Add anonymous scratch checkpoints, synthetic data, and source

review_artifacts/ANONYMIZATION.json

@@ -0,0 +1,237 @@
+{
+  "outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_17.out": {
+    "original_sha256": "9f8e49a279f225196de83e555b8e3859595b2c7be119b85d21c5f2c5398127f4",
+    "anonymous_sha256": "e27c917e0268b328ecdfa8d74817d09873b717cc1f2e77dffa8154692d58a078",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_28.out": {
+    "original_sha256": "70eda61e3d945affe1fea3d88337ffc4b1a9bead04af93f4ec0a7e60d1b52b45",
+    "anonymous_sha256": "e9f5f384f15e96933b391abce2460271b610faadfe988a09fe6fd07a93225968",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_4.out": {
+    "original_sha256": "db8f24ea4cd04aa4c8efe87a82d8403a659085de723d7d64c96d479e4b4175c8",
+    "anonymous_sha256": "f5ba0e71a62b8ba63cddd188afcea46c952bf58896989d9a12b45284080572ea",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/choice/COMPLETE_shard0.json": {
+    "original_sha256": "6dd8b9b841b21105e619b65ce45cd846b12113a7a0e525f3092e852e6a33453a",
+    "anonymous_sha256": "ca75a2f6fb48001d9cd6f6ab3d683871595f78001c5018a1e24cd12fd48a288b",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/full/COMPLETE_shard0.json": {
+    "original_sha256": "fd92733b96c883e2fd45366df72bb5193d3b201cc78fe6d8afa716f2a1a7766b",
+    "anonymous_sha256": "36678830be4c7d8661fde56e1bc3bbc7b8cb96e6fd7ca42116c1e5e90ea79748",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/full/COMPLETE_shard1.json": {
+    "original_sha256": "7cc89eba53aa68a98155402cf9e86bcaf7cb22a0ab4fffc14ea51ebf938cdb04",
+    "anonymous_sha256": "99ce3ad2d82e3c8bb1c897878d08737c881741047a1f646333915259723e0ab1",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/full/COMPLETE_shard2.json": {
+    "original_sha256": "61b8f0dea0bb75dfcab94c7362d1ca5abd57ebee50fe0e4ad230bb593ca72983",
+    "anonymous_sha256": "87ff2c836cb863b0cedbee29470abcf3b44a93a191b0e6cc8099edd845b1f047",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/full/COMPLETE_shard3.json": {
+    "original_sha256": "6baf254516af7657f44d9e16252e3042720ac33c078b2d54b54b7f16d2a04e55",
+    "anonymous_sha256": "8c77ff92cc3c6d38e7c7adf54e1859a49ca718aeb18f320697461fe61443818e",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w000/full/COMPLETE_shard4.json": {
+    "original_sha256": "d1c45c5f86dd5c68002fb6919a0c8979bdf73f861780ccaeb75250a0d8fb4af2",
+    "anonymous_sha256": "2cbf0b35f57450623a6ff4b0e2ac324281089fefaa8ee2084a8a92601da74624",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/choice/COMPLETE_shard0.json": {
+    "original_sha256": "92b6c1499be68a73e6c51aa33be13e58a7f0d7ffa1db4b80b5fa9ff54401a854",
+    "anonymous_sha256": "7a7771bef2df42c92f71e0f9d8d342b87143751859c512776047f4af7953c23d",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/full/COMPLETE_shard0.json": {
+    "original_sha256": "aa66e9831e22a31cf812ba35b2653da7c0ba7f5e71bc184ad4887eff377febf8",
+    "anonymous_sha256": "74fb6482f1809bbd77fd690a14239a4a9fb138d9d1de895f11c8c77b2a6e60e8",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/full/COMPLETE_shard1.json": {
+    "original_sha256": "181f4664c5662477715e65d2ad060e94f878543e5bd1ed201cf61a0e8f079aae",
+    "anonymous_sha256": "822b923ff3333de7d76e3a94b56c3c4d183f179b030fe9d48b2ce6c884ee5627",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/full/COMPLETE_shard2.json": {
+    "original_sha256": "ad6dfeb281a01abcd156a9770d5b7e58ce6d657de9aa7cde34041d31cd4cb2f7",
+    "anonymous_sha256": "9fb786e2c8d91cf4455a9b2f709485f4ed924c833ecc0c03b2127241b4bc1802",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/full/COMPLETE_shard3.json": {
+    "original_sha256": "449f7ab1636fa6c2a12fb315a95f4106aee4fbce26c5f446a8acfe9567e720ed",
+    "anonymous_sha256": "dee6d3dc80b4747d68235b2b012dde84097857b7c725c2e79b11ac464c3e5d4c",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w010/full/COMPLETE_shard4.json": {
+    "original_sha256": "0c73d8d83b46bf87f5655407afe1f335a6a261f1b4004799798cb7fb2d45469b",
+    "anonymous_sha256": "be70e279c2d5ad0cb83c01b77fe7789f1f6ef491642c0d13de24e45d17b4a808",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/choice/COMPLETE_shard0.json": {
+    "original_sha256": "ef8c2adc1b15326ebff40752b0171e27800a209f57d577a3ed24d073b92b1d1e",
+    "anonymous_sha256": "e94149dcaf7f6a95eedaaa6978c43820b95cf3858b0e7143d69480db12f97771",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/full/COMPLETE_shard0.json": {
+    "original_sha256": "e65480c44080962c410b2cbd1a4d6e85f8827bfb3e04957b607350afdf1a4551",
+    "anonymous_sha256": "ffe503b08926a4e59df3d8b93d16b2cfc9339fa2eb4601fab9c9de05edb29e32",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/full/COMPLETE_shard1.json": {
+    "original_sha256": "8b9f055db12fb53fca8237b2b64ecaad755a4729190bd82a2e81e783717e5c81",
+    "anonymous_sha256": "2b703e7327d799fbd5a4c307ec648c17446ea131810d343fc90e598516823978",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/full/COMPLETE_shard2.json": {
+    "original_sha256": "ffa06bc6e1e8b160516975cb1bcd1f8c51de3f99c563299c79fac0b498243935",
+    "anonymous_sha256": "cbb372c8f098e24cf8a117e53a15078f40dfdd680d3d6644de10b9fb68977cd8",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/full/COMPLETE_shard3.json": {
+    "original_sha256": "5210fb6710e98178debfbc57a66d4de91da1a9e7ff855d09620da8bdb33a3d41",
+    "anonymous_sha256": "ed51fd7562b9200e98c02f46940d38125ea65b99908734468308902f3f4132ce",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w030/full/COMPLETE_shard4.json": {
+    "original_sha256": "79fe2998af61eaeaf3b17508041c50efcc60f87000f9b8ea47caa199d217f975",
+    "anonymous_sha256": "f184de16489f626da7c4b12804a253ac17d5da43c5ed95cd7aa06ae2674ce6cc",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/choice/COMPLETE_shard0.json": {
+    "original_sha256": "e99ee26e5c7be75a5b3b3253cc0e935dc5e9e908ca2fc7ea94f1ede59e8a9906",
+    "anonymous_sha256": "37d3e0a6902a822da72e6e2a64c0392835204322f3589f7e7532a52c5041162d",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/full/COMPLETE_shard0.json": {
+    "original_sha256": "4ba54d6816b65c6f1f3dbbbec0cda41be927753883d320f7f420d2469b6c95e6",
+    "anonymous_sha256": "7d6e7de8ef19368f2fe5deb9dadbfb1842c37dbd4bfcb6c4e157ee3b012e3521",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/full/COMPLETE_shard1.json": {
+    "original_sha256": "1a54b95da936a4c1c21bd0eec06f61e43a306e708477a17c6eeca861b5777ea8",
+    "anonymous_sha256": "3dcdde35ec6faea6c06d72c83d3ed8662f360182a0c38b919e643b04477a6cb0",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/full/COMPLETE_shard2.json": {
+    "original_sha256": "50714136334953644b297163dab88e46e1e696be8a4592e704731efc8252bfff",
+    "anonymous_sha256": "83643590eb5defcf676da377ad4a15c74e46d7b5de5f91f00d0f6b0faf32e167",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/full/COMPLETE_shard3.json": {
+    "original_sha256": "e5e4fdd46e750e3db2e9414e6db0e79fac3800b36047455f03bf7895ed5c2c48",
+    "anonymous_sha256": "2fca4ee4cd039d776c2f770ea1da94fbad96963ae3fe7b2b112da0a6d4e93003",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w050/full/COMPLETE_shard4.json": {
+    "original_sha256": "94c940b4d6ba3f358777727879ad517edae4d71490f7dffedcb9b94b9d9a390d",
+    "anonymous_sha256": "80d92bb662996bdb94fc4fc19e6f20cf7c7d437639ed43236194ce882876a9dc",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/choice/COMPLETE_shard0.json": {
+    "original_sha256": "0af576f3283c6a4720ae7274779b8502f7cd32c49c3015baaf3fa9541102d9fc",
+    "anonymous_sha256": "ddb871d8a39063d010f05a99738fcff0436f0617cbd677a4b7815e0823e1f7e2",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/full/COMPLETE_shard0.json": {
+    "original_sha256": "61db7ecb3418d7ec5d64d548b708145d33434c32e178b75e8d3ea6632cbab967",
+    "anonymous_sha256": "31a42fe421c24574c64d091c2fd317e2c12ff135ba5aad3dd51a6700529ff541",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/full/COMPLETE_shard1.json": {
+    "original_sha256": "3808a68568e2bbd0349a08f007c39e85250e7076e36c8de96d87b39bb7337148",
+    "anonymous_sha256": "3e344b32d25254ac008b9f24028f997215c39a1090d857bcd6fffd05c8e2fb49",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/full/COMPLETE_shard2.json": {
+    "original_sha256": "c399415140385896732d5cb84fd09e1cb6edaab4cde4e76f486eab199f5f0465",
+    "anonymous_sha256": "8bb319449983449792da18d6118ef49e782ad45874d5eb2191ac55caa5302059",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/full/COMPLETE_shard3.json": {
+    "original_sha256": "2789aa223695a21c4827e26d747cddab5abe317cc5c2c48d404ff3f4ffc4bd4e",
+    "anonymous_sha256": "2cb1cd98b3c3b2c2472c0c35cd8c305adcb4df2881044ace72c0f98ac76ff622",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w070/full/COMPLETE_shard4.json": {
+    "original_sha256": "19b1918cd2daaf7bafd81ea4ced76e015be00a92002655b88e8c45e2b0804970",
+    "anonymous_sha256": "ffacafa82b93a5303a89e5c6355cfa1d6a4e271250e0541df00d9c2d7034a039",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/choice/COMPLETE_shard0.json": {
+    "original_sha256": "2318c6392bae7304076cea0bf79ae62774a89c5138a1123599ebf93c4ab05c80",
+    "anonymous_sha256": "07235976ea030c2574df48472fadb4ac12986c6c9f1c9cfebf605575436c8506",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/full/COMPLETE_shard0.json": {
+    "original_sha256": "cb2f74b54669d5e06a1392d0e663cd714aa315b1aba620a576f39b9cebb0ea72",
+    "anonymous_sha256": "75a57a0d4fc690b19afbd637e2ef9898e43d7374f102de54a7e761f0a06d3f50",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/full/COMPLETE_shard1.json": {
+    "original_sha256": "c529e0b231ff1a2dc517e14cf2ef81efc8f44b29936b203b06e2b3deec4eeba6",
+    "anonymous_sha256": "0b4c41c252a636f4a52e04f75a79f98220383e39b71e7f9e1920137a5f1f6eb3",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/full/COMPLETE_shard2.json": {
+    "original_sha256": "e64ce83e185dcb985c39ffd5d770d4616d74eaf8f7f4e59079b17e9e4a7d26e4",
+    "anonymous_sha256": "11b7d5fa43d13ceb6ca1773e56b83d9d54f23fabeab8fb96f71378071c1a0473",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/full/COMPLETE_shard3.json": {
+    "original_sha256": "5a44e36a283511923038d44c903fc4ffa98ed8c3be7a684d4c251c8c58e887e8",
+    "anonymous_sha256": "0f12380bdb79e21aabd5da5c94362f5668f2d2545ca8882a602baec8499cc1a6",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w090/full/COMPLETE_shard4.json": {
+    "original_sha256": "c81c3fdabd49584f1c1894dbcb245efb78792452086278cd45f5a94151107df4",
+    "anonymous_sha256": "776c486901c9d82f510405e81895f5bd4f563e49f39a82385df82a050afec7e6",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/choice/COMPLETE_shard0.json": {
+    "original_sha256": "ef48d27dec62f5fe82e3aec3db8bbf7b5b020abe11118598b7f96882cd09f7d5",
+    "anonymous_sha256": "6b378c4c4ff700a868837d8d1112174431dcbce5992aed2d5b3af98a8a6d56bb",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/full/COMPLETE_shard0.json": {
+    "original_sha256": "33e6667faf40979f20e76bfc5a40031c393fe7186fd8e99f95d7a1ae3c00ff43",
+    "anonymous_sha256": "fc66b65f36d64227c93d1c4798fcbd64bbfc2c1de6e97c83ec7b69fdbbd2b4d3",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/full/COMPLETE_shard1.json": {
+    "original_sha256": "a3e19c935bfc60e3d9116601011c62988fad982a2a0f21620434bf8a14bd879a",
+    "anonymous_sha256": "98aa244f1f9a1007e7ef077ff897b6acc2eacbca4ef49339a6c97c73e48f16dc",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/full/COMPLETE_shard2.json": {
+    "original_sha256": "57974399faca12df77c37708cc603c102116eb71efca2206b56562fb43bfe032",
+    "anonymous_sha256": "97604b6c22613a1cf533e6a2269181581881fca9e350f40d3417adcada28144b",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/full/COMPLETE_shard3.json": {
+    "original_sha256": "fc06001ea9b466d69748a0d3008d60ebe27fe67d90b4c1e559638600feb7a843",
+    "anonymous_sha256": "3af28e37018ee5253cb9141d2e7077fbec4afb545ae40ddbfcc4b8e995f6ffd4",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/llama_test_20260925/task_a/production/reward_w100/full/COMPLETE_shard4.json": {
+    "original_sha256": "d8ba545719c0f1e18d46898ee8fbe3cd709c9d377046731c8c4d0383452c519d",
+    "anonymous_sha256": "9eb1fea017769e48fe1888050131745e587be735c7d48791e3c74925418230ea",
+    "transformation": "Anonymize execution paths and log metadata; scientific values unchanged."
+  },
+  "outputs/test_main_integrated_20260925/restless/audit.json": {
+    "original_sha256": "2c8eec497a0ba91c8f478c791ddd968b958a171f243541d878c6d283b6aa4458",
+    "anonymous_sha256": "9c55c942ef799b19bc8c69eab7193cfea719839afe60195de5ce3ac534a4846a",
+    "transformation": "Recomputed input hashes by rerunning the unchanged collector on anonymized released inputs; all three numerical summary files match original SHA-256."
+  },
+  "outputs/test_main_integrated_20260925/spatial/audit.json": {
+    "original_sha256": "cd72684260fa0f5d1a3a563f9ce5ab1791ac4ee0af764acf52af8f2ead31a96c",
+    "anonymous_sha256": "cd72684260fa0f5d1a3a563f9ce5ab1791ac4ee0af764acf52af8f2ead31a96c",
+    "transformation": "Recomputed input hashes by rerunning the unchanged collector on anonymized released inputs; all three numerical summary files match original SHA-256."
+  }
+}

review_artifacts/FULL_ARTIFACTS_STATUS.md

@@ -1,36 +1,3 @@
-# Full anonymous scratch-model artifacts
-
-This directory extends the anonymous source package with the synthetic data
-and scratch-network checkpoints used in the paper. It is hosted in the
-`review_artifacts/` directory of the accompanying anonymous Hugging Face
-repository. It is not the compact OpenReview code ZIP.
-
-## Included
-
-- Task A: all seven weights of train/validation raw NPZ trajectories, plus
-  validation oracle-assay arrays and the generation protocol.
-- Task B: all seven weights of train/validation observable JSONL records and
-  generator-audit NPZ arrays, plus the generation protocol and audit report.
-- Prepared train/validation arrays and JSONL transcripts for the scratch and
-  LLaMA pipelines.
-- Every reported GRU and scratch Transformer best checkpoint: two tasks,
-  full-input and choice-only training, seven weights, three seeds per setting
-  (168 checkpoints), with matching configurations, metrics, and histories.
-- The code, small analysis snapshots, figures, and tests from the anonymous
-  review package. `MANIFEST.json` lists every file and its SHA-256 digest.
-
-## Not yet included
-
-- The 28 LLaMA SFT adapters are not duplicated in this directory. They are
-  available at the root of the same anonymous Hugging Face repository. Each
-  final adapter is about 414 MB. They require the compatible Llama-3.1-70B
-  base model and GPU software environment to load.
-- Reserved test-split trajectories. The paper reports development-validation
-  results, and the test splits have not been used for its analyses.
-- Intermediate training checkpoints and full participant-level probability
-  exports. Only selected best scratch checkpoints are included.
-
-The `*_audit.npz` files contain synthetic latent variables for generator
-checks. Predictors must receive only observable inputs. The existing README
-describes the evaluation routes; this extension does not claim a complete
-70B training rerun or an independently verified installation on a fresh host.
+# Release scope
+
+Includes independent test data, all main test probability exports and summaries, original train/validation data, and 168 scratch checkpoints. LLaMA adapters remain in the repository task_a/task_b folders. Base-model weights and intermediate training checkpoints are not included. See README.md.

review_artifacts/README.md

@@ -1,108 +1,88 @@
-# Anonymous full-artifact package
+# Behavioral model ablations: anonymous review materials
 
-This folder extends the anonymous ICLR 2027 code supplement with synthetic
-train/validation data and GRU/Transformer best checkpoints. It contains no
-author metadata, Git history, institutional paths, or reserved test data.
+This release accompanies the frozen independent-test manuscript. The code ZIP
+and anonymous repository share the same scientific code and main-figure inputs.
+No human data, credentials, Git history, or author-identifying manuscript source
+is included. The separate private repository candidate has the same payload.
 
-The manuscript PDF and appendices are submitted separately. Final LLaMA SFT
-adapters are in the root of the
-[same anonymous artifact repository](https://huggingface.co/randomfolk/behavioral-model-ablations-review).
-Do not use an author-identifying repository link during review.
+## What is where
 
+- Supplementary ZIP: source, configurations, requirements, tests, frozen test
+  summaries, figure scripts, and the exact manuscript figure PDFs.
+- Anonymous repository: https://huggingface.co/randomfolk/behavioral-model-ablations-review
+  `review_artifacts/` contains the code plus full synthetic test records,
+  prepared test prompts, participant-level probability exports, cognitive fits,
+  audit manifests, and the previously released train/validation data and 168
+  validation-selected GRU/Transformer checkpoints.
+- `task_a/` and `task_b/` in that repository contain all 28 final LLaMA adapters.
+  Base LLaMA weights are not redistributed. Use the specified base model under
+  its applicable access and licensing terms.
 
-# Behavioral model ablations: sequential-choice benchmark
+## Quick checks and main-figure rebuild (no inference)
 
-This code accompanies *Better Behavioral Prediction, More Faithful Model
-Ablations? Evidence from Sequential Choice*. It tests whether a predictor's
-response to a reward-history ablation matches the response of the known
-policy that generated its training data. The experiments are synthetic; they
-do not identify a mechanism in human participants.
+From the package root, with Python 3.10 or newer:
 
-## Paper-to-code map
-
-| Paper component | Data and analysis route | Bundled lightweight evidence |
-| --- | --- | --- |
-| Task A: four-armed restless bandit | `src/mechcal/generators/four_armed_restless.py`, `scripts/run_weight_curve_a.py` | `archive/outputs/weight_curve_a_20260915/reward_w050/val_000.npz` (Figure 1 example) |
-| Task B: spatially correlated bandit | `src/mechcal/generators/spatial_mixture.py`, `scripts/generate_spatial_pilot.py`, `scripts/prepare_spatial_neural.py` | `archive/outputs/spatial_pilot_v1/reward_w050/val_audit.npz` (Figure 1 example) |
-| Full-input and choice-only GRU/Transformer fits | `src/mechcal/models/`, `scripts/run_weight_neural_suite.py`, `scripts/run_spatial_neural.py` | Figure 2–4 validation summaries |
-| LLaMA supervised fine-tuning and evaluation | `src/mechcal/training/finetune_centaur_original.py`, `scripts/prepare_weight_llama.py`, `scripts/eval_weight_llama.py`, `scripts/eval_spatial_llama.py` | Integrated summaries; final adapters are hosted separately |
-| Oracle, fitted generator, and pooled RW | `scripts/run_reference_followup.py`, `scripts/ablate_spatial_references.py`, `scripts/run_rw_baseline.py` | Integrated summaries |
-| Matched donor-reward ablation | `scripts/evaluate_weight_neural.py`, `scripts/analyze_spatial_neural.py`, `scripts/eval_weight_llama_operations_fixedshape.py` | Figures 3–4 and operation-sweep summary |
-| Neural-generator and operation sensitivity | `scripts/run_neural_generator_sweep.py`, `scripts/run_trained_transformer_generator.py`, `scripts/run_random_generator_controls.py`, `scripts/plot_weight_operation_sweep_complete.py` | Supplementary figure PDFs in `paper_figures/` |
+```sh
+python -m pip install -r requirements.txt
+python -m pytest -q -p no:cacheprovider tests/test_four_armed_restless.py tests/test_spatial_mixture.py tests/test_reward_ablation.py
+python scripts/reproduce_figures.py
+```
 
-For donor replacement, observed past choices, target choices, trained model
-parameters, and donor assignments are held fixed. The response is the change
-in valid-choice probabilities or choice NLL after replacing past chosen
-rewards. Choice-only training is a *different* comparison: its network never
-sees rewards during fitting. The oracle uses true generating parameters;
-the fitted-generator reference estimates participant parameters from an
-earlier prefix. These three operations must not be conflated.
+The last command renders Figures 1 to 4 and the choice-only behavioral-baseline
+supplement (Figure S3 in the manuscript). It uses the independent-test summaries
+in `archive/outputs/test_main_integrated_20260925/`, not old validation curves.
+Figure 1A retains the manuscript's labeled illustrative validation trial;
+Figure 1C uses test means. `paper_figures/` contains the exact frozen PDFs for
+all main and supplementary figures. Other supplementary generator/operation
+analyses remain validation or training analyses, as labeled in the manuscript.
+Saved-summary plotting is not a rerun of model training.
 
-## Quick check and figure rebuild
+## Full test reaggregation (download review_artifacts first)
 
-From the package root, in an environment with Python 3.10+:
+Run from the root containing `src/`, `data/`, `outputs/`, and `scripts/`:
 
-```bash
-python -m pip install -r requirements.txt
-python -m pytest -q -p no:cacheprovider tests/test_four_armed_restless.py tests/test_spatial_mixture.py tests/test_reward_ablation.py
-python scripts/reproduce_figures.py
+```sh
+python scripts/collect_llama_test_20260925.py --self-test
+python scripts/collect_llama_test_20260925.py --task both --output reproduced_test_summaries
 ```
 
-`requirements.txt` is a convenience installer, not a lockfile for the original
-GPU jobs. It installs the local package and its analysis/test dependencies plus
-PyTorch for the quick checks and scratch networks. For GPU training, choose a
-PyTorch build compatible with the local CUDA driver before running the training
-scripts. The 70B LLaMA path additionally requires a separately configured
-Unsloth/TRL/Transformers environment and access to the base model; those
-hardware- and platform-specific dependencies are not claimed to be covered by
-this requirements file.
-
-The last command recreates Figures 1–4 and S1 from the frozen development-set
-summary records in `archive/`; it does not rerun fitting. The exact figure
-PDFs used in the manuscript are also in `paper_figures/`. The source-to-figure
-and inclusion hashes are listed in `MANIFEST.json`.
-
-## Full experimental route
-
-1. Generate independent train/validation/test synthetic trajectories with
-   `scripts/run_weight_curve_a.py` (Task A) and
-   `scripts/generate_spatial_pilot.py` (Task B). Start with `--help` for output
-   locations and use a new empty output directory. Generation writes a test
-   split, but the reported analyses use the development validation split.
-2. Fit weight-specific, full-input and choice-only scratch networks with
-   `scripts/run_weight_neural_suite.py` and `scripts/run_spatial_neural.py`.
-   The main training code and configs are under `src/mechcal/training/` and
-   `configs/`.
-3. Fit symbolic references and evaluate the same held-out choices and donor
-   assignments. Use the scripts in the map above; do not treat the oracle as
-   a fitted model or choose donor mappings separately by predictor.
-4. For LLaMA, prepare JSONL transcripts, fine-tune the pretrained
-   Llama-3.1-70B *base* model with LoRA, then evaluate with the fixed-shape
-   uncached causal procedure. This path requires access to the base model,
-   Unsloth, TRL, GPU resources, and a separately configured environment.
-   No pretrained base weights, adapters, or private cluster paths are bundled
-   in this folder. Final adapters are in the repository root.
-5. Integrate audited outputs with `scripts/integrate_llama_main.py` and
-   `scripts/integrate_spatial_llama.py`; use the figure scripts to regenerate
-   the complete plots. The accompanying paper specifies counts, windows,
-   optimization, and uncertainty conventions.
-
-The bundled `archive/` is intentionally compact: selected synthetic Figure 1
-inputs and aggregate development-set summaries, **not** the 10+ GB of
-intermediate checkpoints, all participant-level probability exports, or a
-turnkey rerun of the 70B training. Selected scratch checkpoints are under
-`outputs/`. The test split was not used for the reported claims. The
-summary-based figure rebuild is verified separately from full training.
-
-## Structure
-
-- `src/mechcal/`: generators, transcript builders, predictors, and ablations.
-- `scripts/`: generation, training, evaluation, integration, and plotting.
-- `configs/`: recorded scratch-network and generator configurations.
-- `tests/`: unit and small integration checks.
-- `archive/`: frozen figure inputs and audited aggregate summaries.
-- `paper_figures/`: PDF figures matching the manuscript revision.
-- `requirements.txt`: quick-check and scratch-network installer (not a GPU lockfile).
-- `MANIFEST.json`: relative-file SHA-256 inventory of this package.
-
-No human data are included. All bundled trajectories are synthetic.
+This uses the released participant exports and checks frozen checkpoint/script
+provenance, subject IDs, actual choices, donor mappings, conditional probability
+normalization, causal probes and metric identities before aggregation. It does
+not run LLaMA inference. Original absolute compute paths have been replaced
+with package-relative paths. Three resumed shards use anonymized extracts of
+the original successful causal-probe logs; their source hashes are retained in
+`ANONYMIZATION.json`. The release inventory hashes the sanitized files.
+
+## Experimental routes and GPU requirements
+
+| Paper component | Code |
+| --- | --- |
+| Restless and spatial generators | `src/mechcal/generators/`, `scripts/run_weight_curve_a.py`, `scripts/generate_spatial_pilot.py` |
+| Scratch neural training | `scripts/run_weight_neural_suite.py`, `scripts/run_spatial_neural.py` |
+| Non-LLM test evaluation and reference fitting | `scripts/test_nonllm_20260925.py`, `scripts/test_nonllm_audit_20260925.py` |
+| Frozen LLaMA test payloads | `data/llama_test_20260925/`, `scripts/prepare_llama_test_20260925.py` |
+| LLaMA test inference | `scripts/eval_weight_llama.py --test-main`, `scripts/eval_spatial_llama.py --test-main` |
+| Test collection | `scripts/collect_llama_test_20260925.py` |
+| Main-figure reconstruction | `scripts/reproduce_figures.py` |
+
+Use each script's `--help` for task, mode and weight arguments. The non-LLM
+pipeline phases are prepare, neural, spatial_fits, references, and aggregate;
+the released caches allow aggregation without rerunning fits. Never overwrite
+the archived test results when exploring new settings. Models retain their
+validation-selected checkpoints, with no test-based tuning or retraining.
+Individual fitted generators use only the first 150 trials or first six maps;
+scored targets are trials 151-200 or maps 7-8. Both tasks use seven weights and
+20 shared donor derangements, with actual choices retained.
+
+The requirements file supports the analysis and scratch-network paths, not a
+locked 70B GPU environment. LLaMA additionally needs compatible Unsloth, TRL,
+Transformers, bitsandbytes, CUDA, and substantial GPU memory. To rerun inference,
+place adapters in the relative paths recorded in `FROZEN_INPUTS.json`; their
+Hub locations are `task_a/{full,choice_only}/reward_wXXX` and
+`task_b/{full,choice_only}/reward_wXXX`.
+No full retraining or 70B inference rerun is claimed for this release check.
+
+`MANIFEST.json` gives SHA-256 hashes. `RELEASE_SCOPE.json` records which materials
+are test versus development. The manuscript itself is frozen and distributed
+separately. No new code/data license is granted by this review snapshot.

review_artifacts/RELEASE_SCOPE.json

@@ -0,0 +1,8 @@
+{
+  "main_figures": "independent test",
+  "choice_baselines": "independent test",
+  "spatial_prediction_table": "independent test",
+  "figure1_A": "validation illustration",
+  "other_supplements": "original training/validation analyses",
+  "manuscript_modified": false
+}

review_artifacts/archive/outputs/test_main_integrated_20260925/restless/A_alltrials_summary.json

@@ -0,0 +1,422 @@
+{
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+        "ci95": [
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+          0.3698726163956932
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+          0.4130487136542797,
+          0.4326356008648872
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review_artifacts/archive/outputs/test_main_integrated_20260925/restless/A_summary.json

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review_artifacts/archive/outputs/test_main_integrated_20260925/restless/B_summary.json

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review_artifacts/archive/outputs/test_main_integrated_20260925/restless/COMPLETE.json

@@ -0,0 +1,27 @@
+{
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+  "scoring": "trials151-200",
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+}

review_artifacts/archive/outputs/test_main_integrated_20260925/spatial/A_alltrials_summary.json

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review_artifacts/archive/outputs/test_main_integrated_20260925/spatial/A_summary.json

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review_artifacts/archive/outputs/test_main_integrated_20260925/spatial/B_summary.json

@@ -0,0 +1,2515 @@
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review_artifacts/outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_17.out

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review_artifacts/outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_28.out

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review_artifacts/outputs/llama_test_20260925/resume_audit_logs/test-A-full_13587155_4.out

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