValueError('seeds must be distinct nonnegative integers')
+ if task.get('batch_size', 8) < 1:
+ raise ValueError('batch_size must be positive')
+
+
+def load_rows(task: dict[str, Any]) -> tuple[list[dict[str, Any]], dict[str, Any]]:
+ if task.get('rows_jsonl'):
+ path = Path(task['rows_jsonl']).resolve()
+ with path.open() as handle:
+ all_rows = [json.loads(line) for line in handle if line.strip()]
+ source = {'kind': 'jsonl', 'path': str(path), 'file_sha256': file_sha(path)}
+ else:
+ from datasets import load_from_disk
+ dataset_path = task.get('dataset')
+ if not dataset_path:
+ sys.path.insert(0, str(ROOT / 'ops/exp_scaling'))
+ from materialize_modebench_harder_v2 import LEVEL1
+ dataset_path = LEVEL1[task['domain']][task.get('split', 'dev')]
+ path = Path(dataset_path).resolve()
+ dataset = load_from_disk(str(path))
+ if hasattr(dataset, 'keys'):
+ dataset = dataset['multi_answer']
+ all_rows = [dict(row) for row in dataset]
+ source = {'kind': 'saved_dataset', 'path': str(path)}
+ offset = task.get('row_offset', 0)
+ limit = task.get('row_limit', 0)
+ rows = all_rows[offset:offset + limit if limit else None]
+ if not rows:
+ raise ValueError('no input rows')
+ for index, row in enumerate(rows):
+ if not isinstance(row, dict) or not isinstance(row.get('problem'), str) or 'answer' not in row:
+ raise ValueError(f'invalid ModeBench row {offset + index}')
+ spec = json.loads(row['answer']) if isinstance(row['answer'], str) else row['answer']
+ if not isinstance(spec, dict):
+ raise ValueError(f'invalid executable spec at row {offset + index}')
+ source.update(total_rows=len(all_rows), selected_rows=len(rows), row_offset=offset,
+ row_limit=limit, all_rows_sha256=sha(all_rows), rows_sha256=sha(rows))
+ return rows, source
+
+
+def grade_samples(row: dict, output: Any, grader: Callable) -> dict[str, Any]:
+ if len(output.outputs) != 8:
+ raise RuntimeError(f'expected 8 samples, received {len(output.outputs)}')
+ attempts = []
+ for sample in output.outputs:
+ text = str(sample.text)
+ key = grader(text, row['answer'])
+ attempts.append({'text': text, 'verified': key is not None, 'canonical_key': key,
+ 'token_count': len(sample.token_ids),
+ 'finish_reason': getattr(sample, 'finish_reason', None)})
+ verified = sum(attempt['verified'] for attempt in attempts)
+ distinct = len({sha(a['canonical_key']) for a in attempts if a['verified']})
+ return {'pass1': verified / 8, 'pass8': float(verified > 0), 'distinct8': distinct,
+ 'verified_count': verified, 'attempts': attempts}
+
+
+def summarize(prompt_results: list[dict[str, Any]]) -> dict[str, Any]:
+ summary: dict[str, Any] = {'rows': len(prompt_results)}
+ for metric in ('pass1', 'pass8', 'distinct8'):
+ values = [row[metric] for row in prompt_results]
+ summary[metric] = statistics.mean(values)
+ summary[metric + '_standard_error_across_prompts'] = (
+ statistics.stdev(values) / math.sqrt(len(values)) if len(values) > 1 else None)
+ return summary
+
+
+def evaluate_task(llm: Any, tokenizer: Any, task: dict[str, Any], *,
+ model: dict[str, Any], code: dict[str, str], confirm_eval: bool = False,
+ resume: bool = False, grader: Callable | None = None,
+ params_factory: Callable = sampling_params) -> dict[str, Any]:
+ """Evaluate one task using an already-loaded LLM; useful for pool runners."""
+ validate_task(task, confirm_eval)
+ output_path = Path(task['output'])
+ if output_path.exists() and not resume:
+ raise FileExistsError(f'fresh final receipt required: {output_path}')
+ rows, source = load_rows(task)
+ domain = task['domain']
+ interface = frozen_interface(domain, task.get('interface', 'original_level1'))
+ prompts = [tokenizer.apply_chat_template(prompt_messages(domain, row['problem'], interface['prompt_profile']),
+ tokenize=False, add_generation_prompt=True) for row in rows]
+ model_context = int(getattr(llm, '_modebench_max_model_len', interface['max_model_len']))
+ if model_context < interface['max_model_len']:
+ raise ValueError('loaded model context is too short for the frozen interface')
+ for index, prompt in enumerate(prompts):
+ token_count = len(tokenizer.encode(prompt, add_special_tokens=False))
+ if token_count + interface['max_tokens'] > interface['max_model_len']:
+ raise ValueError(f'row {index} exceeds frozen context budget')
+ identity = {'schema': SCHEMA, 'domain': domain, 'level': task['level'], 'split': task.get('split', 'dev'),
+ 'model': model, 'interface': interface, 'interface_sha256': sha(interface),
+ 'source': source, 'seeds': task['seeds'], 'batch_size': task.get('batch_size', 8),
+ 'code_sha256': code, 'rendered_prompts_sha256': sha(prompts)}
+ run_sha = sha(identity)
+ if output_path.exists():
+ completed = json.loads(output_path.read_text())
+ if completed.get('identity_sha256') != run_sha or completed.get('identity') != identity or completed.get('status') != 'complete':
+ raise ValueError('completed receipt identity mismatch; use a fresh output path')
+ print(json.dumps({'event': 'task_already_complete', 'output': str(output_path)}), flush=True)
+ return completed
+ batch_dir = Path(str(output_path) + '.batches')
+ manifest_path = batch_dir / 'run.json'
+ if manifest_path.exists():
+ if not resume:
+ raise FileExistsError(f'partial run exists; use --resume: {manifest_path}')
+ prior = json.loads(manifest_path.read_text())
+ if prior.get('identity_sha256') != run_sha or prior.get('identity') != identity:
+ raise ValueError('resume identity mismatch; use a fresh output path')
+ else:
+ atomic_new(manifest_path, {'identity_sha256': run_sha, 'identity': identity})
+ if grader is None:
+ from oat_drgrpo.math_grader import validated_modebench_outcome_key
+ grader = validated_modebench_outcome_key
+ draws_by_row: list[list[dict[str, Any]]] = [[] for _ in rows]
+ batch_size = task.get('batch_size', 8)
+ for seed in task['seeds']:
+ for start in range(0, len(rows), batch_size):
+ end = min(start + batch_size, len(rows))
+ batch_path = batch_dir / f'seed-{seed}__rows-{start:06d}-{end:06d}.json'
+ if batch_path.exists():
+ batch = json.loads(batch_path.read_text())
+ if (batch.get('identity_sha256') != run_sha or batch.get('seed') != seed
+ or batch.get('start') != start or batch.get('end') != end
+ or len(batch.get('draws', [])) != end - start
+ or batch.get('draws_sha256') != sha(batch['draws'])):
+ raise ValueError(f'invalid resumed batch: {batch_path}')
+ else:
+ params = SimpleNamespace(**interface, seed=seed)
+ batch_params = [params_factory(params, domain, row) for row in rows[start:end]]
+ generated = llm.generate(prompts[start:end], batch_params, use_tqdm=False)
+ if len(generated) != end - start:
+ raise RuntimeError('vLLM output count mismatch')
+ draws = []
+ for local, result in enumerate(generated):
+ if getattr(result, 'prompt', prompts[start + local]) != prompts[start + local]:
+ raise RuntimeError('vLLM returned a mismatched prompt')
+ draws.append(grade_samples(rows[start + local], result, grader))
+ batch = {'identity_sha256': run_sha, 'seed': seed, 'start': start, 'end': end,
+ 'draws': draws, 'draws_sha256': sha(draws)}
+ atomic_new(batch_path, batch)
+ for index, draw in enumerate(batch['draws'], start=start):
+ draws_by_row[index].append({'seed': seed, **draw})
+ print(json.dumps({'event': 'batch_complete', 'domain': domain, 'level': task['level'],
+ 'seed': seed, 'rows_done': end, 'rows_total': len(rows),
+ 'batch_pass8': statistics.mean(d['pass8'] for d in batch['draws'])}), flush=True)
+ prompt_results = []
+ for index, (row, draws) in enumerate(zip(rows, draws_by_row)):
+ spec = json.loads(row['answer']) if isinstance(row['answer'], str) else row['answer']
+ result = {'row_index': source['row_offset'] + index, 'row_sha256': sha(row),
+ 'problem_sha256': sha(row['problem']), 'spec_sha256': sha(spec),
+ 'row_metadata': {key: value for key, value in row.items() if key not in ('problem', 'answer')},
+ 'draws': draws}
+ result.update({metric: statistics.mean(draw[metric] for draw in draws)
+ for metric in ('pass1', 'pass8', 'distinct8')})
+ prompt_results.append(result)
+ payload = {'schema': SCHEMA, 'generated_at': datetime.now(timezone.utc).isoformat(),
+ 'status': 'complete', 'identity_sha256': run_sha, 'identity': identity,
+ 'domain': domain, 'level': task['level'], 'split': task.get('split', 'dev'),
+ 'model_label': model['label'], 'sampling': {**interface, 'seeds': task['seeds']},
+ 'metrics': summarize(prompt_results), 'prompt_results': prompt_results,
+ 'answer_mode_histogram': dict(Counter(str(row.get('answer_mode_count')) for row in rows)),
+ 'metric_definitions': {'pass1': 'mean verified fraction among each n=8 draw',
+ 'pass8': 'mean indicator of at least one verified sample in each n=8 draw',
+ 'distinct8': 'mean count of unique verified canonical keys in each n=8 draw'},
+ 'information_boundary': {'evaluation_prompts_loaded': task.get('split', 'dev') == 'eval',
+ 'confirmation_explicitly_authorized': confirm_eval,
+ 'treatment_training_started': False}}
+ atomic_new(output_path, payload)
+ print(json.dumps({'event': 'task_complete', 'domain': domain, 'level': task['level'],
+ 'output': str(output_path), 'metrics': payload['metrics']}), flush=True)
+ return payload
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--model', required=True, type=Path)
+ parser.add_argument('--model-label', required=True, choices=('3b', '05b'))
+ parser.add_argument('--domain', choices=DOMAINS)
+ parser.add_argument('--level', default='level3', choices=('level1', 'level2', 'level3'))
+ parser.add_argument('--split', default='dev', choices=('dev', 'eval'))
+ source = parser.add_mutually_exclusive_group()
+ source.add_argument('--rows-jsonl', type=Path)
+ source.add_argument('--dataset', type=Path)
+ parser.add_argument('--tasks-json', type=Path)
+ parser.add_argument('--interface', default='original_level1', choices=('original_level1', 'level2_qwen_r5'))
+ parser.add_argument('--output', type=Path)
+ seeds = parser.add_mutually_exclusive_group()
+ seeds.add_argument('--seed', type=int)
+ seeds.add_argument('--seeds', type=int, nargs='+')
+ parser.add_argument('--batch-size', type=int, default=8)
+ parser.add_argument('--row-limit', type=int, default=0)
+ parser.add_argument('--row-offset', type=int, default=0)
+ parser.add_argument('--confirm-eval', action='store_true')
+ parser.add_argument('--resume', action='store_true')
+ return parser.parse_args(argv)
+
+
+def main(argv: list[str] | None = None) -> None:
+ args = parse_args(argv)
+ defaults = {'interface': args.interface, 'level': args.level, 'split': args.split, 'batch_size': args.batch_size,
+ 'row_limit': args.row_limit, 'row_offset': args.row_offset,
+ 'seeds': args.seeds or ([args.seed] if args.seed is not None else [])}
+ if args.tasks_json:
+ raw = json.loads(args.tasks_json.read_text())
+ if not isinstance(raw, list) or not raw:
+ raise ValueError('--tasks-json must contain a nonempty list')
+ tasks = [{**defaults, **task} for task in raw]
+ for task in tasks:
+ if 'seed' in task:
+ task['seeds'] = [task.pop('seed')]
+ else:
+ if not args.domain or not args.output:
+ raise ValueError('--domain and --output are required without --tasks-json')
+ tasks = [{**defaults, 'domain': args.domain, 'output': str(args.output),
+ 'rows_jsonl': str(args.rows_jsonl) if args.rows_jsonl else None,
+ 'dataset': str(args.dataset) if args.dataset else None}]
+ if len({str(Path(task['output']).resolve()) for task in tasks}) != len(tasks):
+ raise ValueError('tasks must have distinct output paths')
+ for task in tasks:
+ validate_task(task, args.confirm_eval)
+ if Path(task['output']).exists() and not args.resume:
+ raise FileExistsError(f"fresh final receipt required: {task['output']}")
+ identity = model_identity(args.model, args.model_label)
+ code = code_identity()
+ import vllm
+ identity['vllm_version'] = importlib.metadata.version('vllm')
+ max_context = max(frozen_interface(task['domain'], task['interface'])['max_model_len'] for task in tasks)
+ llm = vllm.LLM(model=str(args.model.resolve()), dtype='float16', max_model_len=max_context,
+ gpu_memory_utilization=.82, swap_space=16.0, enable_prefix_caching=True)
+ llm._modebench_max_model_len = max_context
+ tokenizer = llm.get_tokenizer()
+ for task in tasks:
+ evaluate_task(llm, tokenizer, task, model=identity, code=code,
+ confirm_eval=args.confirm_eval, resume=args.resume)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/provenance/source_code/ops/evaluate_modebench_level3_independent.py b/provenance/source_code/ops/evaluate_modebench_level3_independent.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4c76773cc52d97f4c4a98ae696a5dac7a113ef4
--- /dev/null
+++ b/provenance/source_code/ops/evaluate_modebench_level3_independent.py
@@ -0,0 +1,338 @@
+#!/usr/bin/env python3
+"""Independent prompt/draw RNG streams for the frozen Level 3 interface.
+
+The registered --seeds are draw labels. Effective vLLM request seeds are
+aligned eight-child blocks derived from domain, raw problem text, and label.
+This version uses a separate receipt/interface namespace and cannot resume v1
+receipts. Prompts, syntax, model precision, and sampling budgets are unchanged.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from datetime import datetime, timezone
+import importlib.metadata
+import json
+import os
+from pathlib import Path
+import statistics
+import sys
+from types import SimpleNamespace
+from typing import Any, Callable
+
+ROOT = Path(__file__).resolve().parents[1]
+for path in (ROOT / 'ops', ROOT / 'src'):
+ if str(path) not in sys.path:
+ sys.path.insert(0, str(path))
+import evaluate_modebench_level3 as original
+from evaluate_modebench_level3 import (
+ DOMAINS, atomic_new, file_sha, grade_samples, load_rows, model_identity,
+ prompt_messages, sampling_params, sha, summarize,
+)
+from modebench_independent_seeds import POLICY, SAMPLES, seed_schedule
+
+SCHEMA = 'modebench-level3-frozen-calibration-independent-v2'
+INTERFACE = 'level2_qwen_r5_independent_v2'
+ENGINE_CONTRACT = {'vllm_version': '0.8.4', 'engine': 'V0',
+ 'parallel_sample_seed_policy': 'request_seed_plus_sample_index'}
+
+
+def require(condition: bool, message: str) -> None:
+ if not condition:
+ raise ValueError(message)
+
+
+def frozen_interface(domain: str, profile: str = INTERFACE) -> dict[str, Any]:
+ require(profile == INTERFACE, 'independent evaluator requires the independent v2 interface')
+ return {**original.frozen_interface(domain, 'level2_qwen_r5'),
+ 'name': INTERFACE, 'seed_policy': POLICY}
+
+
+def code_identity() -> dict[str, str]:
+ return {**original.code_identity(),
+ 'ops/evaluate_modebench_level3_independent.py': file_sha(Path(__file__)),
+ 'ops/modebench_independent_seeds.py': file_sha(ROOT / 'ops/modebench_independent_seeds.py')}
+
+
+def validate_runtime_contract() -> str:
+ require(os.environ.get('VLLM_USE_V1') == '0', 'independent seed contract requires VLLM_USE_V1=0')
+ version = importlib.metadata.version('vllm')
+ require(version == ENGINE_CONTRACT['vllm_version'], 'independent seed contract requires vLLM 0.8.4')
+ return version
+
+
+def validate_task(task: dict[str, Any], confirm_eval: bool) -> None:
+ frozen_interface(task['domain'], task.get('interface', INTERFACE))
+ original.validate_task({**task, 'interface': 'level2_qwen_r5'}, confirm_eval)
+ require(all(type(seed) is int for seed in task['seeds']), 'draw labels must be integers, not booleans')
+
+
+def schedule_record(domain: str, rows: list[dict], labels: list[int]) -> dict[str, Any]:
+ requests = seed_schedule(domain, [row['problem'] for row in rows], labels)
+ return {'policy': POLICY, 'sample_count': SAMPLES, 'draw_labels': list(labels),
+ 'problem_sha256': [sha(row['problem']) for row in rows],
+ 'request_seeds': requests,
+ 'child_seeds': [[[base + sample for sample in range(SAMPLES)] for base in prompt]
+ for prompt in requests]}
+
+
+def draw_seed_metadata(schedule: dict, row_index: int, draw_index: int) -> dict:
+ return {'seed': schedule['draw_labels'][draw_index],
+ 'request_seed': schedule['request_seeds'][row_index][draw_index],
+ 'child_seeds': schedule['child_seeds'][row_index][draw_index]}
+
+
+def validate_draw_seed_metadata(draw: dict, schedule: dict, row_index: int, draw_index: int) -> None:
+ expected = draw_seed_metadata(schedule, row_index, draw_index)
+ require(isinstance(draw, dict) and all(draw.get(key) == value for key, value in expected.items()),
+ f'draw RNG metadata differs from authenticated schedule at row {row_index}, draw {draw_index}')
+ require(type(draw['seed']) is int and type(draw['request_seed']) is int
+ and isinstance(draw['child_seeds'], list) and all(type(seed) is int for seed in draw['child_seeds']),
+ 'draw RNG metadata must contain exact integers')
+
+
+def validate_seed_receipt(receipt: dict[str, Any], rows: list[dict] | None = None) -> dict[str, Any]:
+ """Authenticate v2 RNG evidence; general metric validation remains separate.
+
+ Supplied rows must be the selected source rows in receipt order. Without
+ rows, the receipt's saved source is reopened and authenticated. No model is
+ loaded. Registered labels remain under ``seeds`` for campaign bookkeeping;
+ only the effective request/child schedule identifies the actual RNG streams.
+ """
+ require(receipt.get('schema') == SCHEMA and receipt.get('status') == 'complete',
+ 'independent v2 complete receipt required')
+ identity = receipt.get('identity', {})
+ require(isinstance(identity, dict) and identity.get('schema') == SCHEMA,
+ 'independent v2 identity required')
+ require(receipt.get('identity_sha256') == sha(identity), 'receipt identity hash mismatch')
+ domain = identity.get('domain')
+ require(domain in DOMAINS and receipt.get('domain') == domain, 'receipt domain mismatch')
+ interface = frozen_interface(domain)
+ require(identity.get('interface') == interface and identity.get('interface_sha256') == sha(interface),
+ 'receipt independent interface/policy mismatch')
+ require(identity.get('code_sha256') == code_identity(), 'receipt evaluator/helper code hashes missing or changed')
+ require(identity.get('sampling_engine') == ENGINE_CONTRACT
+ and identity.get('model', {}).get('vllm_version') == ENGINE_CONTRACT['vllm_version'],
+ 'receipt vLLM V0 child-seed contract mismatch')
+ labels = identity.get('seeds')
+ require(isinstance(labels, list) and labels and all(type(seed) is int and seed >= 0 for seed in labels)
+ and len(labels) == len(set(labels)), 'invalid registered draw labels')
+ require(receipt.get('sampling') == {**interface, 'seeds': labels}, 'receipt sampling metadata mismatch')
+ source = identity.get('source', {})
+ require(isinstance(source, dict), 'missing source identity')
+ if rows is None:
+ require(source.get('kind') in ('jsonl', 'saved_dataset') and source.get('path'), 'unsupported source identity')
+ task = {'domain': domain, 'rows_jsonl' if source['kind'] == 'jsonl' else 'dataset': source['path'],
+ 'row_offset': source.get('row_offset', 0), 'row_limit': source.get('row_limit', 0)}
+ rows, reopened_source = load_rows(task)
+ require(reopened_source == source, 'saved source identity changed')
+ require(isinstance(rows, list) and len(rows) == source.get('selected_rows')
+ and sha(rows) == source.get('rows_sha256'), 'selected source rows differ from receipt')
+ expected = schedule_record(domain, rows, labels)
+ require(identity.get('seed_schedule') == expected
+ and sha(identity.get('seed_schedule')) == sha(expected)
+ and identity.get('seed_schedule_sha256') == sha(expected), 'receipt RNG schedule mismatch')
+ results = receipt.get('prompt_results')
+ require(isinstance(results, list) and len(results) == len(rows), 'missing prompt RNG evidence')
+ for row_index, (source_row, result) in enumerate(zip(rows, results)):
+ require(result.get('problem_sha256') == sha(source_row['problem']), 'prompt hash differs from seed source')
+ draws = result.get('draws')
+ require(isinstance(draws, list) and len(draws) == len(labels), 'missing draw RNG evidence')
+ for draw_index, draw in enumerate(draws):
+ validate_draw_seed_metadata(draw, expected, row_index, draw_index)
+ return {'policy': POLICY, 'prompts': len(rows), 'draws_per_prompt': len(labels),
+ 'distinct_request_blocks': len(rows) * len(labels),
+ 'distinct_child_seeds': len(rows) * len(labels) * SAMPLES,
+ 'seed_schedule_sha256': sha(expected)}
+
+
+def evaluate_task(llm: Any, tokenizer: Any, task: dict[str, Any], *,
+ model: dict[str, Any], code: dict[str, str], confirm_eval: bool = False,
+ resume: bool = False, grader: Callable | None = None,
+ params_factory: Callable = sampling_params) -> dict[str, Any]:
+ validate_task(task, confirm_eval)
+ require(code == code_identity(), 'evaluation requires complete current evaluator/helper code hashes')
+ output_path = Path(task['output'])
+ if output_path.exists() and not resume:
+ raise FileExistsError(f'fresh final receipt required: {output_path}')
+ rows, source = load_rows(task)
+ domain = task['domain']
+ interface = frozen_interface(domain, task.get('interface', INTERFACE))
+ schedule = schedule_record(domain, rows, task['seeds'])
+ prompts = [tokenizer.apply_chat_template(prompt_messages(domain, row['problem'], interface['prompt_profile']),
+ tokenize=False, add_generation_prompt=True) for row in rows]
+ model_context = int(getattr(llm, '_modebench_max_model_len', interface['max_model_len']))
+ require(model_context >= interface['max_model_len'], 'loaded model context is too short for the frozen interface')
+ for index, prompt in enumerate(prompts):
+ token_count = len(tokenizer.encode(prompt, add_special_tokens=False))
+ require(token_count + interface['max_tokens'] <= interface['max_model_len'],
+ f'row {index} exceeds frozen context budget')
+ identity = {'schema': SCHEMA, 'domain': domain, 'level': task['level'], 'split': task.get('split', 'dev'),
+ 'model': model, 'interface': interface, 'interface_sha256': sha(interface),
+ 'source': source, 'seeds': task['seeds'], 'batch_size': task.get('batch_size', 8),
+ 'code_sha256': code, 'rendered_prompts_sha256': sha(prompts),
+ 'seed_schedule': schedule, 'seed_schedule_sha256': sha(schedule),
+ 'sampling_engine': dict(ENGINE_CONTRACT)}
+ run_sha = sha(identity)
+ if output_path.exists():
+ completed = json.loads(output_path.read_text())
+ require(completed.get('identity_sha256') == run_sha and completed.get('identity') == identity
+ and completed.get('status') == 'complete', 'completed receipt identity mismatch; use a fresh output path')
+ validate_seed_receipt(completed, rows)
+ print(json.dumps({'event': 'task_already_complete', 'output': str(output_path)}), flush=True)
+ return completed
+ batch_dir = Path(str(output_path) + '.batches')
+ manifest_path = batch_dir / 'run.json'
+ if manifest_path.exists():
+ if not resume:
+ raise FileExistsError(f'partial run exists; use --resume: {manifest_path}')
+ prior = json.loads(manifest_path.read_text())
+ require(prior.get('identity_sha256') == run_sha and prior.get('identity') == identity,
+ 'resume identity mismatch; use a fresh output path')
+ else:
+ atomic_new(manifest_path, {'identity_sha256': run_sha, 'identity': identity})
+ if grader is None:
+ from oat_drgrpo.math_grader import validated_modebench_outcome_key
+ grader = validated_modebench_outcome_key
+ draws_by_row: list[list[dict[str, Any]]] = [[] for _ in rows]
+ batch_size = task.get('batch_size', 8)
+ for draw_index, label in enumerate(task['seeds']):
+ for start in range(0, len(rows), batch_size):
+ end = min(start + batch_size, len(rows))
+ batch_path = batch_dir / f'seed-{label}__rows-{start:06d}-{end:06d}.json'
+ if batch_path.exists():
+ batch = json.loads(batch_path.read_text())
+ require(batch.get('identity_sha256') == run_sha and batch.get('seed') == label
+ and batch.get('start') == start and batch.get('end') == end
+ and len(batch.get('draws', [])) == end - start
+ and batch.get('draws_sha256') == sha(batch['draws']), f'invalid resumed batch: {batch_path}')
+ for row_index, draw in enumerate(batch['draws'], start):
+ validate_draw_seed_metadata(draw, schedule, row_index, draw_index)
+ else:
+ batch_params = []
+ for row_index in range(start, end):
+ base = schedule['request_seeds'][row_index][draw_index]
+ # Each row gets a fresh namespace. A mutating factory cannot
+ # corrupt the next row's registered label or derived seed.
+ params = params_factory(SimpleNamespace(**interface, seed=base), domain, rows[row_index])
+ require(getattr(params, 'seed', None) == base and getattr(params, 'n', None) == SAMPLES,
+ 'sampling parameters must retain the scheduled seed and n=8')
+ batch_params.append(params)
+ generated = llm.generate(prompts[start:end], batch_params, use_tqdm=False)
+ if len(generated) != end - start:
+ raise RuntimeError('vLLM output count mismatch')
+ draws = []
+ for local, result in enumerate(generated):
+ row_index = start + local
+ if getattr(result, 'prompt', prompts[row_index]) != prompts[row_index]:
+ raise RuntimeError('vLLM returned a mismatched prompt')
+ draws.append({**draw_seed_metadata(schedule, row_index, draw_index),
+ **grade_samples(rows[row_index], result, grader)})
+ batch = {'identity_sha256': run_sha, 'seed': label, 'start': start, 'end': end,
+ 'draws': draws, 'draws_sha256': sha(draws)}
+ atomic_new(batch_path, batch)
+ for row_index, draw in enumerate(batch['draws'], start):
+ draws_by_row[row_index].append(draw)
+ print(json.dumps({'event': 'batch_complete', 'domain': domain, 'level': task['level'],
+ 'seed': label, 'rows_done': end, 'rows_total': len(rows),
+ 'batch_pass8': statistics.mean(d['pass8'] for d in batch['draws'])}), flush=True)
+ prompt_results = []
+ for index, (row, draws) in enumerate(zip(rows, draws_by_row)):
+ spec = json.loads(row['answer']) if isinstance(row['answer'], str) else row['answer']
+ result = {'row_index': source['row_offset'] + index, 'row_sha256': sha(row),
+ 'problem_sha256': sha(row['problem']), 'spec_sha256': sha(spec),
+ 'row_metadata': {key: value for key, value in row.items() if key not in ('problem', 'answer')},
+ 'draws': draws}
+ result.update({metric: statistics.mean(draw[metric] for draw in draws)
+ for metric in ('pass1', 'pass8', 'distinct8')})
+ prompt_results.append(result)
+ payload = {'schema': SCHEMA, 'generated_at': datetime.now(timezone.utc).isoformat(),
+ 'status': 'complete', 'identity_sha256': run_sha, 'identity': identity,
+ 'domain': domain, 'level': task['level'], 'split': task.get('split', 'dev'),
+ 'model_label': model['label'], 'sampling': {**interface, 'seeds': task['seeds']},
+ 'metrics': summarize(prompt_results), 'prompt_results': prompt_results,
+ 'answer_mode_histogram': dict(Counter(str(row.get('answer_mode_count')) for row in rows)),
+ 'metric_definitions': {'pass1': 'mean verified fraction among each n=8 draw',
+ 'pass8': 'mean indicator of at least one verified sample in each n=8 draw',
+ 'distinct8': 'mean count of unique verified canonical keys in each n=8 draw'},
+ 'information_boundary': {'evaluation_prompts_loaded': task.get('split', 'dev') == 'eval',
+ 'confirmation_explicitly_authorized': confirm_eval,
+ 'treatment_training_started': False}}
+ validate_seed_receipt(payload, rows)
+ atomic_new(output_path, payload)
+ print(json.dumps({'event': 'task_complete', 'domain': domain, 'level': task['level'],
+ 'output': str(output_path), 'metrics': payload['metrics']}), flush=True)
+ return payload
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--model', required=True, type=Path)
+ parser.add_argument('--model-label', required=True, choices=('3b', '05b'))
+ parser.add_argument('--domain', choices=DOMAINS)
+ parser.add_argument('--level', default='level3', choices=('level1', 'level2', 'level3'))
+ parser.add_argument('--split', default='dev', choices=('dev', 'eval'))
+ source = parser.add_mutually_exclusive_group()
+ source.add_argument('--rows-jsonl', type=Path)
+ source.add_argument('--dataset', type=Path)
+ parser.add_argument('--tasks-json', type=Path)
+ parser.add_argument('--interface', default=INTERFACE, choices=(INTERFACE,))
+ parser.add_argument('--output', type=Path)
+ seeds = parser.add_mutually_exclusive_group()
+ seeds.add_argument('--seed', type=int)
+ seeds.add_argument('--seeds', type=int, nargs='+')
+ parser.add_argument('--batch-size', type=int, default=8)
+ parser.add_argument('--row-limit', type=int, default=0)
+ parser.add_argument('--row-offset', type=int, default=0)
+ parser.add_argument('--confirm-eval', action='store_true')
+ parser.add_argument('--resume', action='store_true')
+ return parser.parse_args(argv)
+
+
+def main(argv: list[str] | None = None) -> None:
+ args = parse_args(argv)
+ defaults = {'interface': args.interface, 'level': args.level, 'split': args.split, 'batch_size': args.batch_size,
+ 'row_limit': args.row_limit, 'row_offset': args.row_offset,
+ 'seeds': args.seeds or ([args.seed] if args.seed is not None else [])}
+ if args.tasks_json:
+ raw = json.loads(args.tasks_json.read_text())
+ require(isinstance(raw, list) and raw, '--tasks-json must contain a nonempty list')
+ tasks = [{**defaults, **task} for task in raw]
+ for task in tasks:
+ if 'seed' in task:
+ task['seeds'] = [task.pop('seed')]
+ else:
+ require(args.domain and args.output, '--domain and --output are required without --tasks-json')
+ tasks = [{**defaults, 'domain': args.domain, 'output': str(args.output),
+ 'rows_jsonl': str(args.rows_jsonl) if args.rows_jsonl else None,
+ 'dataset': str(args.dataset) if args.dataset else None}]
+ require(len({str(Path(task['output']).resolve()) for task in tasks}) == len(tasks), 'tasks must have distinct output paths')
+ # Detect cross-task stream collisions before constructing the model. A
+ # process can evaluate several domains or separately published pool files.
+ seen_blocks = set()
+ for task in tasks:
+ validate_task(task, args.confirm_eval)
+ if Path(task['output']).exists() and not args.resume:
+ raise FileExistsError(f"fresh final receipt required: {task['output']}")
+ rows, _ = load_rows(task)
+ schedule = schedule_record(task['domain'], rows, task['seeds'])
+ blocks = {base for row in schedule['request_seeds'] for base in row}
+ require(not seen_blocks & blocks, 'duplicate or colliding RNG blocks across task manifest')
+ seen_blocks |= blocks
+ runtime_version = validate_runtime_contract()
+ identity = model_identity(args.model, args.model_label)
+ code = code_identity()
+ import vllm
+ identity['vllm_version'] = runtime_version
+ max_context = max(frozen_interface(task['domain'], task['interface'])['max_model_len'] for task in tasks)
+ llm = vllm.LLM(model=str(args.model.resolve()), dtype='float16', max_model_len=max_context,
+ gpu_memory_utilization=.82, swap_space=16.0, enable_prefix_caching=True)
+ llm._modebench_max_model_len = max_context
+ tokenizer = llm.get_tokenizer()
+ for task in tasks:
+ evaluate_task(llm, tokenizer, task, model=identity, code=code,
+ confirm_eval=args.confirm_eval, resume=args.resume)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/provenance/source_code/ops/exp_scaling/materialize_modebench_harder_v2.py b/provenance/source_code/ops/exp_scaling/materialize_modebench_harder_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd7d6e4ba3152fd37bef173f5b0770c47d3a307d
--- /dev/null
+++ b/provenance/source_code/ops/exp_scaling/materialize_modebench_harder_v2.py
@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""Build support-matched harder-v2 ModeBench train/dev/evaluation data."""
+
+from __future__ import annotations
+from collections import Counter
+import hashlib, importlib.util, itertools, json, random, shutil, sys, tempfile
+from pathlib import Path
+from typing import Any
+import numpy as np
+from scipy.optimize import Bounds, LinearConstraint, milp
+from scipy.sparse import lil_matrix
+from datasets import Dataset, DatasetDict, load_from_disk
+
+ROOT=Path(__file__).resolve().parents[2]
+for p in (ROOT/"ops",ROOT/"src",ROOT/"ops/exp_scaling"):
+ if str(p) not in sys.path: sys.path.insert(0,str(p))
+from make_exact_countdown_mode_data import _synthetic_countdown_rows
+from make_exact_answer_mode_data import _synthetic_graph_rows
+from make_python_factor_mode_data import _candidate_values,_row as _python_row
+from make_mathir_action_menu_data import _build_rows as _mathir_rows,_family_support
+from oat_drgrpo.python_modebench import python_factor_mode_count
+from make_pantry_plan_mode_data import FAMILY_POOLS,_build_row as _pantry_row,_canonical_sha256
+from materialize_e117_evaluation_reserves import _identities,_load_source_rows
+
+SCHEMA="modebench_harder_v2_support_matched_splits"
+OUTPUT_ROOT=ROOT/"var/data/modebench_harder_v2_matched"
+DOMAINS=("countdown","graph_coloring","python_factors","mathir","pantry")
+SEEDS={"countdown":4217100,"graph_coloring":4217200,"python_factors":4217300,"mathir":4217400,"pantry":4217500}
+SPLITS={"train":(384,"train"),"dev":(128,"multi_answer"),"eval":(128,"multi_answer")}
+SPLIT_SEED_OFFSETS={"train":0,"dev":10_000,"eval":20_000}
+RESERVE=ROOT/"var/data/e117_evaluation_reserve_v1"
+LEVEL1={
+"countdown":{"train":ROOT/"var/data/exact_countdown_easy3_probe/train","dev":RESERVE/"development/countdown/eval","eval":RESERVE/"confirmation/countdown/eval"},
+"graph_coloring":{"train":ROOT/"var/data/graph_coloring_modebench_v2/train","dev":RESERVE/"development/graph_coloring/eval","eval":RESERVE/"confirmation/graph_coloring/eval"},
+"python_factors":{"train":ROOT/"var/data/python_factor_modebench_v1/train","dev":RESERVE/"development/python_factors/eval","eval":RESERVE/"confirmation/python_factors/eval"},
+"mathir":{"train":ROOT/"var/data/mathir_action_menu_v1/train","dev":RESERVE/"development/mathir/eval","eval":RESERVE/"confirmation/mathir/eval"},
+"pantry":{split:ROOT/f"var/data/pantry_plan_modebench_v2/{split}" for split in SPLITS},
+}
+
+def load_rows(path:Path,split:str)->list[dict[str,Any]]:
+ return [dict(x) for x in load_from_disk(str(path))[split]]
+
+def modes(rows): return Counter(int(x["answer_mode_count"]) for x in rows)
+def row_hash(rows):
+ text="\n".join(json.dumps(x,sort_keys=True,separators=(",",":")) for x in rows)
+ return hashlib.sha256(text.encode()).hexdigest()
+def take_hist(pool,target):
+ got=Counter(); out=[]
+ for row in pool:
+ m=int(row["answer_mode_count"])
+ if got[m]1000000: raise RuntimeError(f"Python pool misses {target-got}")
+ cases=tuple(sorted(rng.sample(values,4)))
+ if max(cases)<=96 or cases in blocked or cases in seen: continue
+ m=python_factor_mode_count(cases)
+ if got[m]>=target[m]: continue
+ rows.append(_python_row(cases=cases,split_tag=tag,seed=seed,index=len(rows)))
+ seen.add(cases); got[m]+=1
+ rng.shuffle(rows); return rows
+
+def build_mathir(target,excluded,seed,tag):
+ raw={(x[1],x[2]) for x in excluded}
+ pool=_mathir_rows(max(1024,sum(target.values())*8),seed=seed,split_tag=tag,family_support=_family_support(),excluded=raw)
+ pool=[x for x in pool if x["mathir_family"] in {"ax_plus_b_eq_dx_plus_c","ax_plus_b_eq_c_minus_dx"}]
+ def harder(row):
+ bindings={k:int(v) for k,v in json.loads(row['answer'])['bindings'].items()}
+ magnitude=sum(abs(bindings[k]) for k in sorted(bindings))
+ coefficient_symmetry=abs(abs(bindings['a'])-abs(bindings['d']))
+ combined_coefficient=(abs(bindings['a']-bindings['d'])
+ if row['mathir_family']=='ax_plus_b_eq_dx_plus_c'
+ else abs(bindings['a']+bindings['d']))
+ return (coefficient_symmetry,combined_coefficient,-magnitude,str(row['problem']))
+ return take_hist(sorted(pool,key=harder),target)
+
+def pantry_pool(count,excluded,seed,tag):
+ curation=json.loads((ROOT/"var/data/pantry_plan_v1/ingredients.json").read_text())
+ source={x["id"]:x for x in curation["ingredients"]}; blocked={x[1] for x in excluded}
+ rng=random.Random(seed); rows=[]; attempts=0
+ while len(rows).5]
+
+def build_pantry_adaptive(target,family_target,support_families,excluded,seed,tag,initial,max_pool=5000,step=500):
+ """Grow one pool, targeting any rare support that blocks exact matching."""
+ curation=json.loads((ROOT/"var/data/pantry_plan_v1/ingredients.json").read_text())
+ source={x["id"]:x for x in curation["ingredients"]}; blocked={x[1] for x in excluded}
+ rng=random.Random(seed); pool=[]; attempts=0
+ def generate(families):
+ nonlocal attempts
+ while True:
+ attempts+=1; family=rng.choice(tuple(families))
+ row=_pantry_row(family=family,split=tag,seed=seed,index=attempts,rng=rng,source_by_id=source,ingredient_table_sha256=curation["ingredient_table_sha256"])
+ if row is None: continue
+ spec=json.loads(row["answer"]); fp_spec=dict(spec); fp_spec.pop("instance_id",None)
+ fingerprint=_canonical_sha256(fp_spec)
+ if fingerprint in blocked: continue
+ row["instance_fingerprint"]=fingerprint; blocked.add(fingerprint); pool.append(row)
+ if len(pool)%250==0: print(f"[level2] {tag} Pantry candidates {len(pool)}",flush=True)
+ return row
+ milestones=list(range(initial,max_pool+1,step))
+ if milestones[-1]!=max_pool: milestones.append(max_pool)
+ for size in milestones:
+ while len(pool)100_000: raise RuntimeError(f"Pantry targeted support {support} exhausted")
+ row=generate(support_families[support])
+ coverage[int(row["answer_mode_count"])]+=1
+ print(f"[level2] {tag} repaired support={support} deficit={deficit} pool={len(pool)}",flush=True)
+ selected=select_pantry(pool,target,family_target,fail=False)
+ print(f"[level2] {tag} Pantry MILP pool={len(pool)} feasible={selected is not None}",flush=True)
+ if selected is not None: return selected,len(pool)
+ raise RuntimeError(f"Pantry exact marginal match infeasible through pool={len(pool)}")
+
+def identity_set(domain,rows):
+ if domain=="pantry": return {("pantry",x["instance_fingerprint"]) for x in rows}
+ return _identities(domain,rows)
+
+def materialize(output=OUTPUT_ROOT,pantry_pool_size=1000):
+ output=output.resolve()
+ if output.exists(): raise FileExistsError(f"refusing overwrite: {output}")
+ output.parent.mkdir(parents=True,exist_ok=True)
+ built={d:{} for d in DOMAINS}; records={d:{} for d in DOMAINS}
+ blocked={d:existing_ids(d) for d in DOMAINS}
+ for domain in ("pantry",)+tuple(d for d in DOMAINS if d!="pantry"):
+ for split,(expected,dataset_split) in SPLITS.items():
+ print(f"[level2] building {domain}/{split}",flush=True)
+ easy=load_rows(LEVEL1[domain][split],dataset_split)
+ if expected%len(easy): raise RuntimeError(f"{domain}/{split} Level-1 size cannot scale exactly")
+ histogram_scale=expected//len(easy)
+ target=Counter({m:count*histogram_scale for m,count in modes(easy).items()})
+ seed=SEEDS[domain]+SPLIT_SEED_OFFSETS[split]; tag=f"level2_{split}"
+ family_target=Counter({family:count*histogram_scale for family,count in Counter(str(x["answer_mode_family"]) for x in easy).items()}) if domain=="pantry" else None
+ support_families={m:sorted({str(x["answer_mode_family"]) for x in easy if int(x["answer_mode_count"])==m}) for m in target} if domain=="pantry" else None
+ if domain=="countdown": rows=build_countdown(target,seed,tag,blocked[domain])
+ elif domain=="graph_coloring": rows=build_graph(target,seed,tag,blocked[domain])
+ elif domain=="python_factors": rows=build_python(target,blocked[domain],seed,tag)
+ elif domain=="mathir": rows=build_mathir(target,blocked[domain],seed,tag)
+ else:
+ pool_size=max(pantry_pool_size,1500 if split=="train" else 1000)
+ rows,pantry_selected_pool=build_pantry_adaptive(target,family_target,support_families,blocked[domain],seed,tag,pool_size)
+ ids=identity_set(domain,rows)
+ verifier=lambda row:(str(row.get("modebench_task","")),str(json.loads(row["answer"]).get("verifier","")))
+ checks={"row_count":len(rows)==expected,"unique_identities":len(ids)==expected,"disjoint_from_all_level1_and_prior_level2":not(ids&blocked[domain]),"exact_support_histogram":modes(rows)==target,"verifier_and_canonicalization_contract":{verifier(x) for x in easy}=={verifier(x) for x in rows}}
+ if domain=="pantry":
+ checks["exact_family_histogram"]=Counter(x["answer_mode_family"] for x in rows)==family_target
+ if not all(checks.values()): raise RuntimeError(f"{domain}/{split} admission failed: {checks}")
+ if domain=="countdown" and not all(len(json.loads(x["answer"])["numbers"])==4 for x in rows): raise RuntimeError("Countdown structural drift")
+ if domain=="graph_coloring" and not all(sum(v is None for v in json.loads(x["answer"])["partial_colors"])==4 for x in rows): raise RuntimeError("graph structural drift")
+ if domain=="python_factors" and not all(len(json.loads(x["answer"])["cases"])==4 and max(json.loads(x["answer"])["cases"])>96 for x in rows): raise RuntimeError("Python structural drift")
+ blocked[domain]|=ids; built[domain][split]=rows
+ records[domain][split]={"seed":seed,"level1_reference_rows":len(easy),"histogram_scale_factor":histogram_scale,"rows":len(rows),"rows_sha256":row_hash(rows),"checks":checks,"answer_mode_count_histogram":dict(sorted(target.items()))}
+ if domain=="pantry":
+ records[domain][split]["family_histogram"]=dict(sorted(family_target.items()))
+ records[domain][split]["selected_candidate_pool_size"]=pantry_selected_pool
+ records[domain][split]["active_dietary_exclusions"]=sum(bool(json.loads(x["answer"])["forbidden_tags"]) for x in rows)
+ staging=Path(tempfile.mkdtemp(prefix=".modebench_harder_v2.",dir=output.parent))
+ try:
+ for domain in DOMAINS:
+ for split,(_,dataset_split) in SPLITS.items(): DatasetDict({dataset_split:Dataset.from_list(built[domain][split])}).save_to_disk(str(staging/domain/split))
+ manifest={"schema":SCHEMA,"status":"pass","decision":"structurally_admitted_pending_frozen_base_model_viability","split_sizes":{k:v[0] for k,v in SPLITS.items()},"domains":records,"level1_reference":{d:{s:str(p.relative_to(ROOT)) for s,p in refs.items()} for d,refs in LEVEL1.items()},"fairness_contract":{"support_histogram":"exact counts when source and target sizes match; exact integer expansion otherwise (Pantry dev 64 to 128 uses factor 2)","identity_disjointness":"all Level-1 and all Level-2 splits","verifier_and_canonicalization":"unchanged","prompt_and_response_budget":"paired evaluator must use identical settings"},"viability_gate":{"metric":"pass@8","minimum":0.10,"maximum":0.90,"harder_than_level1":"level2_pass_at_8 < level1_pass_at_8","freeze":"before treatment training"},"difficulty":{"countdown":"four operands versus three; operands up to 14; within each exact support cell prefer shallow paired-product or one-multiplication targets, then smaller operands","graph_coloring":"four hidden vertices versus three","python_factors":"four cases with at least one value above 96","mathir":"variable-on-both-sides families; within each exact support cell prefer symmetric/cancelling variable coefficients, then larger binding magnitude","pantry":"exact support/family matching; lexicographically maximize active dietary exclusions, then the frozen ingredient-composition difficulty score"}}
+ for name in ("identity.json","admission_fairness_report.json"): (staging/name).write_text(json.dumps(manifest,indent=2,sort_keys=True)+"\n")
+ staging.rename(output); return manifest
+ except BaseException:
+ shutil.rmtree(staging,ignore_errors=True); raise
+
+if __name__=="__main__":
+ import argparse
+ parser=argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--output-root",type=Path,default=OUTPUT_ROOT)
+ parser.add_argument("--pantry-pool-size",type=int,default=1000)
+ args=parser.parse_args()
+ print(json.dumps(materialize(args.output_root,args.pantry_pool_size),indent=2,sort_keys=True))
diff --git a/provenance/source_code/ops/exp_scaling/materialize_modebench_level3.py b/provenance/source_code/ops/exp_scaling/materialize_modebench_level3.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e0fa766b1eff11a81d94878aa7b98327ac095da
--- /dev/null
+++ b/provenance/source_code/ops/exp_scaling/materialize_modebench_level3.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+"""Build disjoint, support-matched Level 3 development candidate pools."""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import hashlib
+import json
+from pathlib import Path
+import sys
+
+ROOT = Path(__file__).resolve().parents[2]
+for path in (ROOT / "src", ROOT / "ops", ROOT / "ops/exp_scaling"):
+ if str(path) not in sys.path:
+ sys.path.insert(0, str(path))
+
+from materialize_modebench_harder_v2 import (
+ DOMAINS, LEVEL1, SPLITS, existing_ids, identity_set, load_rows, modes, row_hash,
+)
+
+REFERENCE = ROOT / "var/data/modebench_harder_v2_matched_r5"
+DEFAULT_OUTPUT = ROOT / "var/data/modebench_level3_calibration_v1"
+SEEDS = {domain: 6317100 + 10000 * i for i, domain in enumerate(DOMAINS)}
+
+
+def write_json(path: Path, value) -> None:
+ path.parent.mkdir(parents=True, exist_ok=True)
+ with path.open("x") as handle:
+ json.dump(value, handle, indent=2, sort_keys=True)
+ handle.write("\n")
+
+
+def historical_ids(domain: str) -> set:
+ blocked = existing_ids(domain)
+ data_root = ROOT / "var/data"
+ prior_roots = set(data_root.glob("modebench_harder*")) | set(data_root.glob("modebench_level3*"))
+ for root in sorted(prior_roots):
+ if not root.is_dir():
+ continue
+ for split, (_, dataset_split) in SPLITS.items():
+ path = root / domain / split
+ if path.is_dir() and (path / "dataset_dict.json").exists():
+ from datasets import load_from_disk
+ dataset = load_from_disk(str(path))
+ for subset in dataset.values():
+ blocked |= identity_set(domain, [dict(row) for row in subset])
+ return blocked
+
+
+def reference_rows(domain: str, split: str) -> list[dict]:
+ return load_rows(REFERENCE / domain / split, SPLITS[split][1])
+
+
+def generator(domain: str):
+ if domain == "graph_coloring":
+ from modebench_level3_graph_v6 import build_pool
+ elif domain == "python_factors":
+ from modebench_level3_python_v4 import build_pool
+ elif domain == "mathir":
+ from modebench_level3_mathir_sign_v1 import build_pool
+ elif domain == "countdown":
+ from modebench_level3_countdown_v3 import build_pool
+ else:
+ from modebench_level3_constraints import build_pool
+ return build_pool
+
+
+def verify_rows(domain: str, rows: list[dict], reference: list[dict],
+ target: Counter, blocked: set) -> dict:
+ ids = identity_set(domain, rows)
+ def contract(row):
+ return (row["modebench_task"], json.loads(row["answer"])["verifier"])
+ checks = {
+ "row_count": len(rows) == sum(target.values()),
+ "unique_identities": len(ids) == len(rows),
+ "historical_and_cross_split_disjointness": not (ids & blocked),
+ "exact_support_histogram": modes(rows) == target,
+ "unchanged_verifier_contract": {contract(r) for r in rows} ==
+ {contract(r) for r in reference},
+ }
+ if not all(checks.values()):
+ raise RuntimeError(f"{domain} structural checks failed: {checks}")
+ return checks
+
+
+def build_development_pool(domain: str, difficulty: int, output: Path,
+ multiplier: int = 1) -> dict:
+ directory = output / "pools" / domain
+ rows_path = directory / f"difficulty_{difficulty}.jsonl"
+ manifest_path = rows_path.with_suffix(".identity.json")
+ if rows_path.exists() or manifest_path.exists():
+ raise FileExistsError(rows_path)
+ reference = reference_rows(domain, "dev")
+ target = Counter({k: v * multiplier for k, v in modes(reference).items()})
+ blocked = historical_ids(domain)
+ pool_directories = {directory}
+ pool_directories.update(root / "pools" / domain for root in (ROOT / "var/data").glob("modebench_level3_calibration*"))
+ for pool_directory in sorted(pool_directories):
+ for path in sorted(pool_directory.glob("*.jsonl")):
+ blocked |= identity_set(domain, [json.loads(line) for line in path.read_text().splitlines()])
+ seed = SEEDS[domain] + 1000 * difficulty
+ extra = {}
+ if domain == "pantry":
+ extra["joint_target"] = Counter({k: v * multiplier for k, v in
+ Counter((int(r["answer_mode_count"]), r["answer_mode_family"]) for r in reference).items()})
+ rows = generator(domain)(domain, target, blocked, seed, "level3_development_pool",
+ difficulty, multiplier=1, **extra)
+ checks = verify_rows(domain, rows, reference, target, blocked)
+ if domain == "pantry":
+ checks["exact_family_histogram"] = Counter((int(r["answer_mode_count"]), r["answer_mode_family"]) for r in rows) == extra["joint_target"]
+ if not checks["exact_family_histogram"]:
+ raise RuntimeError("Pantry family histogram drift")
+ directory.mkdir(parents=True, exist_ok=True)
+ with rows_path.open("x") as handle:
+ for row in rows:
+ handle.write(json.dumps(row, sort_keys=True) + "\n")
+ record = {
+ "schema": "modebench_level3_development_pool_v1", "domain": domain,
+ "difficulty": difficulty, "seed": seed, "rows": len(rows),
+ "rows_sha256": row_hash(rows), "checks": checks,
+ "support_histogram": dict(sorted(target.items())),
+ "source_sha256": hashlib.sha256(Path(sys.modules[generator(domain).__module__].__file__).read_bytes()).hexdigest(),
+ "information_boundary": "development candidates only; no confirmation model outcomes used",
+ }
+ write_json(manifest_path, record)
+ return record
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--domain", required=True, choices=DOMAINS)
+ parser.add_argument("--difficulty", required=True, type=int, choices=range(4))
+ parser.add_argument("--output-root", type=Path, default=DEFAULT_OUTPUT)
+ parser.add_argument("--multiplier", type=int, default=1)
+ args = parser.parse_args()
+ if args.multiplier < 1:
+ parser.error("multiplier must be positive")
+ print(json.dumps(build_development_pool(args.domain, args.difficulty,
+ args.output_root, args.multiplier), sort_keys=True))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/provenance/source_code/ops/exp_scaling/modebench_level3_v3_common.py b/provenance/source_code/ops/exp_scaling/modebench_level3_v3_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dca49050d4bcb45cd0cafcb199b2ed0330a51cf
--- /dev/null
+++ b/provenance/source_code/ops/exp_scaling/modebench_level3_v3_common.py
@@ -0,0 +1,357 @@
+#!/usr/bin/env python3
+"""Shared v3 boundaries for adaptive matching to fixed measured L1 references.
+
+The previous failed confirmation remains immutable. Historical L1 outcomes are
+explicit benchmark targets; only new candidate DEV outcomes may fit a recipe.
+No registration, dataset, receipt, fit or scheduler action is published here.
+"""
+from __future__ import annotations
+
+from collections import Counter
+from copy import deepcopy
+import hashlib
+import importlib.metadata
+import json
+import os
+from pathlib import Path
+import sys
+import tempfile
+
+ROOT = Path(__file__).resolve().parents[2]
+for directory in ('ops', 'ops/exp_scaling', 'src'):
+ if str(ROOT / directory) not in sys.path:
+ sys.path.insert(0, str(ROOT / directory))
+
+OLD_CAMPAIGN = ROOT / 'var/artifacts/modebench_level3_v2'
+OLD_DATASET = ROOT / 'var/data/modebench_level3_matched_v2'
+CAMPAIGN = ROOT / 'var/artifacts/modebench_level3_v3'
+RESULTS = ROOT / 'var/results/modebench_level3_v3'
+DATASET = ROOT / 'var/data/modebench_level3_matched_v3'
+REGISTRATION = CAMPAIGN / 'registration.json'
+OLD_SEAL = OLD_CAMPAIGN / 'confirmation_python_v6/seal.json'
+OLD_SEAL_SHA = '5c9cae0c3d5509db20b8c22418da759710b2a9540eac74cc81ce8e743adca79c'
+OLD_REPORT = OLD_CAMPAIGN / 'confirmation_python_v6/confirmation_report.json'
+OLD_REPORT_SHA = 'fd77c76274b81c73bfb78626666cb4078d4b3968eecd5cac3b05f9e33c854b12'
+OLD_REPLAY = OLD_CAMPAIGN / 'confirmation_python_v6_completed_review/original_grader_replay.json'
+OLD_REPLAY_SHA = '62c112aa891d47f4e5db359f62d5fb1047235d64caeea55cd2c7b8cf72b4c6d5'
+DOMAINS = ('countdown', 'graph_coloring', 'python_factors', 'mathir', 'pantry')
+RETAINED = ('countdown', 'mathir', 'pantry')
+REVISED = ('graph_coloring', 'python_factors')
+DEV_LABELS = (6428000, 6428001, 6428002, 6428003)
+CONF_LABELS = (6429000, 6429001, 6429002, 6429003)
+REFERENCE_LABELS = (6329000, 6329001, 6329002, 6329003)
+TOLERANCES = {'pass1': 0.04, 'pass8': 0.08}
+SPLITS = {'train': 384, 'dev': 128, 'eval': 128}
+SELECTION_SEED = 6391701
+SEEDS = {
+ 'graph_coloring': {'development': 8737100, 'capacity': 8937100, 'train': 9137100, 'eval': 9337100},
+ 'python_factors': {'development': 8837100, 'capacity': 9037100, 'train': 9237100, 'eval': 9437100},
+}
+POOL_ROOTS = {domain: ROOT / ('var/data/modebench_level3_calibration_' + revision)
+ for domain, revision in zip(REVISED, ('graph_v8', 'python_v7'))}
+TARGET_SHA = {
+ 'countdown': 'd333ba84408706e031d71f95a8843b3462fe948acbcb7fe3c22dd2a6f78c8993',
+ 'graph_coloring': 'c46133cbefebd63c024f2c25edeb2ebd02a3c6aab5c4a696a0e5d067c6aaf9c4',
+ 'python_factors': '6787a0df405740a5846be55d675783d4a8b48912a71490dc96a7629f71b43cb9',
+ 'mathir': 'a025c4f943a7407b97944a2d0b57ca929c725b60465cfc0d1db5073af17cef58',
+ 'pantry': '6c623f16392f671f53eda9b9a41262d81a9a068f344bbfa7fb795765d7aff091',
+}
+REGISTRATION_SCHEMA = 'modebench_level3_v3_fixed_reference_registration_v1'
+
+
+def require(ok, message):
+ if not ok:
+ raise ValueError(message)
+
+
+def read(path):
+ return json.loads(Path(path).read_text())
+
+
+def digest(path):
+ value = hashlib.sha256()
+ with Path(path).open('rb') as handle:
+ for part in iter(lambda: handle.read(1024 * 1024), b''):
+ value.update(part)
+ return value.hexdigest()
+
+
+def sha(value):
+ return hashlib.sha256(json.dumps(value, sort_keys=True, separators=(',', ':'), allow_nan=False).encode()).hexdigest()
+
+
+def merge_pins(*mappings):
+ result = {}
+ for mapping in mappings:
+ for path, expected in mapping.items():
+ require(path not in result or result[path] == expected, f'conflicting immutable pin: {path}')
+ result[path] = expected
+ return result
+
+
+def verify_pins(files, directory_files=None):
+ for path, expected in files.items():
+ require(digest(path) == expected, f'immutable file changed: {path}')
+ for directory, expected in (directory_files or {}).items():
+ actual = sorted(str(path.resolve()) for path in Path(directory).rglob('*') if path.is_file())
+ require(actual == expected and all(path in files for path in actual),
+ f'immutable directory inventory changed: {directory}')
+
+
+def atomic_new(path, payload):
+ """Publish one new artifact exclusively; never replace an existing record."""
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ require(not path.exists(), f'fresh output required: {path}')
+ fd, temporary = tempfile.mkstemp(prefix='.' + path.name + '.', dir=path.parent)
+ try:
+ with os.fdopen(fd, 'w') as handle:
+ json.dump(payload, handle, sort_keys=True, indent=2, allow_nan=False)
+ handle.write('\n')
+ handle.flush()
+ os.fsync(handle.fileno())
+ os.link(temporary, path)
+ finally:
+ Path(temporary).unlink(missing_ok=True)
+
+
+def inherited_metadata():
+ return {'seal': {'path': str(OLD_SEAL), 'sha256': OLD_SEAL_SHA},
+ 'report': {'path': str(OLD_REPORT), 'sha256': OLD_REPORT_SHA},
+ 'original_grader_replay': {'path': str(OLD_REPLAY), 'sha256': OLD_REPLAY_SHA}}
+
+
+def benchmark_metadata():
+ return {domain: {'role': 'frozen_benchmark_reference',
+ 'receipt_path': str(ROOT / f'var/results/modebench_level3_v2/confirmation_python_v6/confirmation_05b_{domain}.json'),
+ 'receipt_sha256': expected}
+ for domain, expected in TARGET_SHA.items()}
+
+
+def contract():
+ """JSON-compatible fixed policy; generation and sampling namespaces differ."""
+ return {'reference_semantics': 'fixed_measured_level1_benchmark', 'adaptive_confirmation_round': 2,
+ 'retained_domains': list(RETAINED), 'revised_domains': list(REVISED),
+ 'split_sizes': dict(SPLITS), 'development_draw_labels': list(DEV_LABELS),
+ 'confirmation_draw_labels': list(CONF_LABELS), 'historical_reference_draw_labels': list(REFERENCE_LABELS),
+ 'seed_policy': 'sha256_domain_problem_draw_aligned_n8_v2', 'samples_per_draw': 8,
+ 'selection_seed': SELECTION_SEED, 'weight_denominator': 20, 'weight_combinations': 1771,
+ 'tolerances': dict(TOLERANCES), 'selected_development_sets_scored': 1,
+ 'selected_evaluation_sets_scored': 0,
+ 'selection_algorithm': 'dual_full_pool_dev_eval_forecasts_then_one_fixed_dev_selection_v3',
+ 'weight_objective': 'minimax_normalized_dev_eval_forecast_then_squared_sum_then_weights',
+ 'forecast_reference_splits': ['dev', 'eval'], 'selected_reference_split': 'dev',
+ 'required_development_gates': ['expected_dev', 'expected_eval', 'selected'],
+ 'calibration_quota_rule': 'componentwise_maximum_of_level2_dev_and_eval_cells',
+ 'calibration_rows_per_tier': {'graph_coloring': 142, 'python_factors': 166},
+ 'generation_seed_bases': deepcopy(SEEDS), 'generation_tier_stride': 1000,
+ 'pool_roots': {domain: str(path) for domain, path in POOL_ROOTS.items()},
+ 'dataset_root': str(DATASET), 'results_root': str(RESULTS),
+ 'old_rng_sources': 47, 'old_request_blocks': 23808,
+ 'new_development_sources': 8, 'new_development_request_blocks': 4928,
+ 'new_confirmation_sources': 2, 'new_confirmation_request_blocks': 1024,
+ 'total_sources': 57, 'total_request_blocks': 29760, 'total_child_seeds': 238080,
+ 'historical_level1_confirmation_used_as_fixed_reference': True,
+ 'historical_level3_confirmation_used_for_mixture_fitting': False,
+ 'new_candidate_fitting_uses_development_outcomes_only': True,
+ 'fresh_heldout_claim_applies_to_revised_level3_only': True,
+ 'all_five_fresh_same_round': False, 'statistical_equivalence_claimed': False,
+ 'treatment_training_started': False}
+
+
+def authenticate_inherited():
+ """Authenticate completed old evidence without repeating grading or fitting."""
+ for item in inherited_metadata().values():
+ require(digest(item['path']) == item['sha256'], 'inherited confirmation anchor changed')
+ replay, report, seal = read(OLD_REPLAY), read(OLD_REPORT), read(OLD_SEAL)
+ require(replay.get('schema') == 'modebench_level3_python_v6_completed_confirmation_original_grader_replay_v1'
+ and replay.get('status') == 'verified_original_grader_replay'
+ and replay.get('attempts') == 40960 and replay.get('batches') == 640
+ and replay.get('jobs') == 10 and replay.get('rows') == 1280
+ and replay.get('every_attempt_regraded_including_failures') is True
+ and replay.get('full_canonical_keys_compared') is True
+ and replay.get('recorded_verification_outcomes_regraded') is True
+ and replay.get('draw_prompt_cell_metrics_and_all_five_gates_recomputed') is True
+ and replay.get('report_sha256') == OLD_REPORT_SHA
+ and replay.get('scientific_seal_sha256') == OLD_SEAL_SHA,
+ 'complete original-grader historical replay required')
+ require(report.get('status') == 'outside_match_tolerance' and report.get('phase') == 'confirmation'
+ and report.get('all_five_domains_complete') is True
+ and report.get('confirmation_match_verified') is False
+ and set(report.get('domains', {})) == set(DOMAINS)
+ and report.get('errors') == {} and report.get('missing_domains') == [],
+ 'failed all-five historical confirmation must remain intact')
+ require(len(seal['files_sha256']) == 2232 and len(seal['directory_files']) == 79
+ and len(replay['files_sha256']) == 2978 and len(replay['directory_files']) == 89,
+ 'inherited complete input/evidence coverage differs')
+ require(all(replay['files_sha256'].get(path) == expected for path, expected in seal['files_sha256'].items()),
+ 'replay omits an original sealed input')
+ files = merge_pins(replay['files_sha256'], {item['path']: item['sha256'] for item in inherited_metadata().values()})
+ verify_pins(files, replay['directory_files'])
+ from evaluate_modebench_level3_independent import model_identity
+ for label, expected in seal['models'].items():
+ actual = model_identity(Path(expected['path']), label)
+ actual['vllm_version'] = importlib.metadata.version('vllm')
+ require(actual == expected, f'frozen model/engine changed: {label}')
+ return {'report': report, 'seal': seal, 'replay': replay, 'models': seal['models'],
+ 'files_sha256': files, 'directory_files': deepcopy(replay['directory_files']),
+ 'metadata': inherited_metadata()}
+
+
+def reference_histograms(domain):
+ require(domain in DOMAINS, 'unknown reference domain')
+ from fit_modebench_level3 import cell_histogram
+ from materialize_modebench_level3 import reference_rows
+ return {split: cell_histogram(domain, reference_rows(domain, split)) for split in SPLITS}
+
+
+def calibration_histogram(domain):
+ require(domain in REVISED, 'only revised domains receive fresh calibration')
+ histograms = reference_histograms(domain)
+ keys = set(histograms['dev']) | set(histograms['eval'])
+ result = Counter({key: max(histograms['dev'][key], histograms['eval'][key]) for key in keys})
+ require(sum(result.values()) == contract()['calibration_rows_per_tier'][domain], 'calibration quota total differs')
+ return result
+
+
+def fixed_references():
+ """All five original eval receipts remain eval evidence, never synthetic DEV."""
+ inherited = authenticate_inherited()
+ from audit_modebench_level3_python_v6_independent_match import validate_receipt, verify_dataset
+ from evaluate_modebench_level3_independent import load_rows
+ from fit_modebench_level3 import cell_histogram, serialize_cells
+ targets = {}
+ for domain, metadata in benchmark_metadata().items():
+ path = Path(metadata['receipt_path'])
+ require(digest(path) == metadata['receipt_sha256'], 'frozen measured target changed')
+ receipt = read(path)
+ values = validate_receipt(receipt, domain=domain, role='baseline', development=False)
+ recorded = inherited['report']['domains'][domain]
+ require(recorded['receipts']['baseline']['sha256'] == metadata['receipt_sha256']
+ and recorded['receipts']['baseline']['path'] == str(path)
+ and receipt['identity']['model'] == inherited['models']['05b']
+ and receipt['identity']['seeds'] == list(REFERENCE_LABELS), 'fixed target model/source/seed provenance differs')
+ source_path = Path(recorded['source_datasets']['baseline']['path'])
+ verify_dataset(receipt, source_path)
+ rows, source = load_rows({'domain': domain, 'dataset': str(source_path), 'row_offset': 0, 'row_limit': 0})
+ require(len(rows) == len(values) == 128 and source == receipt['identity']['source'], 'fixed reference source differs')
+ histograms = reference_histograms(domain)
+ baseline_cells = cell_histogram(domain, rows)
+ baseline_support, reference_support = Counter(), Counter()
+ for cell, count in baseline_cells.items():
+ baseline_support[cell[0]] += count
+ for cell, count in histograms['eval'].items():
+ reference_support[cell[0]] += count
+ require(baseline_support == reference_support, 'fixed reference marginal support differs from original EVAL reference')
+ metrics = {metric: sum(row[metric] for row in values) / len(values) for metric in TOLERANCES}
+ require(all(metrics[metric] == recorded['means']['baseline'][metric] for metric in TOLERANCES),
+ 'frozen reference metrics differ from completed report')
+ if domain in RETAINED:
+ require(recorded['observed_approximate_match'] is True
+ and recorded['within_tolerance'] == {'pass1': True, 'pass8': True}, 'retained domain did not pass')
+ targets[domain] = {'receipt': receipt, 'rows': rows, 'metrics': metrics,
+ 'provenance': {**metadata, 'identity_sha256': receipt['identity_sha256'],
+ 'source': source, 'draw_labels': list(REFERENCE_LABELS),
+ 'historical_level1_cells': serialize_cells(baseline_cells),
+ 'level2_eval_cells': serialize_cells(histograms['eval']),
+ 'marginal_support_matches_level2_eval': True,
+ 'joint_cells_match_level2_eval': baseline_cells == histograms['eval'],
+ 'historical_report_sha256': OLD_REPORT_SHA,
+ 'original_grader_replay_sha256': OLD_REPLAY_SHA}}
+ verify_pins(inherited['files_sha256'], inherited['directory_files'])
+ return targets
+
+
+def old_rng_inventory():
+ """All old DEV and CONF schedules remain excluded, including failed ranges."""
+ inherited = authenticate_inherited()
+ from audit_modebench_level3_python_v6_independent_match import authenticated_development_sources
+ from evaluate_modebench_level3_independent import load_rows, schedule_record, validate_task
+ development = authenticated_development_sources()
+ blocks = set(development['blocks'])
+ require(len(development['manifest']) == 37 and len(blocks) == 18688, 'old development inventory differs')
+ manifests = [{'phase': 'historical_development', **item} for item in development['manifest']]
+ plan = read(OLD_CAMPAIGN / 'confirmation_python_v6/plan.json')
+ sealed_sources = {item['name']: item for item in inherited['seal']['sources']}
+ for job in plan['jobs']:
+ task = read(job['tasks'])[0]
+ validate_task(task, True)
+ rows, source = load_rows(task)
+ schedule = schedule_record(job['domain'], rows, task['seeds'])
+ current = {base for row in schedule['request_seeds'] for base in row}
+ expected = sealed_sources[job['name']]
+ require(source == expected['identity'] and sha(schedule) == expected['seed_schedule_sha256']
+ and len(current) == 512 and task['seeds'] == list(REFERENCE_LABELS)
+ and not current & blocks and all(base % 8 == 0 for base in current), 'old confirmation RNG source differs')
+ blocks |= current
+ manifests.append({'phase': 'historical_confirmation', **expected, 'tasks': job['tasks'],
+ 'tasks_sha256': digest(job['tasks']), 'seeds': task['seeds']})
+ require(len(manifests) == 47 and len(blocks) == 23808, 'complete historical RNG inventory differs')
+ return {'manifests': manifests, 'blocks': blocks, 'files_sha256': inherited['files_sha256'],
+ 'directory_files': inherited['directory_files']}
+
+
+def validate_registration(path, expected_sha256):
+ """Caller-supplied pin prevents registration/source hash cycles."""
+ path = Path(path).resolve()
+ require(path == REGISTRATION and isinstance(expected_sha256, str) and len(expected_sha256) == 64
+ and digest(path) == expected_sha256, 'explicit canonical v3 registration pin required')
+ record = read(path)
+ require(record.get('schema') == REGISTRATION_SCHEMA and record.get('contract') == contract()
+ and record.get('inherited_confirmation') == inherited_metadata()
+ and record.get('benchmark_targets') == benchmark_metadata(), 'prospective v3 registration contract differs')
+ require(set(record.get('candidate_revisions', {})) == set(REVISED), 'both new candidate revisions required')
+ require(str(path) not in record['files_sha256'], 'registration cannot pin itself')
+ require(record['files_sha256'].get(str(Path(__file__).resolve())) == digest(__file__), 'common implementation not registered')
+ for domain, revision in zip(REVISED, ('graph_v8', 'python_v7')):
+ descriptor = record['candidate_revisions'][domain]
+ require(descriptor.get('name') == revision and descriptor.get('domain') == domain
+ and descriptor.get('pool_root') == str(POOL_ROOTS[domain])
+ and descriptor.get('rows_per_tier') == contract()['calibration_rows_per_tier'][domain],
+ 'canonical candidate revision/pool/quota differs')
+ for kind, relative in (
+ ('generator', f'ops/exp_scaling/modebench_level3_{revision}.py'),
+ ('materializer', f'ops/exp_scaling/materialize_modebench_level3_{revision}.py'),
+ ('tests', f'tests/test_modebench_level3_{revision}.py')):
+ source = str(ROOT / relative)
+ require(descriptor.get(kind + '_path') == source
+ and descriptor.get(kind + '_sha256') == digest(source)
+ and record['files_sha256'].get(source) == digest(source),
+ 'candidate implementation/test bytes not registered')
+ require(descriptor.get('development_receipts') == {
+ str(tier): str(RESULTS / f'calibration_3b_{revision}_d{tier}.json') for tier in range(4)},
+ 'candidate DEV receipt namespace differs')
+ for key, seed_kind in (('development_seed_base', 'development'), ('capacity_seed_base', 'capacity'),
+ ('final_train_seed_base', 'train'), ('final_eval_seed_base', 'eval')):
+ require(descriptor.get(key) == SEEDS[domain][seed_kind], 'candidate generation seed differs')
+ snapshot = descriptor.get('source_snapshot', {})
+ require(isinstance(snapshot.get('files_sha256'), dict) and bool(snapshot['files_sha256'])
+ and isinstance(snapshot.get('directory_files'), dict)
+ and all(record['files_sha256'].get(source) == expected
+ for source, expected in snapshot['files_sha256'].items())
+ and all(record['directory_files'].get(source) == expected
+ for source, expected in snapshot['directory_files'].items()),
+ 'candidate source snapshot omitted from registration')
+ inherited = authenticate_inherited()
+ require(all(record['files_sha256'].get(source) == expected for source, expected in inherited['files_sha256'].items()),
+ 'v3 registration omits inherited evidence')
+ require(all(record['directory_files'].get(source) == expected for source, expected in inherited['directory_files'].items()),
+ 'v3 registration omits inherited inventories')
+ verify_pins(record['files_sha256'], record['directory_files'])
+ require(digest(path) == expected_sha256, 'registration changed during authentication')
+ return record
+
+
+def validate_new_candidate_receipt(path, domain, labels=DEV_LABELS):
+ """New candidate DEV outcomes; no historical candidate CONF can enter fit."""
+ require(domain in REVISED and tuple(labels) == DEV_LABELS, 'registered new candidate DEV labels required')
+ from fit_modebench_level3 import load_receipt
+ from evaluate_modebench_level3_independent import validate_seed_receipt
+ from audit_modebench_level3_python_v6_independent_match import validate_receipt
+ before = digest(path)
+ receipt, rows, scores = load_receipt(path, domain, 'level3', '3b')
+ require(receipt['identity']['seeds'] == list(DEV_LABELS), 'candidate uses another round of draws')
+ validate_seed_receipt(receipt, rows=rows)
+ validate_receipt(receipt, domain=domain, role='candidate', development=True)
+ require(digest(path) == before, 'candidate receipt changed during verification')
+ return receipt, rows, scores
diff --git a/provenance/source_code/ops/modebench_independent_seeds.py b/provenance/source_code/ops/modebench_independent_seeds.py
new file mode 100644
index 0000000000000000000000000000000000000000..afe1df64fbfae0d1c6e1c87941cc7c487f845db2
--- /dev/null
+++ b/provenance/source_code/ops/modebench_independent_seeds.py
@@ -0,0 +1,55 @@
+"""Target-blind, disjoint n=8 RNG blocks for ModeBench prompt/draw pairs.
+
+vLLM 0.8.4 V0 expands an n=8 request with seed s into eight requests
+with seeds s through s+7. Consecutive draw labels must therefore never
+be passed directly to SamplingParams. This policy hashes each prompt
+and draw label into an aligned block; it is invariant to task sharding,
+row ordering, model identity, answers, and dataset metadata.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import Sequence
+
+POLICY = "sha256_domain_problem_draw_aligned_n8_v2"
+SAMPLES = 8
+BLOCK_BITS = 60
+
+
+def request_seed(domain: str, problem: str, draw_label: int) -> int:
+ """Return a signed-64-bit-safe base seed, reserving the next seven."""
+ if not isinstance(domain, str) or not domain:
+ raise ValueError("domain must be a nonempty string")
+ if not isinstance(problem, str) or not problem:
+ raise ValueError("problem must be a nonempty string")
+ if type(draw_label) is not int or draw_label < 0:
+ raise ValueError("draw label must be a nonnegative integer")
+ payload = json.dumps([POLICY, domain, problem, draw_label],
+ ensure_ascii=False, separators=(",", ":")).encode("utf-8")
+ block = int.from_bytes(hashlib.sha256(payload).digest()[:8], "big") >> 4
+ return block * SAMPLES
+
+
+def seed_schedule(domain: str, problems: Sequence[str],
+ draw_labels: Sequence[int]) -> list[list[int]]:
+ """Build rows-by-draw request seeds, refusing duplicate inputs/collisions.
+
+ Hash collisions are very unlikely but must be detected before sampling;
+ selecting another seed after a collision is not part of this policy.
+ """
+ if not problems or not draw_labels:
+ raise ValueError("nonempty prompts and draw labels required")
+ if len(set(problems)) != len(problems):
+ raise ValueError("duplicate prompt text is not an independent problem")
+ if len(set(draw_labels)) != len(draw_labels):
+ raise ValueError("duplicate draw label")
+ schedule = [[request_seed(domain, problem, label) for label in draw_labels]
+ for problem in problems]
+ flat = [seed for row in schedule for seed in row]
+ if any(seed % SAMPLES or seed < 0 or seed + SAMPLES - 1 >= 2**63
+ for seed in flat):
+ raise ValueError("unaligned or out-of-range RNG block")
+ if len(flat) != len(set(flat)):
+ raise ValueError("RNG block collision; refuse sampling")
+ return schedule
diff --git a/provenance/source_code/tests/test_modebench_hf_release.py b/provenance/source_code/tests/test_modebench_hf_release.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db25a627511310758bc0f08d360f9ae5f6bda54
--- /dev/null
+++ b/provenance/source_code/tests/test_modebench_hf_release.py
@@ -0,0 +1,75 @@
+"""Full source-to-standard-loader roundtrip and portable verifier checks."""
+from __future__ import annotations
+import hashlib,json,os,shutil,subprocess,sys
+from pathlib import Path
+import pytest
+from datasets import load_dataset,load_from_disk
+
+ROOT=Path(__file__).resolve().parents[1]
+PACKAGE=ROOT/'var/artifacts/modebench_hf_release_20260911/package'
+CONFIGS=[f'level{level}_{domain}' for level in (1,2,3) for domain in ('graph_coloring','countdown','python_factors','mathir','pantry_plan')]+['level1_graph_coloring_unique_answer']
+
+@pytest.fixture(scope='session')
+def manifest():return json.loads((PACKAGE/'MANIFEST.json').read_text())
+
+@pytest.mark.parametrize('config',CONFIGS)
+def test_standard_huggingface_loader_preserves_every_row(config,manifest,tmp_path):
+ loaded=load_dataset(str(PACKAGE),config,cache_dir=str(tmp_path/'cache'),keep_in_memory=True)
+ records=[s for s in manifest['splits'] if s['config_name']==config]
+ assert set(loaded)=={s['split'] for s in records}
+ for record in records:
+ original=load_from_disk(str(ROOT/record['source_repository_path']))[record['original_datasetdict_split']]
+ result=loaded[record['split']]
+ assert list(result)==list(original)
+ assert result.features.to_dict()==original.features.to_dict()
+ assert len(result)==record['rows']
+ raw=json.dumps(list(result),sort_keys=True,separators=(',',':'),allow_nan=False).encode()
+ assert hashlib.sha256(raw).hexdigest()==record['rows_sha256']
+
+
+def test_original_auxiliary_and_split_roles_are_explicit(manifest):
+ assert manifest['config_count']==16 and manifest['split_count']==42 and manifest['row_count']==9152
+ primary=[s for s in manifest['splits'] if s['reported_primary_split']]
+ assert sum(s['rows'] for s in primary)==9024
+ assert not any(s['split']=='test' for s in manifest['splits'])
+ assert [(s['config_name'],s['rows']) for s in primary if s['level']==1 and s['split']=='dev']==[('level1_pantry_plan',64)]
+ aux=[s for s in manifest['splits'] if not s['reported_primary_split']]
+ assert [(s['config_name'],s['split'],s['rows']) for s in aux]==[('level1_graph_coloring_unique_answer','eval',128)]
+
+
+def test_portable_frozen_verifier_accepts_and_rejects_all_five_domains(tmp_path):
+ copied=tmp_path/'code';shutil.copytree(PACKAGE/'code',copied)
+ pantry=load_dataset('parquet',data_files=str(PACKAGE/'data/level1_pantry_plan/eval.parquet'),cache_dir=str(tmp_path/'cache'),keep_in_memory=True)['train'][0]
+ (tmp_path/'pantry.json').write_text(pantry['answer'])
+ script=r'''
+import sys,json,itertools
+from pathlib import Path
+sys.path.insert(0,sys.argv[1])
+import oat_drgrpo.math_grader as grader
+assert Path(grader.__file__).resolve().is_relative_to(Path(sys.argv[1]).resolve())
+from oat_drgrpo.pantry_support_action import decode_pantry_support_mask
+f=grader.validated_modebench_outcome_key
+cases=[
+ ({'verifier':'graph_coloring','n':4,'edges':[[1,2],[2,3],[3,4]],'partial_colors':[1,None,None,2]},'21','graph_coloring:1212'),
+ ({'verifier':'countdown','numbers':[2,3,4],'target':14},'2+3*4','countdown:add(2,mul(3,4))'),
+ ({'verifier':'python_factor_function','python_version':'factor-v1','cases':[6,10,15]},'lambda n: 2 if n % 2 == 0 else 3','python_factor:2,2,3'),
+ ({'verifier':'mathir_action_menu','mathir_version':'linear-menu-v1','bindings':{'a':5,'b':2,'c':8,'d':2},'initial_lhs':'add(mul(a,x),b)','initial_rhs':'add(mul(d,x),c)','max_steps':4,'actions':{'A':'sub(b)','B':'sub(mul(d,x))','C':'div(sub(a,d))','D':'sub(add(mul(d,x),b))','E':'add(b)','F':'div(a)'},'support_is_open':False,'num_completions':5},'A;B;C',None),
+]
+for spec,response,expected in cases:
+ reference=json.dumps(spec);key=f(response,reference)
+ assert key is not None,(spec['verifier'],'valid response rejected')
+ if expected is not None:assert key==expected
+ assert f('not a valid answer',reference) is None
+spec=json.loads(Path(sys.argv[2]).read_text());reference=json.dumps(spec)
+for bits in itertools.product('01',repeat=6):
+ response=decode_pantry_support_mask(''.join(bits),spec);key=f(response,reference)
+ if key is not None:break
+else:raise AssertionError('No feasible Pantry support in preserved eval row')
+assert key.startswith('pantry:') or key.startswith('pantry_plan:')
+assert f(decode_pantry_support_mask('000000',spec),reference) is None
+print(json.dumps({'domains_verified':5,'portable_python_worker':True,'pantry_adapter_before_grader':True}))
+'''
+ env=dict(os.environ,PYTHONDONTWRITEBYTECODE='1');env.pop('PYTHONPATH',None)
+ done=subprocess.run([sys.executable,'-B','-c',script,str(copied),str(tmp_path/'pantry.json')],cwd=tmp_path,env=env,text=True,capture_output=True,timeout=45)
+ assert done.returncode==0,done.stdout+done.stderr
+ assert json.loads(done.stdout.strip())['domains_verified']==5
diff --git a/provenance/source_data/exact_countdown_easy3_probe/eval/dataset_dict.json b/provenance/source_data/exact_countdown_easy3_probe/eval/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/eval/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/dataset_info.json b/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/state.json b/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..76c9388cc9deea0b527bcb3431734151604101d5
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/eval/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "e8ea659fc3550420",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/exact_countdown_easy3_probe/train/dataset_dict.json b/provenance/source_data/exact_countdown_easy3_probe/train/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..40d5c04b431649b5c469de1fd03044ba97b6fded
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/train/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["train"]}
\ No newline at end of file
diff --git a/provenance/source_data/exact_countdown_easy3_probe/train/train/dataset_info.json b/provenance/source_data/exact_countdown_easy3_probe/train/train/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/train/train/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/exact_countdown_easy3_probe/train/train/state.json b/provenance/source_data/exact_countdown_easy3_probe/train/train/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..67858f3ed56a9481782b3ef9a00df41c3071dec2
--- /dev/null
+++ b/provenance/source_data/exact_countdown_easy3_probe/train/train/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "3a8e014f1b2c9a37",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/eval/dataset_dict.json b/provenance/source_data/graph_coloring_modebench_v2/eval/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..aa7c0f1e0ccb96d330850535916e18ee4bc36082
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/eval/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer", "unique_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/dataset_info.json b/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/state.json b/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..16d25a6197aea9df9d43bf5de62cb222a701ecb1
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/eval/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "16f3b9a0093b2705",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/dataset_info.json b/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/state.json b/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..b56860156dbefd057cfc78aed7afd93ddf28dd5e
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/eval/unique_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "9fee26682646e7ce",
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+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/identity.json b/provenance/source_data/graph_coloring_modebench_v2/identity.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bcc0f6dcf87f0a9a7ed1f5ead8e994960873784
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/identity.json
@@ -0,0 +1,44 @@
+{
+ "data_tree_sha256": "d27ec343a2adf2522680474842db29b31eaaa9cb5febf32c578a09dbd410cad4",
+ "distinct_problems": {
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+ "train": 384
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+ "eval_multi_answer_support": {
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+ "mean": 6.2656,
+ "min": 4
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+ "generator": "ops/make_exact_answer_mode_data.py",
+ "generator_sha256": "cb59292a32c1af7789676e7dd6eeffaf96a2937b01c14e4141b22ed223f66cba",
+ "overlap_with_superseded": {
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+ "new_train_vs_old_eval": 4
+ },
+ "schema": "graph_coloring_modebench_v2",
+ "seed": 0,
+ "split_overlap_count": {
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+ "train_vs_eval_multi": 0,
+ "train_vs_eval_unique": 0
+ },
+ "split_rows": {
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+ "eval_unique_answer": 128,
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+ "eval_multi_support_mean": 6.3542,
+ "path": "var/data/exact_answer_mode_probe",
+ "train": 192
+ },
+ "train_size": 384,
+ "train_support": {
+ "max": 18,
+ "mean": 6.2969,
+ "min": 4
+ }
+}
diff --git a/provenance/source_data/graph_coloring_modebench_v2/train/dataset_dict.json b/provenance/source_data/graph_coloring_modebench_v2/train/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..40d5c04b431649b5c469de1fd03044ba97b6fded
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/train/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["train"]}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/train/train/dataset_info.json b/provenance/source_data/graph_coloring_modebench_v2/train/train/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/train/train/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
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+ },
+ "modebench_task": {
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+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/graph_coloring_modebench_v2/train/train/state.json b/provenance/source_data/graph_coloring_modebench_v2/train/train/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..3fe22da20625dff007474a2c75055c3ce71bdaf6
--- /dev/null
+++ b/provenance/source_data/graph_coloring_modebench_v2/train/train/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "f649f3b9ac954a46",
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+ "_format_kwargs": {},
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+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/eval/dataset_dict.json b/provenance/source_data/mathir_action_menu_v1/eval/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/eval/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/dataset_info.json b/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..c15fdbab4b5c6018a0638960fc92f250777ce2d9
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/dataset_info.json
@@ -0,0 +1,32 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "mathir_family": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/state.json b/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..1281fdfaf4f2046d8ad8adefb59fb1f53b53ecc7
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/eval/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "25fe8ad57160e2a8",
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+ "_format_kwargs": {},
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+}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/identity.json b/provenance/source_data/mathir_action_menu_v1/identity.json
new file mode 100644
index 0000000000000000000000000000000000000000..c7213187024467b8b02ddc5325ae0e0599823ee2
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/identity.json
@@ -0,0 +1,39 @@
+{
+ "canonical_identity": "normalized_states_from_same_successful_execution",
+ "eval_rows": 128,
+ "eval_rows_sha256": "df5783ddc9d2a70dd27a237450ce6467884d5ba371032ba8c2cb037bbf328b96",
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+ "ax_plus_b_eq_c",
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+ "ax_plus_b_eq_dx_plus_c",
+ "ax_plus_b_eq_c_minus_dx"
+ ],
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+ "mode_key_sha256": "74fa6c88c9374d1ea62ef16e1052215fb07d3e7444aae7ef4b91fd2dda3a5e0c"
+ },
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+ "mode_count": 5,
+ "mode_key_sha256": "63eea5fdb392676524bacf855420853a372d42f80eb9671596ec46df662e1327"
+ },
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+ "mode_key_sha256": "15dac6b473087cd34d061a619eff1e23daabea994e48ee354392a1b3290c4894"
+ },
+ "x_over_a_plus_b_eq_c": {
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+ "mode_key_sha256": "712b6aa21de2beaecd85fbb7cb4934a4cdb22b4fffde923c092e21ed1e819809"
+ }
+ },
+ "gold_mode_catalogue_supplied_to_policy": false,
+ "mode_counts": [
+ 5
+ ],
+ "schema": "mathir_action_menu_v1",
+ "seed": 5900,
+ "support": "finite_exhaustively_enumerated",
+ "train_rows": 384,
+ "train_rows_sha256": "bfc9836206b6e7ed10b42d53ea0c0615559a2a4446cd15f67476b5b0d45d3835",
+ "training_bank_initialization": "empty"
+}
diff --git a/provenance/source_data/mathir_action_menu_v1/train/dataset_dict.json b/provenance/source_data/mathir_action_menu_v1/train/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..40d5c04b431649b5c469de1fd03044ba97b6fded
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/train/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["train"]}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/train/train/dataset_info.json b/provenance/source_data/mathir_action_menu_v1/train/train/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..c15fdbab4b5c6018a0638960fc92f250777ce2d9
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/train/train/dataset_info.json
@@ -0,0 +1,32 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "mathir_family": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/mathir_action_menu_v1/train/train/state.json b/provenance/source_data/mathir_action_menu_v1/train/train/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..76069e469a1c17b32053c56d352cd9f7d8bb1d18
--- /dev/null
+++ b/provenance/source_data/mathir_action_menu_v1/train/train/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "013436c155c1c762",
+ "_format_columns": null,
+ "_format_kwargs": {},
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+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/admission_fairness_report.json b/provenance/source_data/modebench_harder_v2_matched_r5/admission_fairness_report.json
new file mode 100644
index 0000000000000000000000000000000000000000..7523dd09f3eb166ddafd0c882fb8d52824d5f125
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/admission_fairness_report.json
@@ -0,0 +1,933 @@
+{
+ "comparable_prompt_and_response_budget": {
+ "contract": "Each frozen model/domain cell uses one recorded protocol identically for its paired Level-1 and Level-2 evaluation; shared sample count, temperature, top-p, generation-token budget, chat-template mode, dtype, and full-row setting are invariant across all cells. Domain/model-specific prompt, syntax, and context-length profiles are frozen before this final run.",
+ "per_cell": {
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+ "syntax_profile": "countdown_legal_v3",
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+ "top_p": 1.0
+ }
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+ "max_tokens": 192,
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+ "sample_count": 8,
+ "seed": 5217201,
+ "syntax_profile": "none",
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+ "sample_count": 8,
+ "seed": 5217101,
+ "syntax_profile": "none",
+ "temperature": 1.0,
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+ }
+ },
+ "mathir": {
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+ "prompt_profile": "hybrid_solver_v4",
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+ "syntax_profile": "domain_legal_v1",
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+ "syntax_profile": "domain_legal_v1",
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+ }
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+ "syntax_profile": "domain_legal_v1",
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+ "syntax_profile": "domain_legal_v1",
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+ "top_p": 1.0
+ },
+ "qwen-0.5b": {
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+ "syntax_profile": "domain_legal_v1",
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+ "sample_count": 8,
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+ "top_p": 1.0
+ }
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+ "difficulty": {
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+ "pantry": "exact support/family matching; lexicographically maximize active dietary exclusions, then the frozen ingredient-composition difficulty score",
+ "python_factors": "four cases with at least one value above 96"
+ },
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+ "mathir": "admit",
+ "pantry": "admit",
+ "python_factors": "admit"
+ },
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+ },
+ "checks": {
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+ "unique_identities": true,
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+ "8": 24,
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+ "row_count": true,
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+ "checks": {
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+ "row_count": true,
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+ "verifier_and_canonicalization_contract": true
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+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/graph_coloring/eval",
+ "train": "var/data/graph_coloring_modebench_v2/train"
+ },
+ "mathir": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/mathir/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/mathir/eval",
+ "train": "var/data/mathir_action_menu_v1/train"
+ },
+ "pantry": {
+ "dev": "var/data/pantry_plan_modebench_v2/dev",
+ "eval": "var/data/pantry_plan_modebench_v2/eval",
+ "train": "var/data/pantry_plan_modebench_v2/train"
+ },
+ "python_factors": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/python_factors/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/python_factors/eval",
+ "train": "var/data/python_factor_modebench_v1/train"
+ }
+ },
+ "revision_provenance": {
+ "countdown": "regenerated development-first by compose_modebench_level2_r3.py from Level-1 support targets",
+ "graph_coloring": "byte-identical datasets and records from modebench_harder_v2_matched_r1",
+ "mathir": "byte-identical datasets and records from modebench_harder_v2_matched_r2",
+ "pantry": "byte-identical datasets and records from modebench_harder_v2_matched_r1",
+ "python_factors": "byte-identical datasets and records from modebench_harder_v2_matched_r1"
+ },
+ "schema": "modebench-level2-final-admission-fairness-v1",
+ "split_sizes": {
+ "dev": 128,
+ "eval": 128,
+ "train": 384
+ },
+ "status": "pass",
+ "viability_gate": {
+ "freeze": "before treatment training",
+ "harder_than_level1": "level2_pass_at_8 < level1_pass_at_8",
+ "maximum": 0.9,
+ "metric": "pass@8",
+ "minimum": 0.1
+ }
+}
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/dataset_dict.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/dataset_info.json
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+ "answer_mode_split": {
+ "dtype": "string",
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+ "homepage": "",
+ "license": ""
+}
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/state.json
new file mode 100644
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--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/dev/multi_answer/state.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
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@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/multi_answer/dataset_info.json
new file mode 100644
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--- /dev/null
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+ "license": ""
+}
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/eval/multi_answer/state.json
new file mode 100644
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--- /dev/null
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/dataset_dict.json
new file mode 100644
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--- /dev/null
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\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/train/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/train/dataset_info.json
new file mode 100644
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--- /dev/null
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+ "license": ""
+}
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/train/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/countdown/train/train/state.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/multi_answer/dataset_info.json
new file mode 100644
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+++ b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/multi_answer/dataset_info.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/dev/multi_answer/state.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/dataset_dict.json
new file mode 100644
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/multi_answer/dataset_info.json
new file mode 100644
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+++ b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/multi_answer/dataset_info.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/eval/multi_answer/state.json
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/dataset_dict.json
new file mode 100644
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@@ -0,0 +1 @@
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/train/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/train/dataset_info.json
new file mode 100644
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/train/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/graph_coloring/train/train/state.json
new file mode 100644
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@@ -0,0 +1,13 @@
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diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/identity.json b/provenance/source_data/modebench_harder_v2_matched_r5/identity.json
new file mode 100644
index 0000000000000000000000000000000000000000..0da053bd543463b777c61e4921c502568eb74e3e
--- /dev/null
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@@ -0,0 +1,590 @@
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+ "difficulty": {
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+ "graph_coloring": "four hidden vertices versus three",
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+ "pantry": "exact support/family matching; lexicographically maximize active dietary exclusions, then the frozen ingredient-composition difficulty score",
+ "python_factors": "four cases with at least one value above 96"
+ },
+ "domains": {
+ "countdown": {
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+ "verifier_and_canonicalization_contract": true
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+ "seed": 4217300
+ }
+ }
+ },
+ "fairness_contract": {
+ "identity_disjointness": "all Level-1 and all Level-2 splits",
+ "prompt_and_response_budget": "paired evaluator must use identical settings",
+ "support_histogram": "exact counts when source and target sizes match; exact integer expansion otherwise (Pantry dev 64 to 128 uses factor 2)",
+ "verifier_and_canonicalization": "unchanged"
+ },
+ "level1_reference": {
+ "countdown": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/countdown/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/countdown/eval",
+ "train": "var/data/exact_countdown_easy3_probe/train"
+ },
+ "graph_coloring": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/graph_coloring/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/graph_coloring/eval",
+ "train": "var/data/graph_coloring_modebench_v2/train"
+ },
+ "mathir": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/mathir/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/mathir/eval",
+ "train": "var/data/mathir_action_menu_v1/train"
+ },
+ "pantry": {
+ "dev": "var/data/pantry_plan_modebench_v2/dev",
+ "eval": "var/data/pantry_plan_modebench_v2/eval",
+ "train": "var/data/pantry_plan_modebench_v2/train"
+ },
+ "python_factors": {
+ "dev": "var/data/e117_evaluation_reserve_v1/development/python_factors/eval",
+ "eval": "var/data/e117_evaluation_reserve_v1/confirmation/python_factors/eval",
+ "train": "var/data/python_factor_modebench_v1/train"
+ }
+ },
+ "revision_provenance": {
+ "countdown": "regenerated development-first by compose_modebench_level2_r3.py from Level-1 support targets",
+ "graph_coloring": "byte-identical datasets and records from modebench_harder_v2_matched_r1",
+ "mathir": "byte-identical datasets and records from modebench_harder_v2_matched_r2",
+ "pantry": "byte-identical datasets and records from modebench_harder_v2_matched_r1",
+ "python_factors": "byte-identical datasets and records from modebench_harder_v2_matched_r1"
+ },
+ "schema": "modebench_harder_v2_support_matched_splits",
+ "split_sizes": {
+ "dev": 128,
+ "eval": 128,
+ "train": 384
+ },
+ "status": "pass",
+ "viability_gate": {
+ "freeze": "before treatment training",
+ "harder_than_level1": "level2_pass_at_8 < level1_pass_at_8",
+ "maximum": 0.9,
+ "metric": "pass@8",
+ "minimum": 0.1
+ }
+}
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..c15fdbab4b5c6018a0638960fc92f250777ce2d9
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/dataset_info.json
@@ -0,0 +1,32 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "mathir_family": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..09207b5e91bd7f457902033de3562b99e0f89432
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/dev/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "83471e842b7ef58d",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
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+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..c15fdbab4b5c6018a0638960fc92f250777ce2d9
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/dataset_info.json
@@ -0,0 +1,32 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "mathir_family": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..09bca51c5a6d593e0e142960849ec071abc6228b
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/eval/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "7b5a3dc2f9cdb4eb",
+ "_format_columns": null,
+ "_format_kwargs": {},
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+ "_output_all_columns": false,
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+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..40d5c04b431649b5c469de1fd03044ba97b6fded
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["train"]}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..c15fdbab4b5c6018a0638960fc92f250777ce2d9
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/dataset_info.json
@@ -0,0 +1,32 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "mathir_family": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1a71948cfd17b56ca96ed489ce7128595a22f46
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/mathir/train/train/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "d1721086d6960abb",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/dataset_dict.json b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/dataset_dict.json
@@ -0,0 +1 @@
+{"splits": ["multi_answer"]}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..424248b26b9785c58704f9c453d455f680b4adb7
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/dataset_info.json
@@ -0,0 +1,36 @@
+{
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+ "description": "",
+ "features": {
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+ "dtype": "string",
+ "_type": "Value"
+ },
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+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_family": {
+ "dtype": "string",
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+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "instance_fingerprint": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/state.json b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..26fa98f9fb8b6e72a68c9cf5d0f387c84c5affdb
--- /dev/null
+++ b/provenance/source_data/modebench_harder_v2_matched_r5/pantry/dev/multi_answer/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/provenance/source_data/modebench_level3_matched_v3/graph_coloring/dev/multi_answer/state.json b/provenance/source_data/modebench_level3_matched_v3/graph_coloring/dev/multi_answer/state.json
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diff --git a/provenance/source_data/modebench_level3_matched_v3/mathir/dev/dataset_dict.json b/provenance/source_data/modebench_level3_matched_v3/mathir/dev/dataset_dict.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec072a844d9c8b58cd512cdd5004f9b8c26ad64
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diff --git a/provenance/source_data/modebench_level3_matched_v3/mathir/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_level3_matched_v3/mathir/dev/multi_answer/dataset_info.json
new file mode 100644
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diff --git a/provenance/source_data/modebench_level3_matched_v3/mathir/dev/multi_answer/state.json b/provenance/source_data/modebench_level3_matched_v3/mathir/dev/multi_answer/state.json
new file mode 100644
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diff --git a/provenance/source_data/modebench_level3_matched_v3/pantry/dev/multi_answer/dataset_info.json b/provenance/source_data/modebench_level3_matched_v3/pantry/dev/multi_answer/dataset_info.json
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diff --git a/provenance/source_data/modebench_level3_matched_v3/python_factors/dev/multi_answer/state.json b/provenance/source_data/modebench_level3_matched_v3/python_factors/dev/multi_answer/state.json
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diff --git a/provenance/source_data/modebench_level3_matched_v3/python_factors/eval/multi_answer/state.json b/provenance/source_data/modebench_level3_matched_v3/python_factors/eval/multi_answer/state.json
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index 0000000000000000000000000000000000000000..c652b1e108c4ab80a608be8064715836a7a11d2d
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diff --git a/provenance/source_data/modebench_level3_matched_v3/recipes/mathir.json b/provenance/source_data/modebench_level3_matched_v3/recipes/mathir.json
new file mode 100644
index 0000000000000000000000000000000000000000..8da31797d7e6a6c2f8367889550f787c6d76968d
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..e1b3184c327ad284ca45a897bea7d44acb16e329
--- /dev/null
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+{"splits": ["train"]}
\ No newline at end of file
diff --git a/provenance/source_data/python_factor_modebench_v1/train/train/dataset_info.json b/provenance/source_data/python_factor_modebench_v1/train/train/dataset_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..5db33a5d461fde3365428ea1e1ebdee57e5e6d21
--- /dev/null
+++ b/provenance/source_data/python_factor_modebench_v1/train/train/dataset_info.json
@@ -0,0 +1,28 @@
+{
+ "citation": "",
+ "description": "",
+ "features": {
+ "problem": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "modebench_task": {
+ "dtype": "string",
+ "_type": "Value"
+ },
+ "answer_mode_count": {
+ "dtype": "int64",
+ "_type": "Value"
+ },
+ "answer_mode_split": {
+ "dtype": "string",
+ "_type": "Value"
+ }
+ },
+ "homepage": "",
+ "license": ""
+}
\ No newline at end of file
diff --git a/provenance/source_data/python_factor_modebench_v1/train/train/state.json b/provenance/source_data/python_factor_modebench_v1/train/train/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..08ea11c59c768d01f0a61a646139fccb01837157
--- /dev/null
+++ b/provenance/source_data/python_factor_modebench_v1/train/train/state.json
@@ -0,0 +1,13 @@
+{
+ "_data_files": [
+ {
+ "filename": "data-00000-of-00001.arrow"
+ }
+ ],
+ "_fingerprint": "e45685c59c2daa2f",
+ "_format_columns": null,
+ "_format_kwargs": {},
+ "_format_type": null,
+ "_output_all_columns": false,
+ "_split": null
+}
\ No newline at end of file
diff --git a/requirements-verifier.txt b/requirements-verifier.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0ea4603431bdcb719b3367b1768a6b4122ff5c25
--- /dev/null
+++ b/requirements-verifier.txt
@@ -0,0 +1,11 @@
+datasets==2.16.1
+latex2sympy2_extended==1.10.1
+math-verify==0.7.0
+pyarrow==11.0.0
+pylatexenc==2.10
+sympy==1.13.1
+torch==2.6.0
+numpy==1.26.4
+fsspec==2023.10.0
+huggingface-hub==0.36.0
+PyYAML==6.0.3
diff --git a/tools/export_modebench_hf_release.py b/tools/export_modebench_hf_release.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b59c78a1fff1167b5e6b240319d3984251fbc6f
--- /dev/null
+++ b/tools/export_modebench_hf_release.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python3
+"""Export frozen ModeBench datasets without altering source rows or live training."""
+from __future__ import annotations
+import argparse,ast,hashlib,importlib.metadata,json,os,re,shutil,sys,tempfile
+from collections import Counter,defaultdict
+from datetime import datetime,timezone
+from pathlib import Path
+
+ROOT=Path(__file__).resolve().parents[1]
+OUT=ROOT/'var/artifacts/modebench_hf_release_20260911/package'
+REPO_ID='od2961/ModeBench'
+L1={'graph_coloring':'graph_coloring_modebench_v2','countdown':'exact_countdown_easy3_probe','python_factors':'python_factor_modebench_v1','mathir':'mathir_action_menu_v1','pantry_plan':'pantry_plan_modebench_v2'}
+L2=ROOT/'var/data/modebench_harder_v2_matched_r5'
+L3=ROOT/'var/data/modebench_level3_matched_v3'
+L3_SHA='890d7697af7789e0ae53c803586ec7f722685b7eb2239643175a1933fa45650d'
+CONF=ROOT/'var/artifacts/modebench_level3_v3/confirmation/confirmation_report.json'
+SOURCE=ROOT/'var/artifacts/source_snapshots/e122_level3_6caff9b52d509a86499f/src/oat_drgrpo'
+DOMAINS=list(L1)
+
+def require(ok,message):
+ if not ok:raise ValueError(message)
+def sha(p):
+ h=hashlib.sha256()
+ with Path(p).open('rb') as f:
+ for b in iter(lambda:f.read(1<<20),b''):h.update(b)
+ return h.hexdigest()
+def read(p):return json.loads(Path(p).read_text())
+def now():return datetime.now(timezone.utc).isoformat()
+def row_digest(rows,style='json_array'):
+ if style=='json_array':text=json.dumps(rows,sort_keys=True,separators=(',',':'),allow_nan=False)
+ elif style=='json_lines_no_final_newline':text='\n'.join(json.dumps(r,sort_keys=True,separators=(',',':'),allow_nan=False) for r in rows)
+ else:raise ValueError(style)
+ return hashlib.sha256(text.encode()).hexdigest()
+def write_json(path,data):
+ path.parent.mkdir(parents=True,exist_ok=True)
+ with path.open('x') as f:json.dump(data,f,indent=2,sort_keys=True,allow_nan=False);f.write('\n')
+def copy_file(source,target):
+ require(source.is_file() and source.resolve()==source and not source.is_symlink(),f'unsafe source {source}')
+ target.parent.mkdir(parents=True,exist_ok=True);require(not target.exists(),f'output exists {target}');shutil.copyfile(source,target)
+ require(sha(source)==sha(target),f'copy changed {source}')
+ return {'source_repository_path':str(source.relative_to(ROOT)),'path':str(target),'sha256':sha(target),'bytes':target.stat().st_size}
+def source_files(path):return sorted(p for p in path.rglob('*') if p.is_file())
+def roundtrip_dataset(dataset,path):
+ from datasets import Dataset
+ rows=list(dataset);features=dataset.features.to_dict();columns=dataset.column_names
+ path.parent.mkdir(parents=True,exist_ok=True);require(not path.exists(),f'Parquet output exists {path}')
+ dataset.to_parquet(str(path),compression='zstd')
+ with tempfile.TemporaryDirectory(prefix='modebench_parquet_cache_') as cache:
+ recovered=Dataset.from_parquet(str(path),keep_in_memory=True,cache_dir=cache)
+ require(list(recovered)==rows,'Parquet changed rows or order')
+ require(recovered.features.to_dict()==features,'Parquet changed original feature types')
+ require(recovered.column_names==columns,'Parquet changed column order')
+ return {'rows':len(rows),'features':features,'source_column_order':columns,'rows_sha256':row_digest(rows),'rows_jsonl_sha256':row_digest(rows,'json_lines_no_final_newline'),'parquet_sha256':sha(path),'parquet_bytes':path.stat().st_size}
+def code_closure():
+ pending={'math_grader','templates','canonical_actions','pantry_support_action','modebench_guided','python_modebench_worker','maze_modebench_worker','__init__'};seen=set()
+ while pending:
+ name=pending.pop()
+ if name in seen:continue
+ p=SOURCE/(name+'.py');require(p.is_file(),f'missing verifier dependency {name}');seen.add(name);text=p.read_text();tree=ast.parse(text)
+ for node in ast.walk(tree):
+ if isinstance(node,ast.ImportFrom) and node.level==1 and node.module:
+ dep=node.module.split('.')[0]
+ if (SOURCE/(dep+'.py')).exists():pending.add(dep)
+ for dep in re.findall(r'oat_drgrpo\.([A-Za-z_][A-Za-z0-9_]*)',text):
+ if (SOURCE/(dep+'.py')).exists():pending.add(dep)
+ return sorted(seen)
+
+def export(output=OUT):
+ from datasets import load_from_disk,disable_progress_bar
+ disable_progress_bar()
+ sys.path[:0]=[str(ROOT/'ops'),str(ROOT/'ops/exp_scaling'),str(ROOT/'src')]
+ from materialize_modebench_harder_v2 import identity_set
+ require(not output.exists(),f'fresh staging directory required {output}')
+ require(sha(L3/'identity.json')==L3_SHA,'E122 Level3 dataset identity changed')
+ confirmation=read(CONF);require(confirmation['status']=='matched_fixed_reference' and confirmation['all_five_domains_complete'] and confirmation['confirmation_match_verified'],'Level3 separate confirmation missing')
+ require(confirmation['dataset']['sha256']==L3_SHA and confirmation['dataset']['dataset_root']==str(L3),'Level3 confirmation binds different dataset')
+ output.mkdir(parents=True)
+ identities={2:read(L2/'identity.json'),3:read(L3/'identity.json')}
+ configs=defaultdict(list);splits=[];seen_ids={d:set() for d in DOMAINS};all_source_pins={};source_before={};role_rows={};validation=[]
+ roots=[ROOT/'var/data'/r for r in L1.values()]+[L2,L3]
+ for root in roots:
+ for p in source_files(root):
+ require(p.suffix in ('.arrow','.json','.md','.txt'),f'unexpected source file {p}')
+ st=p.stat();source_before[str(p)]={'sha256':sha(p),'size':st.st_size,'mtime_ns':st.st_mtime_ns};all_source_pins[str(p.relative_to(ROOT))]=source_before[str(p)]['sha256']
+ for level in (1,2,3):
+ for domain in DOMAINS:
+ native='pantry' if domain=='pantry_plan' else domain
+ root=ROOT/'var/data'/L1[domain] if level==1 else (L2 if level==2 else L3)/native
+ for role in ('train','dev','eval'):
+ path=root/role
+ if not path.is_dir():continue
+ data=load_from_disk(str(path));require(set(data.keys())<=({'train'} if role=='train' else {'multi_answer','unique_answer'}),f'unexpected serialized split {path}')
+ for subset,dataset in data.items():
+ primary=(subset=='train' if role=='train' else subset=='multi_answer')
+ config=f'level{level}_{domain}'+('' if primary else '_'+subset)
+ rows=list(dataset);require(rows and all(isinstance(r.get('problem'),str) and isinstance(r.get('answer'),str) for r in rows),'invalid row surface')
+ specs=[json.loads(r['answer']) for r in rows];require(all(isinstance(s,dict) and isinstance(s.get('verifier'),str) for s in specs),'missing executable specification')
+ require(all(type(r.get('answer_mode_count')) is int and r['answer_mode_count']>0 for r in rows),'invalid support count')
+ expected=384 if role=='train' else 64 if level==1 and domain=='pantry_plan' and role=='dev' else 128
+ require(len(rows)==expected,f'original cardinality changed {path}/{subset}')
+ ids=identity_set(native,rows);require(len(ids)==len(rows),f'duplicate canonical problem identities {path}/{subset}')
+ if primary:
+ require(not(ids&seen_ids[domain]),f'canonical problem overlap across published primary splits {config}/{role}')
+ seen_ids[domain].update(ids);role_rows[level,domain,role]=rows
+ recorded=None
+ if level>1:
+ recorded=identities[level]['domains'][native][role]
+ hashes={style:row_digest(rows,style) for style in ('json_array','json_lines_no_final_newline')}
+ require(recorded['rows_sha256'] in hashes.values(),f'frozen row hash differs {config}/{role}')
+ require(recorded['rows']==len(rows) and all(recorded['checks'].values()),f'frozen structural validation differs {config}/{role}')
+ target=recorded.get('support_histogram',recorded.get('answer_mode_count_histogram'))
+ require({str(k):v for k,v in Counter(r['answer_mode_count'] for r in rows).items()}==target,f'support histogram differs {config}/{role}')
+ relative=f'data/{config}/{role}.parquet';result=roundtrip_dataset(dataset,output/relative)
+ record={'config_name':config,'level':level,'domain':domain,'source_domain_directory':native if level>1 else L1[domain],'split':role,'original_datasetdict_split':subset,'source_repository_path':str(path.relative_to(ROOT)),'reported_primary_split':primary,'data_file':relative,**result,'support_histogram':dict(sorted(Counter(str(r['answer_mode_count']) for r in rows).items())),'modebench_task_values':sorted({r['modebench_task'] for r in rows}),'verifier_values':sorted({s['verifier'] for s in specs}),'canonical_problem_identities_unique':True,'frozen_identity_record':recorded}
+ configs[config].append({'split':role,'path':relative});splits.append(record)
+ validation.append({'config':config,'split':role,'source_rows_equal_export_rows':True,'feature_schema_equal':True,'row_order_equal':True,'canonical_problem_identities_unique':True,'frozen_row_digest_verified':level>1})
+ # Level3 intentionally preserves Level2 support histograms, not equal difficulty.
+ for domain in DOMAINS:
+ for role in ('train','dev','eval'):
+ require(Counter(r['answer_mode_count'] for r in role_rows[2,domain,role])==Counter(r['answer_mode_count'] for r in role_rows[3,domain,role]),f'L2/L3 support mismatch {domain}/{role}')
+ copied=[]
+ for root in roots:
+ for p in source_files(root):
+ if p.suffix=='.json':copied.append(copy_file(p,output/'provenance/source_data'/p.relative_to(ROOT/'var/data')))
+ for rel in ['var/artifacts/modebench_level3_v3/confirmation/confirmation_report.json','paper/preregistration/e122_level3_factorial_20260909.md','paper/preregistration/e119_level2_qwen05b_factorial_20260901.md']:
+ p=ROOT/rel
+ require(p.is_file(),f'missing release protocol {p}');copied.append(copy_file(p,output/'provenance'/p.name))
+ # Preserve existing source licenses and attribution; no new dataset terms invented.
+ copied.append(copy_file(ROOT/'LICENSE',output/'licenses/source_repository_APACHE_2.0.txt'))
+ ingredients=ROOT/'var/data/pantry_plan_v1/ingredients.json'
+ if ingredients.is_file():copied.append(copy_file(ingredients,output/'provenance/pantry_ingredient_source.json'))
+ code=[]
+ for name in code_closure():
+ source=SOURCE/(name+'.py');live=ROOT/'src/oat_drgrpo'/(name+'.py');expected=confirmation['files_sha256'].get(str(live))
+ require(expected and sha(source)==expected,f'verifier source not bound by completed Level3 confirmation: {name}')
+ code.append(copy_file(source,output/'code/oat_drgrpo'/source.name))
+ for rel in ['ops/evaluate_modebench_level2_viability.py','ops/evaluate_modebench_level3.py','ops/evaluate_modebench_level3_independent.py','ops/modebench_independent_seeds.py','ops/check_paper_domain_prompts.py','ops/exp_scaling/materialize_modebench_harder_v2.py','ops/exp_scaling/materialize_modebench_level3.py','ops/exp_scaling/modebench_level3_v3_common.py']:
+ copied.append(copy_file(ROOT/rel,output/'provenance/source_code'/rel))
+ manifest={'schema':'modebench-hf-dataset-release-v1','prepared_at_utc':now(),'repo_id':REPO_ID,'source_data_mutated':False,'levels':[1,2,3],'primary_configs':15,'auxiliary_configs':len(configs)-15,'config_count':len(configs),'split_count':len(splits),'row_count':sum(s['rows'] for s in splits),'configs':[{'config_name':k,'data_files':v} for k,v in sorted(configs.items())],'splits':splits,'source_file_sha256':all_source_pins,'code_files':code,'provenance_files':copied,'runtime':{k:importlib.metadata.version(k) for k in ('datasets','pyarrow','sympy','math-verify','latex2sympy2_extended','pylatexenc','torch','numpy','fsspec','huggingface-hub','PyYAML')},'level3_identity':{'path':'provenance/source_data/modebench_level3_matched_v3/identity.json','sha256':L3_SHA,'original_decision':identities[3]['decision']},'level3_confirmation':{'path':'provenance/confirmation_report.json','sha256':sha(CONF),'status':confirmation['status'],'scope':'Adaptive second-round matching to fixed historical Level1 references; Graph/Python newly confirmed in v3, Countdown/MathIR/Pantry retain earlier bytes and evidence; not a statistical equivalence claim.'},'license_provenance':{'source_code':'Existing repository Apache-2.0 LICENSE copied verbatim; existing source copyright notices retained.','dataset_specific_license':'No separate dataset license found in the frozen dataset roots; no new dataset license assigned by this export.','pantry_ingredient_source':read(ingredients)['source'] if ingredients.exists() else None}}
+ for p,before in source_before.items():
+ s=Path(p).stat();require(s.st_size==before['size'] and s.st_mtime_ns==before['mtime_ns'] and sha(p)==before['sha256'],f'source changed during export {p}')
+ write_json(output/'MANIFEST.json',manifest)
+ write_json(output/'VALIDATION.json',{'schema':'modebench-hf-roundtrip-validation-v1','validated_at_utc':now(),'status':'passed','config_count':len(configs),'split_count':len(splits),'row_count':manifest['row_count'],'all_source_files_unchanged':True,'all_primary_problem_identities_disjoint_across_published_splits':True,'all_L2_L3_support_histograms_equal':True,'source_verifier_code_bound_to_completed_confirmation':True,'splits':validation,'limits':['Canonical problem identities and original structural checks are validated; this export does not regenerate every exhaustive answer catalogue or rerun model admission.','Original source split names and auxiliary Graph unique-answer rows are preserved; no test split is created.']})
+ write_docs(output,manifest)
+ return manifest
+
+def write_docs(output,m):
+ # These files are release documentation; original dataset features remain unmodified.
+ import yaml
+ header={'language':['en'],'task_categories':['text-generation'],'tags':['reasoning','reinforcement-learning','verifiable-rewards','modebench'],'pretty_name':'ModeBench','configs':m['configs']}
+ yamltext=yaml.safe_dump(header,sort_keys=False,allow_unicode=True)
+ intro='''# ModeBench
+
+ModeBench measures both correctness and the variety of correct answers produced by language models. Each problem has an executable verifier and a canonical definition of answer identity, so distinct wording alone does not count as a new solution.
+
+This release contains three frozen benchmark levels across **Graph Coloring, Countdown, Python Factors, MathIR, and PantryPlan**. It preserves every original feature, row, and row order. The `answer` field is a serialized executable specification, not a target answer to include in the model prompt.
+
+[Level 1](#level-1) · [Level 2](#level-2) · [Level 3](#level-3) · [Loading](#loading) · [Evaluation](#evaluation) · [Manifest](MANIFEST.json) · [Validation](VALIDATION.json)
+
+The accompanying [model and research-artifact archive](https://huggingface.co/od2961/maxent-grpo-models) contains experiment-specific model indexes and mappings to the paper results. Dataset publication does not imply completed Level-3 training results.
+
+
+## Level 1
+
+The original benchmark uses 384 training problems and 128 primary evaluation problems per domain. PantryPlan additionally has its original 64-row development split. Graph Coloring also includes a separate 128-row `unique_answer` evaluation subset; it is published as the auxiliary `level1_graph_coloring_unique_answer` configuration and is not the primary multi-answer paper evaluation.
+
+
+## Level 2
+
+Level 2 is the frozen `modebench_harder_v2_matched_r5` release. It changes problem structure while retaining the verifier and canonicalizer contracts. Each domain has 384 training, 128 development, and 128 evaluation rows. Support histograms follow the recorded matched reference construction; refer to the original identity manifest for the precise reference splits and scaling factors.
+
+
+## Level 3
+
+Level 3 is the E122-frozen `modebench_level3_matched_v3` release, with 384 training, 128 development, and 128 evaluation rows per domain. It preserves Level-2 support histograms. Admission used adaptive second-round matching to fixed historical Level-1 measurements: Graph and Python have fresh v3 confirmation, while Countdown, MathIR, and Pantry retain prior split bytes and confirmation evidence. This is not a claim of statistical equivalence, or of five fresh same-round confirmations.
+
+The original [identity manifest](provenance/source_data/modebench_level3_matched_v3/identity.json) still records `pending_fresh_candidate_confirmation`. That historical record is preserved verbatim. The later [confirmation report](provenance/confirmation_report.json) records `matched_fixed_reference` and binds that exact dataset identity. Neither record has been rewritten for publication.
+
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("od2961/ModeBench", "level2_countdown")
+train = ds["train"]
+development = ds["dev"]
+evaluation = ds["eval"]
+
+# Raw problem text goes to the policy. Keep the executable answer spec private
+# from the policy and use it only in the verifier.
+problem = evaluation[0]["problem"]
+reference_spec = evaluation[0]["answer"]
+```
+
+Use `level1_`, `level2_`, or `level3_` followed by `graph_coloring`, `countdown`, `python_factors`, `mathir`, or `pantry_plan`. Only existing splits are exposed; Level 1 generally has no `dev` split. No `test` split is invented. The Parquet files load through the standard datasets loader without remote dataset code. Pin a dataset revision for reproducible experiments.
+
+
+## Evaluation
+
+Use the original held-out `eval` rows and the frozen prompt, decoding, and verifier settings for a stated comparison. Use `dev` only where the original release provides it. Do not fit prompts, choose checkpoints, or tune methods on evaluation outcomes; training, development, and evaluation roles remain separate after public release.
+
+The same executable validation produces acceptance and canonical identity. Report correctness and distinct correct identities separately: with K sampled answers, pass@K is whether any answer verifies, and distinct@K counts unique verified canonical keys. The reported main benchmark uses K=8; the papers specify the seeds, draws, and training checkpoints. [Evaluation and prompt guide](EVALUATION.md) documents the supplied verifier interface and its versioned source.
+
+The support-count metadata and reference specifications are for construction, checking, and analysis. Do not supply gold support catalogues or hidden evaluation references to the policy. Original prompt strings are retained; [prompt formatting](PROMPTS.md) distinguishes raw problems from the model-specific chat and decoding interfaces.
+
+## Files and provenance
+
+`MANIFEST.json` records every source identity, feature schema, ordered-row digest, split mapping, and Parquet digest. `VALIDATION.json` records full row and feature round trips, unchanged source files, canonical problem-identity checks, and support matching. `code/oat_drgrpo/` preserves verifier and template source files byte for byte; `provenance/` preserves original identities and construction/admission records.
+
+## License provenance
+
+The original repository's Apache-2.0 source-code license is preserved in [licenses/source_repository_APACHE_2.0.txt](licenses/source_repository_APACHE_2.0.txt), and copied source copyright notices remain intact. The frozen dataset roots contain no separate dataset-specific license declaration; this export does not invent new dataset license terms. PantryPlan ingredient provenance records USDA FoodData Central Foundation Foods under CC0/public-domain source terms in [pantry_ingredient_source.json](provenance/pantry_ingredient_source.json).
+'''
+ (output/'README.md').write_text('---\n'+yamltext+'---\n\n'+intro)
+ lines=['# Configurations and original split mappings','','| Config | Split | Rows | Original serialized subset |','| --- | --- | ---: | --- |']
+ for s in m['splits']:lines.append(f"| `{s['config_name']}` | `{s['split']}` | {s['rows']} | `{s['original_datasetdict_split']}` |")
+ (output/'SPLITS.md').write_text('\n'.join(lines)+'\n')
+ (output/'PROMPTS.md').write_text('''# Prompt formatting
+
+Every Parquet row preserves the original `problem` string and the original serialized `answer` specification. Send the problem through the selected model/experiment's frozen template; do not concatenate the answer specification or support metadata into the prompt.
+
+For the original Qwen Level-1 training interface, Graph, Countdown, Python, and MathIR use `qwen_boxed`; Pantry uses `qwen_pantry_support_mask`. The copied `code/oat_drgrpo/templates.py` preserves the exact template text and Qwen/Falcon role markers. `provenance/source_code/ops/check_paper_domain_prompts.py` binds the original paper examples to those strings.
+
+Level-2/Level-3 admission uses the versioned interface in `provenance/source_code/ops/evaluate_modebench_level2_viability.py` and `evaluate_modebench_level3.py`: Graph uses the direct boxed interface, while the other domains use the registered hybrid solver interface and their legal-output constraints. That admission interface and its model-native tokenizer rendering are distinct from a training experiment's fixed template. Copying a dataset alone does not reproduce a decoder or inference-engine configuration.
+
+The original training and evaluation launchers remain the source of model-specific precision, length limits, guided-decoding profiles, draw seeds, and selected model checkpoints. The files here preserve the shared prompt/template implementation and the dataset rows; they do not silently standardize these experiment-specific choices.
+''')
+ (output/'EVALUATION.md').write_text('''# Executable verification and canonical identity
+
+For responses already decoded to the task's verifier surface, the supplied `code/oat_drgrpo/math_grader.py` entry point is `validated_modebench_outcome_key(response, row["answer"])`. It returns a canonical key only when that same response passes the executable task verifier; invalid responses return `None`.
+
+| Domain | Executable acceptance | Canonical identity |
+| --- | --- | --- |
+| Graph Coloring | Parse assignments; preserve fixed colors and satisfy every edge | Complete color vector |
+| Countdown | Restricted arithmetic AST, exact operand multiplicities and exact target value | Normalized expression AST with registered commutative normalization |
+| Python Factors | Restricted function AST executed in the bounded external worker on the specified cases | Executed return vector |
+| MathIR | Restricted action menu expanded and executed using exact equation transformations | Executed normalized state trajectory |
+| PantryPlan | Bounded ingredient quantities, serving steps, mass/nutrient constraints and dietary restrictions | Ingredient support |
+
+Install the versioned dependencies recorded in `MANIFEST.json`/`requirements-verifier.txt`; prepend the release's `code` directory to Python's import path. The code is supplied as inspectable ordinary source, not automatically executed by the datasets loader.
+
+```python
+import sys
+sys.path.insert(0, "path/to/ModeBench/code")
+from oat_drgrpo.math_grader import validated_modebench_outcome_key
+
+key = validated_modebench_outcome_key(response, row["answer"])
+verified = key is not None
+```
+
+Policy action adapters must run before this grader when the registered interface uses them. In particular, Pantry's six-bit support mask is not an allocation string: use `decode_pantry_support_mask(mask, json.loads(row["answer"]))` from `oat_drgrpo.pantry_support_action`, then grade the returned allocation. The frozen adapter searches only the chosen support's registered quantity grid and returns an invalid sentinel when infeasible; it does not consult a gold support catalogue. `canonical_actions.py` preserves the other registered action-to-verifier adapters.
+
+Python Factors retains its original separate, syntax-restricted worker. Use the supplied implementation for model outputs rather than evaluating arbitrary response strings in a notebook. The code also contains imported legacy task support needed by the original module; this release's dataset configurations contain only the five listed domains.
+
+All copied verifier/template dependencies are authenticated against the completed Level-3 confirmation's source hashes. The release validation checks canonical *problem* identity uniqueness and disjointness, row/feature preservation, and the original structural/support-histogram records. It does not regenerate every exhaustive answer catalogue, retrain models, or claim a new model-admission result.
+''')
+ (output/'requirements-verifier.txt').write_text('\n'.join(f'{name}=={version}' for name,version in m['runtime'].items())+'\n')
+ (output/'LICENSE_PROVENANCE.md').write_text('# Existing source terms\n\n'+json.dumps(m['license_provenance'],indent=2,sort_keys=True)+'\n')
+ copy_file(Path(__file__).resolve(),output/'tools/export_modebench_hf_release.py')
+
+if __name__=='__main__':
+ p=argparse.ArgumentParser(description=__doc__);p.add_argument('--output',type=Path,default=OUT);a=p.parse_args();m=export(a.output.resolve());print(json.dumps({'status':'prepared','output':str(a.output.resolve()),'configs':m['config_count'],'splits':m['split_count'],'rows':m['row_count'],'parquet_bytes':sum(s['parquet_bytes'] for s in m['splits'])},indent=2))
diff --git a/tools/seal_modebench_hf_release.py b/tools/seal_modebench_hf_release.py
new file mode 100644
index 0000000000000000000000000000000000000000..882c196b214dfb07e4186b416759eab6176cb6a7
--- /dev/null
+++ b/tools/seal_modebench_hf_release.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""Seal a prepared dataset-only upload plan; never uploads or deletes files."""
+from __future__ import annotations
+import hashlib,json
+from datetime import datetime,timezone
+from pathlib import Path
+ROOT=Path(__file__).resolve().parents[1]
+BASE=ROOT/'var/artifacts/modebench_hf_release_20260911'
+PACKAGE=BASE/'package'
+
+def digest(path):
+ data=path.read_bytes()
+ return {'bytes':len(data),'sha256':hashlib.sha256(data).hexdigest(),'git_blob_sha1':hashlib.sha1(b'blob '+str(len(data)).encode()+b'\0'+data).hexdigest()}
+def save(path,value):
+ with path.open('x') as f:json.dump(value,f,indent=2,sort_keys=True);f.write('\n')
+def seal():
+ m=json.loads((PACKAGE/'MANIFEST.json').read_text())
+ v=json.loads((PACKAGE/'VALIDATION.json').read_text())
+ l=json.loads((PACKAGE/'LOADER_VALIDATION.json').read_text())
+ assert (m['config_count'],m['split_count'],m['row_count'])==(16,42,9152)
+ assert v['status']==l['status']=='passed' and l['tests_passed']==18
+ for s in m['splits']:
+ d=digest(PACKAGE/s['data_file']);assert d['sha256']==s['parquet_sha256'] and d['bytes']==s['parquet_bytes']
+ for group in ('code_files','provenance_files'):
+ for item in m[group]:
+ p=Path(item['path']);assert p.is_relative_to(PACKAGE)
+ d=digest(p);assert d['sha256']==item['sha256'] and d['bytes']==item['bytes']
+ for relative,expected in m['source_file_sha256'].items():assert digest(ROOT/relative)['sha256']==expected
+ payload=[]
+ for p in sorted(PACKAGE.rglob('*')):
+ if not p.is_file():continue
+ assert not p.is_symlink() and p.resolve()==p and p.suffix in {'.json','.parquet','.py','.md','.txt'}
+ relative=p.relative_to(PACKAGE).as_posix();assert not any(x.startswith('.') for x in p.relative_to(PACKAGE).parts)
+ payload.append({'path_in_repo':relative,**digest(p)})
+ contents={'schema':'modebench-public-content-manifest-v1','self_excluded':'CONTENT_SHA256.json','files':payload,'files_count':len(payload),'total_bytes':sum(x['bytes'] for x in payload)}
+ save(PACKAGE/'CONTENT_SHA256.json',contents)
+ payload.append({'path_in_repo':'CONTENT_SHA256.json',**digest(PACKAGE/'CONTENT_SHA256.json')})
+ plan={'schema':'modebench-dataset-upload-plan-v1','status':'prepared','created_at_utc':datetime.now(timezone.utc).isoformat(),'repo_type':'dataset','repo_id':'od2961/ModeBench','public':True,'package_root':str(PACKAGE),'upload_only':True,'local_deletions_authorized_by_this_plan':False,'config_count':16,'primary_config_count':15,'auxiliary_config_count':1,'split_count':42,'row_count':9152,'primary_row_count':9024,'files_count':len(payload),'total_bytes':sum(x['bytes'] for x in payload),'files':[dict(item,source_path=str(PACKAGE/item['path_in_repo'])) for item in payload],'source_manifest_sha256':digest(PACKAGE/'MANIFEST.json')['sha256'],'content_manifest_sha256':digest(PACKAGE/'CONTENT_SHA256.json')['sha256'],'loader_validation_sha256':digest(PACKAGE/'LOADER_VALIDATION.json')['sha256'],'required_final_verification':{'immutable_commit_sha':True,'remote_path_size_and_sha256_or_git_blob_sha1_match_every_file':True,'publication_receipt_path':str(BASE/'publication_verified.json'),'receipt_fields':{'status':'verified','repo_type':'dataset','repo_id':'od2961/ModeBench','commit_sha':'actual immutable commit','upload_plan_sha256':'actual plan SHA256','all_files_verified':True}},'stable_anchors':['level-1','level-2','level-3','loading','evaluation']}
+ save(BASE/'upload_plan.json',plan)
+ save(BASE/'preparation.json',{'status':'prepared','upload_plan_sha256':digest(BASE/'upload_plan.json')['sha256'],'files_count':plan['files_count'],'total_bytes':plan['total_bytes'],'all_original_source_files_unchanged':True,'full_roundtrip_and_loader_tests_passed':True,'portable_verifier_five_domains_passed':True,'no_HF_mutations':True,'no_original_source_deletions':True})
+ print(json.dumps(json.loads((BASE/'preparation.json').read_text()),indent=2))
+if __name__=='__main__':seal()