# Code companion release specifications

Last audited: 2026-08-25

A companion specification identifies the smallest audited source surface that supports navigation from a book experiment to code. It is not a claim that the package reproduces every reported number, that dependencies are frozen, or that the underlying data and media may be redistributed.

## Summary

| Package | Proposed companion | Families | Audited files | Audited bytes | Status |
|---|---|---:|---:|---:|---|
| `categories-gt-archive` | Categories for AGI learning experiments | 4 | 35 | 476,306 | private-clean-environment-verified |
| `kan-do-dcdi-archive` | Judo Calculus supplementary experiments | 1 | 21 | 73,720 | private-clean-artifact-verified |
| `atlas-csql-archive` | ATLAS/CSQL worked examples | 1 | 3 | 19,008 | private-clean-environment-verified |
| `deep-url-archive` | Deep URL reproducibility packet | 1 | 5 | 35,994 | private-clean-environment-verified |
| `synthetic-creativity-archive` | Infinitesimal Creativity code companion | 19 | 288 | 3,723,688 | private-cpu-smoke-and-result-packet-verified |

## Categories for AGI learning experiments

- Registry package: `categories-gt-archive`
- Current archive status: `local-not-public`
- Proposed disposition: `split-release`
- Priority: `high`
- License: `decision required`
- Audited source surface: 35 files, 476,306 bytes

### Book-facing experiment families

- `catagi-db-gt` — Diagrammatic Backpropagation and Geometric Transformers (Chapter(s) 6, 7, 8; `bounded-demo-and-plot-provenance`)
- `catagi-information-ket` — GT information regimes, KET, and TopoCoend (Chapter(s) 9, 10, 11; `curation-required`)
- `catagi-gt-meanfield` — Mean-field diagnostics for Geometric Transformers (Chapter(s) 13; `archived-artifact-only`)
- `catagi-gt-scaling` — Depth sweeps for Geometric Transformers (Chapter(s) 14; `curation-required`)

### Bounded result packet

- Claim: Categories for AGI, Chapters 6--8 and 10--11: the executable DB energy illustration, two archived commutator-proxy figures, and the strict-causal, future-carrier, and archived block-4 language-model tables
- Claimed artifacts: 15
- Total bytes: 157,873
- Verification command: `python verify_chapters_6_8.py && python verify_book_tables.py`
  - `Category-Theory-for-AGI-UMass-CMPSCI-692CT/notebooks/week03_db_colimit_energy.ipynb`
  - `NeurIPS 2026 Arxiv Papers/Category Theory for AGI Revised/two_moons_commutator_energy.png`
  - `NeurIPS 2026 Arxiv Papers/Category Theory for AGI Revised/wiki103_prefix_suffix_cech_obs.png`
  - `outputs/causal_ablation/lm_causal_comparison_ptb_L2_D256_20260305_010640/summary.csv`
  - `outputs/causal_ablation/lm_causal_comparison_ptb_L2_D256_20260305_010640/summary.json`
  - `outputs/causal_ablation/lm_causal_comparison_wikitext2_L2_D256_20260305_011350/summary.csv`
  - `outputs/causal_ablation/lm_causal_comparison_wikitext2_L2_D256_20260305_011350/summary.json`
  - `outputs/causal_ablation/lm_causal_comparison_wikitext103_L2_D256_20260305_020457/summary.csv`
  - `outputs/causal_ablation/lm_causal_comparison_wikitext103_L2_D256_20260305_020457/summary.json`
  - `KET/ket_experiments/results/logs/lm_unified_ptb_L16_D64_20260425_102233/aggregate.csv`
  - `KET/ket_experiments/results/logs/lm_unified_ptb_L16_D64_20260425_102233/aggregate.json`
  - `KET/ket_experiments/results/logs/lm_unified_wikitext2_L2_D256_20260307_184700/aggregate.csv`
  - `KET/ket_experiments/results/logs/lm_unified_wikitext2_L2_D256_20260307_184700/aggregate.json`
  - `KET/ket_experiments/results/logs/lm_unified_wikitext103_L2_D256_20260307_192034/aggregate.csv`
  - `KET/ket_experiments/results/logs/lm_unified_wikitext103_L2_D256_20260307_192034/aggregate.json`
- Boundary: The packet executes the Chapter 6 numerical notebook cells and verifies the exact archived Chapter 8 figure bytes, dimensions, producing-script hashes, and manuscript captions. The figures have no surviving per-step raw logs and are therefore plot-only evidence, not independently reproduced runs. Separately, the packet verifies the rounded Chapter 10 and 11 table cells against twelve archived CSV/JSON artifacts and preserves the exact run configurations. It does not rerun training; the Chapter 10 values are single runs, the Chapter 11 PTB rows are five-seed aggregates, and the remaining Chapter 11 slices are unmatched single-run snapshots.

### Phase-one packet

- The audited implementation, runner, and dependency files linked from Chapters 6–14
- An executable Chapter 6 DB energy notebook and two exact archived Chapter 8 commutator-proxy figures with explicit plot-only provenance
- Twelve bounded CSV/JSON artifacts that support the revised Chapter 10 and 11 language-model tables
- Deterministic verifiers for the Chapter 6 notebook, Chapter 8 artifact provenance, and 87 Chapter 10–11 run-metadata and rounded table-cell assertions
- A dependency specification and one CPU smoke test per experiment family
- The public RADAR code and result records supporting the Chapter 12 manifold studies

### Excluded pending review

- The 651 GB parent research workspace
- Model checkpoints, downloaded corpora, caches, build products, and unrelated projects
- Result directories not tied to a named book claim

### Outstanding release work

- Choose the companion repository boundary and license
- Recover per-step raw logs for the Chapter 8 plots if they still exist; otherwise retain their explicit archived plot-only status
- Preserve Chapters 13–14 as archived-only evidence unless complete raw run artifacts are found

### Exact audited source manifest

| Package-relative path | Role | Bytes | SHA-256 |
|---|---|---:|---|
| `KET/ket_experiments/datasets_ptb_wt.py` | dependency | 5,493 | `3bf8b17487753e09ec4d4794d6de88bc7aca4c82600035e1913de323630fcd65` |
| `KET/ket_experiments/eval/__init__.py` | runner | 0 | `e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855` |
| `KET/ket_experiments/eval/eval_ppl.py` | runner | 3,195 | `a0c888514acbbaa4dbac6e0e69330160492ad31baf9b39fe2e69474a0ca407e5` |
| `KET/ket_experiments/eval/freeze_ablate.py` | runner | 3,214 | `696a94b104c21f46477dbaa2a30bc6a41ce4d964fdf03acd5f7123d4441038fb` |
| `KET/ket_experiments/eval/model_utility.py` | runner | 3,829 | `0ac786573070bba8414d872438ac6a074ea0de2c41d85b919bdee3a05c943762` |
| `KET/ket_experiments/eval/ngram_sweep.py` | runner | 1,892 | `5751c4ad2434aa0e4fdceb33789fcb47c208e3f67f09c5cb8b1fd0f9b83e844b` |
| `KET/ket_experiments/eval/perm_ablate.py` | runner | 2,611 | `839346a7bcf08397357f687b52b301b01d2b88ea4b3dd768a045c4e3a0aaa03b` |
| `KET/ket_experiments/ket_unified_harness.py` | runner | 32,402 | `90480649044a045f66ef2b20634098273b799f08bdebf385f1138b84e1491334` |
| `KET/ket_experiments/models/__init__.py` | implementation | 0 | `e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855` |
| `KET/ket_experiments/models/block_heads.py` | implementation | 1,812 | `a859c12a97b561b320dffa88d67f8293aab7089e94dc2ee304ab03ab6d3fdfce` |
| `KET/ket_experiments/models/ket_block_wrappers.py` | implementation | 1,173 | `923caad68158f2be811dcf0f47503ac568cda57181772ca625d64f51ada04ed4` |
| `KET/ket_experiments/models/ket_model.py` | implementation | 4,515 | `c91e4079ca4ac5196d321337810fe93bffad42d5475a85eb21f6bdd42e279f83` |
| `KET/ket_experiments/train/train_ket.py` | runner | 3,886 | `1c6903d5bcf96803a4e1fac799951eb501c2fa1f830133b1e037a575b0724cd3` |
| `KET/ket_experiments/train/train_ket_block.py` | runner | 24,188 | `b37859dc0bbf0b797ccc1fab31dd8abaf59ba9b3afaaff8ee882ba639103d166` |
| `KET/ket_experiments/train_torch_gt_lm.py` | dependency | 16,946 | `0c11367581b1b2e3ce284473d2aa6e91356c1f920e4db0faf9107da512c018a8` |
| `KET/ket_experiments/train_torch_kan_lm_ptbwt_incidence_mp_compat.py` | dependency | 14,121 | `eb783abbca8557564e5310fee3808cc5e5f7f7f9fe61a6a0995a2db48f039b20` |
| `KET/ket_experiments/train_torch_kan_lm_ptbwt_softpred_hint.py` | dependency | 20,033 | `2f63577ccea942e77d0d3ba99bc55cb617c8b317fc2fe2ad7d75bf22110e6037` |
| `KET/ket_experiments/train_torch_lm.py` | dependency | 9,949 | `b269656d74eed799ebaedf4aa5253f6eabe4ab1333bf0f67f6dc54961eb03079` |
| `KET/ket_experiments/train_torch_topocoend_lm_ptbwt_compat.py` | dependency | 19,478 | `6fb021e4939fc3446495bd1c334407f5366ad3423b3f8e66676cf1c3762f70f8` |
| `KET/ket_experiments/training/block_utils.py` | implementation | 1,910 | `00b8b4a753cf6ef27578c1eb25a460c110a058c6a9229b8db8cbf443503c727a` |
| `scripts/datasets_linked_wt2.py` | dependency | 2,602 | `da7cfda120f27a1f4035940cffd3833eaa2f7f035553a812d693cdbe56e54fa2` |
| `scripts/datasets_ptb_wt.py` | dependency | 5,493 | `3bf8b17487753e09ec4d4794d6de88bc7aca4c82600035e1913de323630fcd65` |
| `scripts/gt/__init__.py` | implementation | 176 | `3467f3f6f6683ffa8c27a8722954fa2da8c63eb3b81210a163ea653e83becbcc` |
| `scripts/gt/geometric_transformer.py` | implementation | 1,056 | `4b3ddf2e7020fe90e1fe5b91f7c2cc153ef29a73d7f0214f693fe30a25acb9ea` |
| `scripts/gt/simplicial_mp.py` | implementation | 2,669 | `241adca0aa7188a73dd221279d5ec73772bc09603bc38e3c80324592d63a8d91` |
| `scripts/resmlp_gt_commutator_demo.py` | implementation | 11,842 | `ee9436c086474a2be154e03153a1a1cb4caa84d4c9457380aa686bb129eeb895` |
| `scripts/seq2seq_gt_compare.py` | implementation | 34,967 | `4c906d0ad679f6e502781eb52240fde4593645747a3ae6609db44a48fa98f6c5` |
| `scripts/seq2seq_scaling_GTFull.py` | runner, dependency | 58,913 | `8733324e1498a3eabc6bb69e6c8ef65578f634e4803d389ef05c32168f847243` |
| `scripts/seq2seq_scaling_depth.py` | runner | 55,171 | `bc5ffad70223f8e42694adf29cc5496e0b50d3dd90288a3e43c173259aa1a82d` |
| `scripts/seq2seq_scaling_preln_transformer.py` | runner | 58,829 | `e0298cfe57afff60fa058a27865920252b7686feb4255589a6b699cd4dffa732` |
| `scripts/seq2seq_wiki103_prefix_suffix.py` | implementation | 17,237 | `dd2b0fffec6207de250d5cf81dc6b31ba1a5b4eb805c2824bc4452edba3104c3` |
| `scripts/seq2seq_wiki103_scaling-GT-Full.py` | runner | 17,330 | `485c0f999ad21c9aabf4747352bfa0ba10cbce0055224250d9f436f7d07abff3` |
| `scripts/seq2seq_wiki103_scaling-GT-Lite.py` | runner | 15,221 | `d66e385665103533b36ccdab77c54265b633f4ceeec5c810e3e76fbe59d7ea38` |
| `scripts/seq2seq_wiki103_scaling-GT-MoE.py` | runner | 15,292 | `11aed9b925d13821e8f7d100a1d77e274c9cfaa5562b84592149db9517435f8b` |
| `scripts/topo_probe.py` | dependency | 8,861 | `d0bac4f0f0805c65b05b883f75f0740c74bfbf0598c0feb158fb62f67bb6878f` |

## Judo Calculus supplementary experiments

- Registry package: `kan-do-dcdi-archive`
- Current archive status: `local-not-public`
- Proposed disposition: `merge-or-snapshot`
- Priority: `medium`
- License: `decision required`
- Audited source surface: 21 files, 73,720 bytes

### Book-facing experiment families

- `catagi-judo` — Judo Calculus causal-discovery experiments (Chapter(s) 20; `public-code-linked`)

### Bounded result packet

- Claim: Categories for AGI, Judo Calculus chapter: four directed-recovery rows and three Radon--Nikodym calibration rows
- Claimed artifacts: 7
- Total bytes: 825
- Verification command: `python verify_book_tables.py`
  - `runs/synth10_kando_metrics.csv`
  - `runs/j_sheaf_fast/results_metrics.csv`
  - `runs/s9_kando_metrics.csv`
  - `runs/s9_kando_multi_metrics.csv`
  - `calibration_synth.csv`
  - `runs/lincs_do_hspa8/lincs_rn_calibration_HSPA8.csv`
  - `runs/pisa2022_do_hisei_escs/pisa_rn_calibration_hisei_escs.csv`
- Boundary: The packet verifies the chapter's rounded table cells against seven archived CSV artifacts. It does not independently reproduce the Sachs, LINCS, or PISA runs; those require dataset provenance review, and the archived PISA calibration runner has a formatting defect that must be corrected before a clean rerun.

### Phase-one packet

- The audited Python runners, core operators, and transitive local helper modules linked from Chapter 20
- Seven bounded CSV artifacts that exactly support the chapter's four directed-recovery rows and three calibration rows
- A deterministic table-cell verifier that retains the weak Sachs results as negative evidence
- A map to the overlapping public Causal Discovery on Sheaves and LINCS BRIDGE/SKFM code

### Excluded pending review

- Virtual environments, Python caches, duplicate ZIP bundles, and generated figures
- Large run directories that are not needed by a named table or figure
- Third-party datasets without an explicit redistribution record

### Outstanding release work

- Decide whether the packet belongs in Causal Discovery on Sheaves or a dated snapshot repository
- Choose a license compatible with the public companion
- Document redistribution rights and provenance for the Sachs, LINCS, and PISA inputs
- Repair the PISA runner's diagnostic formatting defect and repeat the retained experiments in a clean environment

### Exact audited source manifest

| Package-relative path | Role | Bytes | SHA-256 |
|---|---|---:|---|
| `edge_scores.py` | dependency | 3,740 | `ab5defda574278f7d2d76fefea46c481bcdbed8a97ca5b6bacb30e7e9d9d84a0` |
| `edge_scores_fastpatch.py` | dependency | 8,088 | `973f73c69e09cf96fb23cd0cfdd26dd3eb18d8add003eccacb443ebbbc8c18a1` |
| `experiments/j_sheaf_regimes.py` | runner | 3,461 | `39956f1dea2c44814023f2fa767fd47e94a9932142fdeb86f90b39e3374edbc2` |
| `experiments/j_sheaf_regimes_fast.py` | runner | 3,966 | `329c5179be84f4e237a940e9b21116eef3f1caece78456c0f826b21a705e7249` |
| `experiments/kan_do_lincs.py` | runner | 2,957 | `a507ecf943728f82f7a35501b4db65abd3847bb8f312e1a1f80668c4bca160ed` |
| `experiments/kan_do_pisa2022.py` | runner | 2,680 | `e781b3cbaf51cf8ce462ea93833f2778d37ed870975c8a258e612d4cf573c3af` |
| `experiments/kan_do_s9.py` | runner | 3,338 | `ed6c442c7f17bdb4cffc8796bc588ebbbbef090d45e27c92aacbd0bcf152606f` |
| `experiments/kan_do_s9_multivar.py` | runner | 5,413 | `60d24453215ef45e81073dab3dcd03290bc0e0adfa9beb87fcedac8e2a08db6d` |
| `experiments/kan_do_synth_linear.py` | runner | 3,206 | `f773fbf26c8e33aa4d0c5255cf9e57c5ff68ba6c444a9dc6b30361b02e963286` |
| `experiments/sachs_miniexp.py` | runner | 4,362 | `f434f4fcfee89ca49b416750e9df877c8c7c6e210e7c0f877ddc9627918ffc0c` |
| `experiments/sachs_miniexp_enhanced.py` | runner | 7,348 | `e4ea4764b7e4b7b5ad3c1fa9429dc455cbb59f7ffeed124da27fd6c2c0199ac7` |
| `experiments/sheaf_glue.py` | runner | 1,942 | `4b33c1eb0c84bb023fc1bb202ece65c72feaac27f54ce4ba06fee4cde7058583` |
| `experiments/synthetic_linear_gaussian.py` | runner | 4,816 | `c9f18577e84b903843c53c99b7541c75396ca0958500e807c64172c89bd47509` |
| `kan_do_dcdi_core.py` | runner | 2,208 | `05302ca845488fb14094110fc85fadd3bd3b831db43d72911433bfc9b9a55a20` |
| `kan_ops.py` | runner | 355 | `6c9a7151e48a87ecb457bb2c39252f02d985baef94b261d3804311aa4c2f6aa7` |
| `lincs_m_hspa8.py` | runner | 4,792 | `7adc2de48a3cb02bbafa0b18408d99fe13405013973599bc0f34ec8cd5c9c230` |
| `metrics.py` | dependency | 998 | `e3e5f0ed96a79bca3916656e161720eed0379180eb0e48972873d28eccab73c7` |
| `mmd_utils.py` | dependency | 2,023 | `9ffbe2e4b39f1b62b15b2fcb8625cb65a60a82f84a74af7482eb2784fd29a4ee` |
| `pisa_run_calib_hisei_escs.py` | runner | 4,447 | `9b60cc64d8a043ea6283d06248f6fecc6fbd53fe0d446e4654df29d75c35c937` |
| `rn_flow.py` | dependency | 2,400 | `3f0c4d5b043ffe4d4eb84b3452dc4fd9939046972334165d8a55c0530a2d5392` |
| `sheaf_metrics.py` | dependency | 1,180 | `a39ce517e253997d6c064321c3e041bb5daf468d3d91593a0b5061cc8044beb4` |

## ATLAS/CSQL worked examples

- Registry package: `atlas-csql-archive`
- Current archive status: `local-not-public`
- Proposed disposition: `curate-separately`
- Priority: `medium`
- License: `decision required`
- Audited source surface: 3 files, 19,008 bytes

### Book-facing experiment families

- `catagi-csql` — CSQL and ATLAS database construction (Chapter(s) 21; `curation-required`)

### Phase-one packet

- The two audited SQL query files and one starter notebook linked from Chapter 21
- A generated, rights-clear synthetic categorical database instance
- Schema, query, expected-result documentation, and a deterministic DuckDB smoke test

### Excluded pending review

- Article text or other source material not licensed for redistribution
- Derived database instances whose provenance has not been documented
- Notebook outputs that contain embedded source text or credentials

### Outstanding release work

- Choose a license for the public companion
- Decide whether the corpus-specific notebook belongs in the public packet and pin its separate environment

### Exact audited source manifest

| Package-relative path | Role | Bytes | SHA-256 |
|---|---|---:|---|
| `atlas_WaPoHumanOrigins/atlas_queries.sql` | implementation | 3,902 | `47edc84241fb75c61113884b130ac5f8a2bfb593f41b67e534410693e82ecf41` |
| `atlas_WaPoHumanOrigins/atlas_queries_quant.sql` | implementation | 1,779 | `63b447662f2b9209f64759d05401c4bc12ffc53458d8cb86af1b4457282d9d4e` |
| `atlas_WaPoHumanOrigins/ncsql_starter_colab.ipynb` | implementation | 13,327 | `3b789344c33b870735a9d793b7674dcfa6acb5b8863a4cd7345bb1b9ed8b4bef` |

## Deep URL reproducibility packet

- Registry package: `deep-url-archive`
- Current archive status: `local-not-public`
- Proposed disposition: `curate-separately`
- Priority: `medium`
- License: `decision required`
- Audited source surface: 5 files, 35,994 bytes

### Book-facing experiment families

- `catagi-deep-url` — Deep Universal Reinforcement Learning (Chapter(s) 28; `curation-required`)

### Bounded result packet

- Claim: Categories for AGI, Deep URL chapter: the thirty-run synthetic line-world ablation table
- Canonical run family: `paper_short/GT_RL_Coalgebra/outputs/ablation/20260223_105353/runs`
- Raw runs: 30
- Report artifacts: 3
- Total bytes: 19,503,739
- Models: `gt`, `gtdb`, `mlp`
- Seeds: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
- Training command: `python -m GT_RL_Coalgebra.run_synthetic_mdp_ablation --episodes 300 --max-steps 36 --seeds 10 --seed-start 0 --models gtdb,gt,mlp --n-states 32 --random-start --slip-prob 0.10 --reward-noise-std 0.05 --db-coef 0.1 --device cpu`
- Report command: `python -m GT_RL_Coalgebra.report_synthetic_mdp_ablation --inputs-glob 'results/canonical_30_run/raw/*.csv' --out-dir reproduced_report --success-window 20 --success-target 0.8 --bootstrap-samples 2000`
- Boundary: Result packets contain only artifacts needed to audit a named book claim. Inclusion establishes provenance and re-aggregation, not independent replication.

### Phase-one packet

- The five audited Python bridge, runner, and reporting files linked from Chapter 28
- The canonical thirty raw runs and three report artifacts referenced by the chapter
- A deterministic CPU smoke configuration, result manifest, and byte-for-byte report reproduction check

### Excluded pending review

- Python caches, duplicate manuscript sources, and generated plots reproducible from CSV
- Opaque binary artifacts without a documented reader
- Exploratory outputs not used by the chapter

### Outstanding release work

- Choose a license for the public companion

### Exact audited source manifest

| Package-relative path | Role | Bytes | SHA-256 |
|---|---|---:|---|
| `report_synthetic_mdp_ablation.py` | runner | 9,657 | `028843dbb8208a008377e69da5f182ce0b9a583bbb208c66bfb7ff1135ce4664` |
| `report_synthetic_mdp_metrics.py` | runner | 3,516 | `9ec7fcc47bd751347b65e9b5c95bcb1c8385f2e25de05e6e8b7b709d24e7b104` |
| `run_synthetic_mdp_ablation.py` | runner | 4,478 | `c29f151bff9f81cb3552be30cfa3e90b13f22d5ceefefba0a4e86b2474b6544f` |
| `run_synthetic_mdp_loop.py` | runner | 15,582 | `f43b96ff95bd9a8c0450522cdd8fda7a5c99e269f4d2fcb58cbc3788bb42cfbc` |
| `synthetic_mdp_bridge.py` | runner | 2,761 | `c6166e9b9bb94eca47fa4c717c902f85f65ea77447ea5f8a7d645fe4ce0fddbb` |

## Infinitesimal Creativity code companion

- Registry package: `synthetic-creativity-archive`
- Current archive status: `local-curated`
- Proposed disposition: `dedicated-repository`
- Priority: `high`
- License: `decision required`
- Audited source surface: 288 files, 3,723,688 bytes

### Book-facing experiment families

- `ic-dial-core` — DIAL core ablation ladder (Chapter(s) 10; `local-curated`)
- `ic-dial-allora` — DIAL-ALLORA prototype ladder (Chapter(s) 10; `local-curated`)
- `ic-clic` — CLIC / DIAL-SKFM (Chapter(s) 11; `local-curated`)
- `ic-optic` — OPTIC / DIAL-SkillOpt (Chapter(s) 12; `local-curated`)
- `ic-relic` — RELIC and DIAL-Schema-GIRL (Chapter(s) 13; `local-curated`)
- `ic-agentic` — AGENTIC workflow composition (Chapter(s) 14; `local-curated`)
- `ic-lea` — AGENTIC-Lea theorem-construction ladder (Chapter(s) 15; `local-curated`)
- `ic-ipc` — Infinitesimal pattern-construction ladder (Chapter(s) 15; `local-curated`)
- `ic-ai-feynman` — Categorical AI-Feynman ladder (Chapter(s) 15; `local-curated`)
- `ic-dial-url` — DIAL-URL coalgebraic theory construction (Chapter(s) 15; `local-curated`)
- `ic-sgte` — Simulator-grounded theory extension (Chapter(s) 16; `local-curated`)
- `ic-ctte` — Corpus-to-testable-theory construction (Chapter(s) 17; `local-curated`)
- `ic-glp1` — GLP-1 theory and grant construction (Chapter(s) 17; `local-curated`)
- `ic-artistic-ti-ni` — ARTISTIC textual-inversion and natural-image ladder (Chapter(s) 18; `local-curated`)
- `ic-artistic-bongard` — ARTISTIC Bongard problems (Chapter(s) 18; `local-curated`)
- `ic-artistic-sports` — ARTISTIC sports, pose, and scene enforcement (Chapter(s) 18; `mixed-provenance`)
- `ic-dial-can` — DIAL-CAN structural-obstruction calibration (Chapter(s) 19; `local-curated`)
- `ic-dilate-paintbrush` — DILATE paintbrush geometry (Chapter(s) 19; `local-curated`)
- `ic-dilate-video` — DILATE video, codec, and cross-video transport (Chapter(s) 19; `local-curated`)

### Bounded result packet

- Claim: Infinitesimal Creativity, Chapters 10--19: the registered protocols, target cards, concise result summaries, completion records, and bounded numerical metrics for every documented DIAL-X experiment family
- Experiment families: 19
- Catalogued run directories: 229
- Selected text records: 629
- Total bytes: 1,726,523
- Per-file size ceiling: 250,000 bytes
- Excluded path/credential-pattern records: 29
- Verification command: `python verify_result_packet.py`
  - `ic-dial-core`: 44 records
  - `ic-dial-allora`: 92 records
  - `ic-clic`: 12 records
  - `ic-optic`: 8 records
  - `ic-relic`: 117 records
  - `ic-agentic`: 17 records
  - `ic-lea`: 10 records
  - `ic-ipc`: 47 records
  - `ic-ai-feynman`: 16 records
  - `ic-dial-url`: 15 records
  - `ic-sgte`: 16 records
  - `ic-ctte`: 24 records
  - `ic-glp1`: 42 records
  - `ic-artistic-ti-ni`: 3 records
  - `ic-artistic-bongard`: 20 records
  - `ic-artistic-sports`: 45 records
  - `ic-dial-can`: 16 records
  - `ic-dilate-paintbrush`: 37 records
  - `ic-dilate-video`: 48 records
- Boundary: This is a text-only provenance packet, not a media release or an independent rerun. Selection is restricted to top-level registration, preregistration, target-card, result-summary, completion, freeze, status, and bounded metric files no larger than 250,000 bytes. It excludes images, video, prompts, raw model outputs, datasets, caches, nested environments, and large generated ledgers. Every documented experiment family must retain at least one selected record.

### Phase-one packet

- The 288 audited Python implementation and runner files linked from Chapters 10–19
- Registration, configuration, and textual result records selected by an explicit allowlist
- CPU-scale smoke tests for the DIAL core and one representative DIAL-X family
- Pinned, portable CLIP model resolution with validated offline-path overrides

### Excluded pending review

- The full 22 GB experiment workspace
- Private directories, prompts containing confidential material, model weights, caches, and downloaded corpora
- Raw or generated imagery and video until rights, privacy, and provenance are approved
- Developmental runs that lack a registration or declared evidence boundary

### Outstanding release work

- Separate reusable library code from one-off experiment drivers
- Choose a license, dependency policy, model-access policy, and media provenance format
- Complete manual security, privacy, and publication review beyond the passing narrow path, credential-pattern, media-type, and file-size checks
- Publish or vendor the six-file alfworld_bridge dependency

### Exact audited source manifest

| Package-relative path | Role | Bytes | SHA-256 |
|---|---|---:|---|
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/build_artistic_ti2d_adaptive.py` | runner | 3,610 | `21d7a7410fbb7fb525619c5fa38279aa825fc3adaf35a2b923e42b17f920e87d` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/build_artistic_ti3_adaptive.py` | runner | 3,417 | `c37fccdcf439ae263f783063405fde3cbb612df188190b8acf525e376ed52023` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/build_typed_visual_adaptive.py` | runner | 2,665 | `01280cdd04a346529e8c63a9cbad0edffb7f6fcb571bb2b5fd802aad37676298` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/calibrate_original_mask_observer.py` | runner | 3,734 | `7d3c42ff5bd1a6f0b4e6240175eede3848ace566433d473cc89a69794a437513` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/diagnose_artistic_ni2b_relations.py` | runner | 3,273 | `7a5e770e1b5cd243613f3d0097d3179accb3554b4bd282506619afb15e9da0c4` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/diagnose_artistic_soccer2.py` | runner | 7,472 | `d51f99eb558bf5a713b9a0cecaf563fb5684c39fe4bd2a898d0a738b99d2cd68` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/download_frozen_model.py` | runner | 1,171 | `71c8b9a376fb3930d8458e299d6d97b42b9c4e0d9ff0139dbc5dcafb9d2070d6` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4_declarations.py` | runner | 5,720 | `fdc0de555aae20b19f05ac1b2fd41af0abf7f2734c82e4e2e4a2a4ddc46911ed` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4b_proximity_declarations.py` | runner | 3,323 | `563da5cdfbae8606f525a5a899da54357d7f38c39f63cb3df481c2ad92c4a575` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4c_proximity_declarations.py` | runner | 338 | `fa8aa210fa5395a79ead047d480fe3daeb5c1a4f4b8a9d1e52840f4e25544c6f` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4c_proximity_declarations_v2.py` | runner | 5,190 | `7649788d568128aaa2b741994e6321019344cff48a3f78a2c2fbd409f8ba99e5` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4d_semantic_declarations.py` | runner | 4,403 | `b89df1c1f3864f661eae512954c6d029990d1d2db91bb16b926e420dd8036046` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/estimate_ti4e_multicue_declarations.py` | runner | 5,599 | `8282d844760dcab0c6b406c7c41d73696eee54f5f77cdcc82ec1254da4a5caed` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ni0.py` | runner | 10,464 | `4ca3caae60fe5c02c497afaecc8752dfedfb4e697544ddbfe92d8be4c358d04e` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ni2a.py` | runner | 13,644 | `566fb063e6fa55f05ec11e5e5a93dfa08b427de045720aeb94fdada162ae3dd7` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ni2b.py` | runner | 9,948 | `18c3062f25536ac8f4c48a3bc2880726d1cda0c2fc7288d358372a52ebb8c554` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ni2c.py` | runner | 11,545 | `b941d30751b81201d12ff370199048ec240b1f123816bdb9b255b08238b2b944` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti2d.py` | runner | 4,781 | `487a21ad3a79c75cdbe106712255e367d76a90ef6b0cc07874e10b5dcaa1215a` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti3.py` | runner | 5,678 | `a2ad19e48e0cbd14e858bec96f5b7a6d6fdb3d3613fc0460b109a78a04b2f27d` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti3b.py` | runner | 5,638 | `95bcbd647b87d3ee4b2e72a90d4b4866dab51690be97d130a5f2220a82268f57` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti3c.py` | runner | 6,147 | `38d812b4bb4f6c9b41d04cccef96b82b477586ae7f0a81dee232eaaffa1f14ae` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti3d.py` | runner | 6,395 | `bb9752b105a92506eda3a301e768c253547a555778336c0379ef32134a32dc90` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4a.py` | runner | 7,950 | `39e794ed39c7e0c00045e9c7ef10bc5f996a32a3f6aef7aba4b3f678db3695f2` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4b.py` | runner | 524 | `81a52711c0ee571b048a273953115254e8695d558f5fba8d78a6d3924eb132ab` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4b_failed_run.py` | runner | 2,680 | `34c2fc8af65f0d3741f1d065ff76fd5539c9ad3d274c840ff7a1c1689d8c06f7` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4c.py` | runner | 563 | `f63d86ecf16cf0d4b6c13fc7a95bd74c6bd42711868fb2a5d23b4fd2aba7f9d5` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4d.py` | runner | 563 | `87bbb0e5582547bd032ca5bfa84c78977007e3d7ee7e76cec5f35c4473b497d7` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_artistic_ti4e.py` | runner | 563 | `0a2450c1a8611127935bbb4af9d23c2a27940079f5c2db84c6c13916edaebc13` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_dial_stage2.py` | runner | 5,013 | `95f250c172edb4de0dedba43739dd2aea17a05d2e5739091601b9d889153c99b` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_structural_ground_truth.py` | runner | 10,267 | `78626be3177a58298f8c64cde6628bc7309450f8f68b4cf846f29e8b22fe4630` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_substantive.py` | runner | 7,871 | `85b25a6e49489df920d5b12b4c044a87111351f9a84c3767c370bc62b547b3fc` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_typed_visual.py` | runner | 4,564 | `ccb0279bb019cc929514e28c733df6a995a4298e9823911233c9fbe5e32bad07` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_typed_visual_adaptive.py` | runner | 4,478 | `85b6e02fc073b766d5d82fb5cfffeeeac2ca388feb2a4c0ed9b4b49d11e7d458` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/evaluate_typed_visual_masks.py` | runner | 5,487 | `c35bf94f81887638e669d345fe7cec8b27948453512ae19bc60b9d5608c5a7fc` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/extract_artistic_ni2a_coco_metadata.py` | runner | 935 | `ba7e976125f0320d464fd02682cf104fb277d22405cbc775d74b1f087f4e9dff` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/fit_ti4c_source_observers.py` | runner | 3,742 | `b3331854087b3c23b8c83fc91dc5c353285df36d986446e8b09c10a60be13fbd` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_dataset.py` | runner | 4,370 | `ddfdccd8eda60b2e392f02fff0d379e8a4dc5f7e9c28d1000dce2b49c83af0ae` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_dial_stage2_dataset.py` | runner | 6,430 | `bf95a6fc75172c8f6be15f45dbc9d5bf24f79030c65ce9d021cc45bd100ad873` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_original_virel_masks.py` | runner | 5,110 | `4d2060ebb16f31a5ef6c6c94d4c7e9e6ca587beab972935681cf65c14b3f6624` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_ti4_new_objects.py` | runner | 5,996 | `033fca9fa284802f184ba6c4d73bfcda7da529b9e3adb583bb091bb344d0c454` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_ti4b_fresh_cohort.py` | runner | 4,940 | `c727a5b291406c3c957c569e356aeb5a26bf57c418c58350db2750ff94f3f259` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_ti4c_fresh_cohort.py` | runner | 2,318 | `f37708437f63e59c50be3191a21d625eeddaf15d2991b4842368d544445f97dc` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_ti4d_fresh_cohort.py` | runner | 3,033 | `014280c68208d2df9538d6c5ad05fa2f5dcbabc4d12f827fe0ebf49f87b65053` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_ti4e_fresh_cohort.py` | runner | 3,088 | `6fd030985d5bc9f0adb63056a333cad23dbf3fb67470ceb6275e44958e573771` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/generate_typed_visual_scenes.py` | runner | 6,744 | `52928be58c5cd2d34b4ad27ac7689c0be63c5f8ea88ba8d7dc53d57205191a24` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/infer_smoke.py` | runner | 2,584 | `b1e631af693171c3cdb0a319adc688a3a4c0914338a1e3f9150f52167ef7cdee` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/make_adaptive_contact_sheet.py` | runner | 1,374 | `fa768c5dde41e049f4f3a2fbb6d65878625e65c7e73458f837c0ee3447cc2880` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/make_random_control.py` | runner | 1,717 | `72dad35c957929f3a76bb54b31a1f47968047eb520d2e9f89592195ce940eff7` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/package_artistic_ni0_sealed.py` | runner | 1,964 | `72f2d8a4726376bfc497e77b989d77fa18dfebb6dae367fbf6078c489afcb5c6` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/prepare_artistic_ni0_public.py` | runner | 7,383 | `29d0deef1708ca0e85123226f6a2c455fe6282438263b81f7e75e5c7510dcc9b` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/prepare_artistic_ni2a_public.py` | runner | 8,157 | `91956cd1f1bc4c330cf2c7ba939a742c6bca9be5588946a97f25f240936ade84` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/prepare_artistic_ni2b_public.py` | runner | 9,667 | `3dbb98d00dfe47df1021c8169d25cf68b7e740c7f8512ce0616a2b95ed07ffd1` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/render_artistic_ni2a_typed_overlay.py` | runner | 2,426 | `3924075ccd5cdbf520475830d03bf668ccf2d29ae35a1aef33ce37447b45c374` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/render_artistic_ni2b_typed_overlay.py` | runner | 1,981 | `173d0f2764fa2e37e9f5fad24da596ba083d0e28c029cad9d6f38259310892a7` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_external_single.py` | runner | 17,817 | `9a839431acae677a91d848b36fb49eab7fc7b5d8fd6fe77b3e99b1c9aaecf184` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ni0.py` | runner | 12,504 | `320b1cc068fad3f821bd44d53192ddba24b5c4820d700b72de0d1e67cf2a21bf` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ni2a.py` | runner | 12,525 | `5143cf1b1afd75bedd9d895745fa68d66d1d9614d2d3718b7ed6fd630f59f95d` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ni2b.py` | runner | 9,213 | `d735639d9ba8b24d22ec8138aa166a62bdd73c6e32310c321126202550cb0974` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_soccer2.py` | runner | 15,490 | `5bcff6a7479e21f300244bd076b0d765325b822b1ac1415fb3f382e840ed5b64` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti2d_inference.py` | runner | 4,693 | `bf97352409e29776346ffeb8a08196e0b5126496a7b5df9b8814375a8ff24dc2` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti3_inference.py` | runner | 4,413 | `a65259fbc7a1eaf77ee35b72e8c6395c013ea5440b5d2d669c8e068dc3fe65de` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti3b_retraction.py` | runner | 5,921 | `40d966ffbbb4d2b69703f928d5632b8f87f23a2a3895f066613190da3d500c68` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti3c_componentwise.py` | runner | 7,792 | `7255e1a21d997059852bd57595d3de12c97a74daafbebbeef2cde904b96f5f6b` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti3d_localized.py` | runner | 9,508 | `e5213ff3c3f9f04b1addbfcc9e56ddc247a4115814e78848b83f1970f6a7d518` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti4a.py` | runner | 11,158 | `ef26bdc0470d45e69df237d4e1b293febda828d20e8b45d30ddd55691273ccc6` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti4b.py` | runner | 455 | `711e1ccd0c09f11e85999e6dfb06f654083eac183175814c5815fac29b19e9be` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti4c.py` | runner | 488 | `ed41600b2943e180bab056535b1fbd9e329c9dde84654089c17eaa971a0dedab` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti4d.py` | runner | 488 | `94e52872a35e747bfbf77e78220443c29572bce79479afc47ea15cd24dfc83ad` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_artistic_ti4e.py` | runner | 488 | `2804312c693cf0cc9b005d1d72d99d22917f6e9bec547593dba67f8852642e71` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_dial_stage2_inference.py` | runner | 4,851 | `33b0d65bacef7a2480a89be92bf779959db9b4397131ccb5caa6584d7b1ea05d` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_substantive_inference.py` | runner | 6,187 | `2ddd2d9182991742f9cc53867fb785a537ee35d9318b414d3c92a3fc18073dd8` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_typed_visual_adaptive_inference.py` | runner | 5,004 | `e61218b333427a508866df9ec5c1e29fa0afa3915bef3388cfbb7468287c315c` |
| `2026-08-14_artistic_ti0_concept_naming_registered/scripts/run_typed_visual_inference.py` | runner | 5,343 | `f7243480d45b3918ffd326444dcc84003e10fc75604c51cdec7a6e66cd649a8a` |
| `2026-08-15_artistic_bongard_bp0_registered/run.py` | runner | 17,196 | `236d7f87db6ea90c0306ec83c598f83d1e3c9a79f2a5ebaab650475b753bb4c2` |
| `2026-08-15_artistic_bongard_bp1_compositional_registered/run.py` | runner | 13,273 | `51b071ed924102f2e40107710ecbed254b55dffca226b0f2264ebc522910189f` |
| `2026-08-15_artistic_bongard_bp2_declaration_extension_registered/run.py` | runner | 13,687 | `cd4f5ee51a6af974c04dca56df18cc974655e9a484e3cb352effc44451ce4bb2` |
| `2026-08-15_artistic_bongard_corpus0_registered/run.py` | runner | 25,269 | `4a4d7a0b1d1e710c73759f92969bef88b2f2c7557a4af38fee1c88fe3fed33fa` |
| `2026-08-15_artistic_bongard_corpus1_historical/run.py` | runner | 10,815 | `c21cd06a6c13ae61b0b8468362833a581a7dbdd44cdfa780c9f47a48de675cf8` |
| `2026-08-15_artistic_bongard_corpus2_blind/run_blind.py` | runner | 11,467 | `0a35591eeebd562cf23bcdc6412a5df0b26a7ffaa0d7492963a84add82b3307c` |
| `2026-08-15_artistic_bongard_corpus2_blind/score_revealed.py` | runner | 9,458 | `01ddd70290b2c1594de86f9879093569728d14c526878e69e3cd489ebb266de2` |
| `2026-08-15_artistic_bongard_corpus3_typed/run.py` | runner | 20,770 | `356d9fffe5dc921b8863eae3e1701565c4949cfbeaf219544615543c45fb1816` |
| `2026-08-15_artistic_bongard_corpus4_active/run.py` | runner | 17,117 | `94f2eb7fb299dbea9dc98e09b6f3dc3c85711f55081eb8a5bc63655364a60535` |
| `2026-08-15_artistic_cricket0_v1/run.py` | runner | 4,412 | `62463cd063d1c0fa8582e1296b8a18de106b9d91583ba3a62ca8caaccc8c0a58` |
| `2026-08-16_artistic_active_admission_cricket2/run.py` | runner | 9,357 | `d2af8e1d8b10b5adb5a0bc15e67a0aa9f1cda4092ba0706d40242586209d287b` |
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| `2026-08-16_artistic_tangent_cricket0/make_trajectory_montage.py` | runner | 1,289 | `1457cab22de54aaa8293d5392a59651a7915c4d8b6592fbc31452abfecd51585` |
| `2026-08-16_artistic_tangent_cricket0/run.py` | runner | 9,516 | `aba63ec0754a121a62f73a651f0cc9445ac3212ea159a141895e7aedc91fb61c` |
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| `2026-08-16_dial_artistic_1_mixed_witness_ablation/extract_foreground.py` | runner | 1,883 | `df603ecab67901106e101da2dcae0c4bd781ba852f48427405e513e7cd3a0d52` |
| `2026-08-16_dial_artistic_1_mixed_witness_ablation/run_ablation.py` | runner | 5,775 | `637425ee53112ab92b694db80c4659033aafd9765a33ff320bd57a60c229a35f` |
| `2026-08-16_dial_artistic_2_uncertain_observers/run_observers.py` | runner | 9,686 | `96784cca2954add56f5276701fac6b69f98ed5a1e9711ccb010403c0f7d13e56` |
| `2026-08-16_dial_artistic_2b_supported_observers/run_test.py` | runner | 5,927 | `971c810996d59301685266b3b6c3729af2c7b8483ea37fbd8e53fb17b5e8ab15` |
| `2026-08-16_dial_artistic_2c_prototype_observers/run_test.py` | runner | 7,334 | `93117a06f03ea9df8621c0c2d500b07da0e806c0ba49c78a393116728cf56f26` |
| `2026-08-18_dial_can_paintbrush_01_registered/run.py` | runner | 24,391 | `a968229c1aa079c00ced95a85fd3fcce11f313b178929147a2f2c41ed39d1bca` |
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| `2026-08-18_dial_can_paintbrush_1_registered/run.py` | runner | 30,081 | `6c0e7c32202f84765655e5380fa63ffda13464b2950d6b7f450d79c4a6dad910` |
| `2026-08-18_dial_can_paintbrush_21_algebroid_path/run.py` | runner | 10,144 | `8feae7402b4ec5d3c61fc198996fc478d1469f0a7707b482295226760a75150c` |
| `2026-08-18_dial_can_paintbrush_22_metric_connection/run.py` | runner | 28,765 | `4985e3c307d163a5d5937a8fd06ab76b2c52f6ca0f458468cfea80398d973faf` |
| `2026-08-18_dial_can_paintbrush_2_intent/run.py` | runner | 27,822 | `52b7b3df607904b6b8a00f26cc93be3ee59f57ac5433618955c4ec51c861d63d` |
| `2026-08-19_dial_url_0_exact_type_recovery_registered/run_dial_url_0.py` | runner | 5,620 | `f20698ec9c3b5e16169d0c4107063104322ba21311159a9accab415f435b3508` |
| `2026-08-19_dial_url_1_finite_trajectory_registered/run_dial_url_1.py` | runner | 12,456 | `c21c53653acbdc11f39c717d051ca135f56145196df1b194553e119107d951e8` |
| `2026-08-19_dial_url_2_active_discrimination/run_dial_url_2.py` | runner | 10,151 | `bcc905bf2603c1e775286d674cb0ee4084dc53423f9404497ace0f9a2586f2a6` |
| `2026-08-19_dial_url_3_grammar_synthesis/run_dial_url_3.py` | runner | 12,502 | `3cc722dfacb9e4c4789f13b75dc3de86680cc4325ba94a0a194e34e2d5b7f243` |
| `2026-08-19_dial_url_4_withheld_composition_law/run_dial_url_4.py` | runner | 15,075 | `a0757af612a076c3b56e299a9e65c1eb5d8a032748ce417ccc42429a1f526fb0` |
| `2026-08-19_dilate_codec_0_registered/run.py` | runner | 15,502 | `ed3e24c3529733ac1ec0ab17dd6ee97ee8c54580def73f0b59f31641f84a65de` |
| `2026-08-19_dilate_codec_1_structural_registered/run.py` | runner | 17,559 | `77da4eda269b1ed0c2fba5d193eb5eb2cdaaba4f03d16eeac29cdf294ac77be9` |
| `2026-08-19_dilate_codec_2_sprite_registered/run.py` | runner | 16,561 | `494f237cbbd6e942f5fa576c0186b6e4ce4b358a3805e18d3aba859b6997dd39` |
| `2026-08-19_dilate_codec_3_semantic_track_registered/run.py` | runner | 16,205 | `2498dab35ff38cd2fda31389ca1c7353a0e7e374ba7eddf765b5e209d146000e` |
| `2026-08-19_dilate_egret_0_registered/run.py` | runner | 21,619 | `e3439193a5f6dc7660f4290faece68f22fff4f97b58c06548e673ad01b884f36` |
| `2026-08-19_dilate_egret_1_factored_registered/run.py` | runner | 21,800 | `9c9c092427b0fa09fabcc32f1ada8d03cb432e4fde643225cf241dfaf127fad3` |
| `2026-08-19_dilate_egret_2_cross_video_registered/run.py` | runner | 11,443 | `a179b2c5502bce2119756a4aa695e87639c8516597a9529d30e9009a145226bd` |
| `2026-08-19_dilate_movies_0_theory_routing_registered/run.py` | runner | 10,921 | `a72cc65611d739e9e71c44d305e892557d87206f428e5a7b4fad46e2867f6998` |
| `2026-08-19_dilate_movies_1_conflict_repair_registered/run.py` | runner | 7,338 | `91b32ec384ac35da78c74946ebb18d0d322a84bfdf06fc034973dadaf5dd7e92` |
| `2026-08-19_dilate_movies_2_independent_transport_registered/run.py` | runner | 7,751 | `f503c5bc56f572232759295ca686c87721f54892981f6e6dc2816fc19b184af3` |
| `2026-08-20_ctte0_generated_corpus_registered/run.py` | runner | 21,033 | `bd5eca2794950729c8c423a405f83178d99ee8256bb95e5899ae9b330c9a2884` |
| `2026-08-20_ctte1_alias_registration_registered/run.py` | runner | 22,135 | `2f635fe3710874e5415f06bda9f0c44123d8c08fe1e32b9c0d0a81525ff6db1e` |
| `2026-08-20_ctte2_context_cover_registered/run.py` | runner | 22,459 | `30b42b0b7b3d479d99748df7d890999b0f64b625f4ce87cdb61f6b5e277edaf5` |
| `2026-08-20_ctte3_active_acquisition_registered/run.py` | runner | 19,077 | `55263ec413da77d2e63cc12971e40ed3f27262a8340eb886992c03862c8c798e` |
| `2026-08-20_ctte4_sketch_extension_registered/run.py` | runner | 15,436 | `35dec9568b51507557808b55c0d539d528f5b5db2d9eb03bf8614c3509e16ddf` |
| `2026-08-20_ctte5_domain_transport_registered/run.py` | runner | 24,845 | `d710fba54f2f1530788d719f9a8aad310ba6783e54d35a19c33bf7e742c53414` |
| `2026-08-20_glp1_grant0_program_compilation_registered/run.py` | runner | 19,243 | `cb66e2b5788dd8e37b337b27143f5a513024a80b7ca975707131fa55647f885b` |
| `2026-08-20_glp1_grant1_semantic_firewall_registered/run.py` | runner | 22,205 | `c5bc7919fbce314e3a13bb955cfcade1f5f4508f041ff2b74c12618f320eef5e` |
| `2026-08-20_glp1_grant2_typed_ir_roundtrip_registered/run.py` | runner | 20,386 | `d7b46cc07cee023931040cc0f1b68d19f46611aafd8db17790b5e595c261c7a9` |
| `2026-08-20_glp1_grant3_cross_field_invariants_registered/run.py` | runner | 22,722 | `6206000ebb49f9c48a9aaed458d2f09a0249a2f10354eea3e85e1b6501a00264` |
| `2026-08-20_glp1_grant4_1_sanitized_realization_registered/run.py` | runner | 22,221 | `a5c425bec2a984a09a7d83634766bbcef50c969d7ac3ce575aa2bd18eb36bc3e` |
| `2026-08-20_glp1_grant4_2_structure_first_realization_registered/run.py` | runner | 16,418 | `0b59ca45b151eab07398cb251b133ee6343055ce11a8dc788d4dc226a9580a92` |
| `2026-08-20_glp1_grant4_3_bounded_realization_registered/run.py` | runner | 13,938 | `9e9f6d467170ca7c8194a8a287f765562a0cdd7f4e861d47405077d84d54f7a6` |
| `2026-08-20_glp1_grant4_4_controlled_paraphrase_registered/run.py` | runner | 12,499 | `d1646c7a6cd78ad0e4e024ae7836dbed1d59fc5c2edbcf189b489442a0fb4f3d` |
| `2026-08-20_glp1_grant4_auditable_realization_registered/run.py` | runner | 26,865 | `eedd00f58cd3b8bdf6ea49c2a2023b4955de16810c215deefa248b2c99a35e6b` |
| `2026-08-20_glp1_pilot0_withdrawal_context_registered/run.py` | runner | 10,702 | `9d8a7ee2c3fcceb6fe9909a5676e1768ed8ef3b2b364614a83dd7475aa513a29` |
| `2026-08-20_glp1_pilot1_hidden_context_registered/run.py` | runner | 16,442 | `bde327c18ba545bb74be2ba50903e0a7cde7fa5ee869fd680adc4d8c9da50493` |
| `2026-08-20_sgte1_singing_mice_registered/run.py` | runner | 11,701 | `2423237290afc792a65b530e4430bf4f2cb42b0abab07c57fea7c0fc81e1b0d9` |
| `2026-08-20_sgte2_singing_mice_observer_registered/run.py` | runner | 16,521 | `ac91c4cbae527fd02828e8de457942480ff25855765e054476eadc98aba78911` |
| `2026-08-20_sgte3_active_probe_registered/run.py` | runner | 10,579 | `224f7c65bf05d1901ce42f8952b9c26e037097fca88151af81b8de9a13d447da` |
| `2026-08-20_sgte4_latent_mediator_registered/run.py` | runner | 13,404 | `d53977c284018808b6349a8509823fcae638c1563b6f8521db6785ad0c493ce1` |
| `af0_categorical_feynman.py` | runner | 22,874 | `dfdb97ede23c7a859bce05c130690864986f08356d83d118afe0b15ecc865c1e` |
| `af1_ambiguous_active_feynman.py` | runner | 15,353 | `077d8364b6e5a40bc522a038dfa12307e53839030940df5d1173ec28d9d1f201` |
| `af2_unnamed_generator.py` | runner | 11,851 | `ab8bd760523fea17b85e97ff1088221444a2417517628128199331e1c8d7d59c` |
| `af3_sequential_generator_probes.py` | runner | 9,145 | `767ee33fe9cf4f2dc39148d8122d19fe26faafbb1d69acfd9c347209d96dbd11` |
| `af4_open_generator_search.py` | runner | 12,063 | `39e268db100ae0268617f7b3b2a331a32fd677ca4a32df31eae950f685b50923` |
| `af5_proposal_aware_active.py` | runner | 13,466 | `023c44f1be7f8d1d5628861a810b3e76a06faa2027d0d9faff6cb85a051e8cc5` |
| `agentic_0a_exact_workflow_composition.py` | runner | 17,743 | `767b7ab05990efd00bd51b35d0d64538972cab41bd5cb298b0c48bd106d81bda` |
| `agentic_0b1_active_boundary_acquisition.py` | runner | 19,808 | `439a623f45b5a51828ca4f66f9089b9f6e10333ea4614c8301f7a7115cebc476` |
| `agentic_0b_estimated_clic_composition.py` | runner | 17,816 | `7654eaa0cc63891191979d97021652abf0f4a2aeb7626b6fdc668d1017def3fa` |
| `agentic_0c1_active_optic_acquisition.py` | runner | 20,075 | `bf05d5dee0ec87e90b49f406fec83c5a2a9da663d515ad56bdbeeea1f76371df` |
| `agentic_0c_estimated_optic_composition.py` | runner | 18,818 | `848ebd1d5784a5d0c57f41a21a4c40dc753ec2dff34b2884d58586c932daf82a` |
| `agentic_0d1_active_relic_acquisition.py` | runner | 22,323 | `97efde7d12b536a4748ffa99dd314ca82a69b0094e4fe48c5f677ae35186599e` |
| `agentic_0d2_hierarchical_active_relic.py` | runner | 22,456 | `1af697cf757da4547aa9577287704108961264e6d79572e1e2a5c72ead2cc7bd` |
| `agentic_0d_estimated_relic_composition.py` | runner | 20,264 | `dfa9705ba7221446f8bc7d2cba7fcb2fa5b5ba4c3dc6f5e2eb21507ceac69ad4` |
| `agentic_lea_0/extract_proposal.py` | runner | 694 | `664325512a39a1dabb79582902bb55a199990304403c34b11a4f098ccb7a6e73` |
| `agentic_lea_0/run_audit.py` | runner | 4,860 | `338607d9312bdb07f9eb2d1f93f685da5a849593a2d530cfe43411e1a9fcb9f8` |
| `agentic_lea_0/run_lea.py` | runner | 2,609 | `5f0010eda29d7b3a55d83093bc26ef56d752773bfb5d7f97b878c6bfe47b2138` |
| `agentic_lea_1/run_audit.py` | runner | 6,085 | `a01bc8b5e268d30be45ab882a5de38a01b3c87311aa894c78e2820b781cd440b` |
| `agentic_lea_1/run_followup.py` | runner | 2,694 | `8164bccc65c225c61d2388512b26484004f710c96237be7c358161b5b02afd10` |
| `agentic_lea_1/run_followup2.py` | runner | 2,786 | `66e26c74aeb0259b34ce4b7f4541c58d648bff3feed45c6a89b55c8aa75c42a3` |
| `agentic_lea_1/run_lea.py` | runner | 2,489 | `5cb49cda0154e012ebbf4f99a9c35f5c7930e26b298bb5075d1eb9c6cd2df88d` |
| `agentic_lea_2/run_audit.py` | runner | 5,943 | `1ad4207d94a90f3bc2d2859707bba1e9191f9a3b7994c53eb7a640be844a6aab` |
| `agentic_lea_2/run_lea.py` | runner | 1,904 | `2ddbfac701b010c87b1dcb53d470818781e964d86d08a86831eb8f6ac3895068` |
| `agentic_lea_2b/run_audit.py` | runner | 5,492 | `2fb7bf2a943add2964b338d869818950e13c2dfce91521385a73d625484ed586` |
| `agentic_lea_2b/run_lea.py` | runner | 1,803 | `8d465b15fcc5dcec5c6c3d362545cdcfe772b86234cd25e1098eb15feefcebf4` |
| `agentic_lea_2c/freeze_stage_a.py` | runner | 490 | `9e3d34c3ae2688a339b9f47b5d4c41e4f9dda1d9012d623db2d20e9ac432a6e5` |
| `agentic_lea_2c/run_audit.py` | runner | 9,761 | `345aee653f7ec7e6ad286eb6379cdb2f54e08e9d9f0f6b22257ed8483ffe6403` |
| `agentic_lea_2c/run_stage_a.py` | runner | 1,777 | `3244a884f3d13dc36f537352b8d569f4d7767fa1adec6dd891a5eb896217392e` |
| `agentic_lea_2c/run_stage_a_followup.py` | runner | 2,645 | `ffb4589820b68eae92118fe9befd14bc9f4f3ce17e63103797965822eda10bac` |
| `agentic_lea_2c/run_stage_a_followup2.py` | runner | 2,571 | `ba91f4fef7f1f834dd7fb40f0f295e6de0d8b59075334e377f7fb9774d06df8e` |
| `agentic_lea_2c/run_stage_b.py` | runner | 1,871 | `83b8abd0daaacabe81c6124c187a88208eb92b0d41daf0447296b7dacf4da4ee` |
| `dial0a_linear_exact.py` | implementation | 23,038 | `c7aa43fdfdd3b7a18f0a789b265b53c890b11fa794471c77601f066ff7d5f79e` |
| `dial0b_differentiation.py` | implementation | 21,948 | `76de7181cee18e6bdddcfb482f189505a8d584252bd66414a4493797ac7c0567` |
| `dial0c_mixed_witness_ablation.py` | implementation | 16,783 | `6cb758445d11a258e81c676e6e69645526dc51924660b096bd5553476bbd877d` |
| `dial0d_dual_operator_estimation.py` | implementation | 21,614 | `9c8acaea41b8759cabb0a7b7d95f3c62221989b597f0c64eb6c708676993875c` |
| `dial1_static_causal_identification.py` | implementation | 24,162 | `3b414a431b0c514a8137b4f758d2dc6362075f1af74a1de7c2a025a75020ec6a` |
| `dial1b_equivalence_intervention_design.py` | implementation | 19,081 | `a24bbb0b484528b8055045823234038f241ff804c4c09e6866e1ee338cd75609` |
| `dial1c_active_intervention_selection.py` | implementation | 18,610 | `7701a11e7c1609be6405f63a448a37e426a0e23e5b72545d573c3e04cefbcc63` |
| `dial1d_sequential_intervention_escalation.py` | implementation | 15,419 | `aa5daf261d1b76d329fa72ee20cc53c9aa5fcf3123f66df765460aef3094e276` |
| `dial2a_nonstationary_decisions.py` | implementation | 23,914 | `da9660e59134f245f491acdb55a775c0cb02343caba85ba15534cdc1fe30835d` |
| `dial2n1_girl_state_admission.py` | implementation | 16,587 | `306f3d39d03b3d652d16b436bbace212b8f358f46f9293f05d48f42483191b87` |
| `dial2n_girl_recurrence.py` | implementation | 19,705 | `f2e9fd3d0372831201f85e00b438de21cbb2393047937a57941c6fd3efe6ecb6` |
| `dial_allora_a0b0_exact_mediator.py` | implementation | 18,069 | `3d25c930bb3755453c2f0ab57f83c6d96f89f5b5af0aa2967c15197509510d85` |
| `dial_allora_active_acquisition.py` | implementation | 14,683 | `9fe434433e1e0e49f1eb48198b508c1ad47bba8ddb61549ba8f81bf87a2dc452` |
| `dial_allora_affine_learner.py` | implementation | 28,901 | `22b7cb3bd564682a441fba97e6a2dded6aeb3bef006cc71b1d7e0e7d2d56c1a6` |
| `dial_allora_b1_exact_construction.py` | implementation | 19,182 | `d446e30d00d17f0b0d7c2455420060c3c54aa9d5deaace99cfcb2f5ec8cdc13a` |
| `dial_allora_b2_noisy_subspace.py` | implementation | 23,256 | `de67d3fc1c65b295917c7c5ab49eff5d97447e79487174752fd93566073531d7` |
| `dial_allora_b3_trained_adapters.py` | implementation | 33,144 | `16d96d9e6f9556af14eb092458118bdfb93c26db41301f7684e2e15de76bb5fb` |
| `dial_allora_b4_transformer_transfer.py` | implementation | 22,995 | `9b8f749cc8ad3292ea8cfe6852154e6e89bf85580b141ab2eb1497894393a890` |
| `dial_allora_b51_task_witness.py` | implementation | 21,398 | `da267ba1402f95ce782fcacd6bb474ec9ddc08bc227c33385d818beaf060b41d` |
| `dial_allora_b5_pretrained_semantic.py` | implementation | 27,833 | `084f75b24583913a21b7eb9a84365e76b7e04dd3a798a05d127610aab5e28498` |
| `dial_allora_b61_local_descent.py` | implementation | 22,723 | `d4a99b33440b56fd1785c077dade0dd13f4be60fb2a18b81b079bb5afb5ed118` |
| `dial_allora_b62_semantic_descent.py` | implementation | 20,877 | `3d45adc80ebf04e087b49bdbdd8bdd6875e0cbfe3680144cb68b66e885dc8646` |
| `dial_allora_b63_relational_observer.py` | implementation | 22,762 | `21e135ff458bee871658399ccf6eaa00bbb34b0166f2fb2f3b6a24fa03f8d72e` |
| `dial_allora_b64_active_acquisition.py` | implementation | 18,192 | `a49ad3fc1a034bdc870e8d794c22dd5531db3ffcabe44b8e21bfbccf3d464bda` |
| `dial_allora_b65_sequential_acquisition.py` | implementation | 17,853 | `fd3e0b8bdb77f5b970f21c1ffba4206e74f2288d791c85dc75fc135a2cefadbe` |
| `dial_allora_b66_hierarchical_calibration.py` | implementation | 13,440 | `74541b09a6d81c213905cf3cd736fbd765a98e96da204cfc58d3f0dc2b91d979` |
| `dial_allora_b67_transport_calibration.py` | implementation | 11,376 | `eb76d05238e2b007eea39de992fda582a1fb8294d4f59034464930b6ec85d8c8` |
| `dial_allora_b68_chart_bundle.py` | implementation | 15,891 | `718ba57c863ee081cd67a2c7801be7dffdc7e4d7c4377a00fa1a7168d100a254` |
| `dial_allora_b69_semantic_bundle.py` | implementation | 14,104 | `1c0c8586a6c79d24e391a298426152e41ed5e9eae12579a79e4006511fb48543` |
| `dial_allora_b6_generative_adapter.py` | implementation | 31,119 | `3b0467c7408f8fb66a04bbe75aea194607fe8ebabaa3242f5d25baa2aa39483c` |
| `dial_allora_boundary_enrollment.py` | implementation | 14,181 | `bc2ed5cdb47ae96af5c59ba64944c21b7e0618da5891d22fc224ee86d923b015` |
| `dial_allora_oracle_ceiling.py` | implementation | 10,594 | `e47363390a45df02a5499ac302aac0354dd470049cc52747c467c9c7cf1511f2` |
| `dial_allora_oracle_expanded.py` | implementation | 15,081 | `1970f270de0812bfa7c3bd679f3081938fd70951b9c4188968363707ae4d1262` |
| `dial_allora_selective_learner.py` | implementation | 12,098 | `2d3bc4b459acca6de7de2c9bbd8ef32f78520d0a6c6e0666c08db28642c5259b` |
| `dial_can_0_bracket_calibration.py` | implementation | 20,215 | `f70dea2b984958abd898662abf9dd432613f44417666ef9d8edaca409954ab73` |
| `dial_can_1_stochastic_gan.py` | implementation | 37,119 | `7d0699e1cc0835535cb6736be7a9417154b0473131de7fa450b80b2501b4eebe` |
| `dial_can_1b_smooth_observer.py` | implementation | 3,275 | `02fe9c4d9146490aec6f7527233f8e9d25920251ea2f8740b5dd81980310522e` |
| `dial_can_2_decision_regions.py` | implementation | 28,727 | `9a56c72e2c455f85ad5e00809cb32bb70c488d31d112cb3d5347228e288e2ca9` |
| `dial_can_3_finite_estimator.py` | implementation | 23,644 | `9946f64b086ef9d359cc63c626be54abdf9b98dbadbe4643587f87bbfac05f06` |
| `dial_girl_anytime_mixture.py` | implementation | 11,214 | `e0789106afab2d985f4a6966bb14a75aae679ecc9b85b80a5b6e0b75befebc83` |
| `dial_girl_composite_markov.py` | implementation | 15,938 | `216e6ed581c6f1b93e598d53a7b8fc5dcd6a093e9217634878f9bb904bfeb949` |
| `dial_girl_composite_null.py` | implementation | 14,126 | `53dc55ed1040a7e3dcf6778836b4b263a86b923635592db49b6c27d375ad89ea` |
| `dial_girl_decision_value.py` | implementation | 10,549 | `271d2f6403796cd7842b2d0ea0ce509e18a3159cf974a0211dc818d10bc68825` |
| `dial_girl_hmm_core.py` | implementation | 7,538 | `c0f1e00fc6ae7d8e11544d09356d2fd23ce325053d1f348a3205c0289ea81f2a` |
| `dial_girl_hmm_equivalence.py` | implementation | 13,375 | `4e4a1fb1f2910eb49bc0254fcb7da2b7644506bc58bf94ec00a938cfc34f22b8` |
| `dial_girl_learned_filter.py` | implementation | 16,005 | `6d2767c0d71bb876ab45777c98daac29f51eaafab464c3bb2274d598bf35ab2f` |
| `dial_girl_learned_prototypes.py` | implementation | 20,622 | `3acef52fd22489cf3974169ee13764c578f540c300f0c8bffbe9b486ddd24e3e` |
| `dial_girl_long_horizon_markov.py` | implementation | 9,899 | `b6a34fa2a313f46383c7d8fbb27ec04e36cf71d4ff0a73bfd53c5d608326022f` |
| `dial_girl_pilot_priors.py` | implementation | 16,044 | `e7c72fe1cdd29c26d5c79b0831fe03fbc08d4c9d80a691c3d71661a126a67476` |
| `dial_girl_sequential_evidence.py` | implementation | 12,398 | `7742f37406f660d158e5e1b7c25edc7b55e4bffe94c6399255cf3c1dfcb2a6e7` |
| `dial_girl_skillopt.py` | implementation | 22,795 | `7e147deb6cfa6b348885aa1b3127fdaacf22f4be5810cb84db6feba4944ab3cc` |
| `dial_girl_switch_transport.py` | implementation | 14,784 | `5104acf9babdc1e3fa78ee29a37965f49e109b1fbc9b64d6b017854f472c4c42` |
| `dial_girl_trajectory.py` | implementation | 25,973 | `bad0d6b35da0506b1b21b6709510913999f458fbf8e5bd3459501d19a3a9a17b` |
| `dial_schema_girl_0.py` | implementation | 13,702 | `9af7870ea4ce689cff4567664fbd868d4e61b8b516fc5fe2fd1c1e7ce393e1b8` |
| `dial_schema_girl_1a_textworld.py` | implementation | 23,306 | `1ce81ef53fb7a41b454a1450a7540f52b33f0cc073679795684c782c0108c314` |
| `dial_schema_girl_1b_balanced_textworld.py` | implementation | 13,686 | `bed1eedecfc7ce1c07a3fb6798ab7b12d90317eb3532f21bef5da877420a0a09` |
| `dial_schema_girl_1c_planning.py` | implementation | 21,610 | `a0305e4a93281da6370b88bf05421be3f0660071c23f4f22103a33e93f6533ff` |
| `dial_schema_girl_1d1_balanced_relational_fit.py` | implementation | 16,289 | `33ceb21ff9961c837f29a35e58f59f76c3be27fa5680821a972de30519bc8229` |
| `dial_schema_girl_1d_relational_counterwitness.py` | implementation | 25,165 | `f22186bbab6bfc67420a3a621624032610d18d4238dde6f0c696e8d71f6d2f14` |
| `dial_schema_girl_1e_relational_planning_replay.py` | implementation | 13,393 | `aba42d76b97702fc5950d15d9cdfe747ab6960223b92f1f26a9791ef6db8a5f7` |
| `dial_schema_girl_1f1_total_location_state.py` | implementation | 11,757 | `8c41dada66d4eb6c7a368a5661df93432e7ef1a65758dcfc07e9c089c3da43cd` |
| `dial_schema_girl_1f_containment_discovery.py` | implementation | 14,300 | `0e21d91d7a9359e3db3291be9b2e94516a750083ee4fd02902becf2a3b79a734` |
| `dial_schema_girl_1g1_colocated_take_planning.py` | implementation | 15,721 | `cc196c0f7d4763d8a89f8c5b4d623b819057f8a994df88f009708867ff2408d5` |
| `dial_schema_girl_1g_fresh_containment_planning.py` | implementation | 18,686 | `826713e5e0b11712c59107ea99c35eb75eeaf11bc364e929628c2ffad04f7f48` |
| `dial_schema_girl_1h_persistent_tool_planning.py` | implementation | 16,240 | `7e65b417c7213fffcb3b249a6f72f87a38906ea31175efa11f0e3268c62275f8` |
| `dial_schema_girl_1h_recovery_analysis.py` | implementation | 8,345 | `42e0519f1d3a5b9b560e1d97d95d3f28594be8411d26352317d9bcafe0bc0108` |
| `dial_skfm_1a_exact_closure_repair.py` | implementation | 13,643 | `a36b1354149a7d7dee6c90a02d45c00a19094c74989ddb1de738b3430d7d2978` |
| `dial_skfm_1b_finite_operator_uncertainty.py` | implementation | 13,502 | `081143e58013a34ba8a129d9304c24988e3c674688b044fff64d12b6854c2d66` |
| `dial_skfm_1c_active_counterwitness.py` | implementation | 17,153 | `ba9413291bd9faf5f012a1a73059cacc17c2cfdbfe6bf208854d4bea2921ea12` |
| `dial_skfm_1d_explicit_alternative_posterior.py` | implementation | 19,701 | `f3665b0e6412b651ae813f60e797c172b6139051514c4ae3a8799c6c6bbcae86` |
| `dial_skfm_factored.py` | implementation | 18,007 | `f848dc320c362d922cb8adc23da31a8bee9eb1f4c18a1714cd3250a999a2b915` |
| `dial_skfm_typed_transducer.py` | implementation | 8,341 | `b9ebd3e32362d7b6ed861512475e1092dbe9adc8cc2cccb7d9a154c598d15f0a` |
| `dial_skillopt1_preregistered.py` | implementation | 26,431 | `913d976b9a075e7551353f58964b428e1537bf7cfd78d8dd94fe6c0bec792cd1` |
| `dial_skillopt1d_factored.py` | implementation | 19,252 | `02c9e52a0c4ee8f3625ff70daf2836d7d82e83aca2b315872f09905c0283d10f` |
| `dial_skillopt1d_factored_v2.py` | implementation | 2,634 | `2a669e3e8fec94d407a8a2f889e006ad94090f5feb66302acb44ab96391a85ff` |
| `dial_skillopt_registered.py` | implementation | 24,951 | `c8ca8fb2af905c3e1724c9e052cfcc27b7ddcb9ef98eefde4cd8a6bfed3533c4` |
| `ipc0_beta.py` | runner | 27,319 | `008144423eef4ac6ffae391b255da86b2bf0457867cd91ff1d49c6bee2bab428` |
| `ipc0_beta_v2.py` | runner | 15,626 | `cc1ef0966beecbb0693b3dcf2d20101881189ab2fca3b94b470b5c36f8205f80` |
| `ipc0_gamma.py` | runner | 23,714 | `8f363bb81efc224da1962dc004f2b3365eb6b62e461a8f8829ef8f5738a66e9f` |
| `ipc0_smoke.py` | runner | 16,236 | `c092acde88f49d09323fbe1e44e31274845904c74e733d8afbae12bdb55617e3` |
| `ipc10_predictive_state_sheaf.py` | runner | 34,452 | `f0a800ba69c7311cc2292787fb48791583d77769b73394a9db5b3afba3e7f18e` |
| `ipc11_hidden_overlap_registration.py` | runner | 33,028 | `c4c7a34d974b4ff51669b6b30ef74a6fcee837f9725692827bb9b0a454670112` |
| `ipc12_cech_cycle_consistency.py` | runner | 20,233 | `1cfde80ee52640b942b0ec38ce26f1aebdd886b7cb320b9f5db5b5f82df10917` |
| `ipc13_approximate_holonomy.py` | runner | 24,438 | `571e895d71a0d874946ef7a7bd2b1cda2e3551fbab98d3ff7158ad6f8663c949` |
| `ipc14_trajectory_holonomy.py` | runner | 28,598 | `6e5e6795d82f2f2f7c2f2bf1b16178e81b7c1454f20e16ffb16e07c3c4257dde` |
| `ipc15_power_coverage_ladder.py` | runner | 20,701 | `643beed905bd1e379933ea0eacf939eb49c3ac429de8db51ef4815b1872b61e4` |
| `ipc16_active_interventions.py` | runner | 18,476 | `d36e611d972a4f175ee757ef745349441f3caf8762ffb0cd22e07c8bdf4669b2` |
| `ipc1_symmetry.py` | runner | 22,910 | `d46b886021408a0cac5a50179e1120f28aabd82d1fc1632ac9f2c5ed832ae93d` |
| `ipc2_diagnostic.py` | runner | 13,986 | `66c011ef95ed9ab1f3039f18df970799e15846c03fc488b46a33f1cec73ae6df` |
| `ipc2_mixed_symmetry.py` | runner | 22,246 | `37714a419ea46462db11a48b484177fac7b3a2d4ba17fadc026a298857e2a5ac` |
| `ipc3_latent_property.py` | runner | 20,475 | `09e880f033e7f68f33b624342a36e986757e2906230a5610c2d83535836689b0` |
| `ipc4_latent_factors.py` | runner | 15,737 | `7e6b06573a78c9d19a9ad71062c6fd54f34f714ec34565a43bb6617de0f4705d` |
| `ipc5_noisy_latent_factors.py` | runner | 21,813 | `46df700aec07fb076db37d82f932e5585d9ce4399bd5b61570933adcad9046a3` |
| `ipc6_doctrine_obstruction.py` | runner | 13,720 | `ada5514952483852c0a15f07d8a50bc233d62734ddb12e5a199e875deb5889fa` |
| `ipc7_entangled_latent_graph.py` | runner | 26,912 | `ca5e8e4a942b353c01c26d347440967cbedf565ac28f126ddab88e2c155cad2b` |
| `ipc8_edge_counter_witness.py` | runner | 13,822 | `40c059dbbc6baf8c9d16f569f8c7d0e9079dd2e4ce9be4fa3889b0107c008759` |
| `ipc9_anchor_free_subspace.py` | runner | 24,615 | `a78d3577d61f7a21187a1e3180325c5b53229fc46131c128c65c4cba3bab4a34` |
| `relic_alfworld_a0_full_validation.py` | implementation | 25,426 | `ae654affcf6a8f72585c06935336c539243a5b6fe349255135dfaa2d02bda1ef` |
| `relic_alfworld_a0_manifest_launcher.py` | implementation | 7,131 | `f628a5b7ebd9d95eac23c07aca5706c426cdb3d369a36980cf01059be7e01c4f` |
| `relic_alfworld_a1_controls_sensitivity.py` | implementation | 19,609 | `114aad481cc8d24ed719750b5dd83af3fb8f612fa04e086de8248c5408ae45ed` |
| `relic_alfworld_b0_recovery_analysis.py` | implementation | 4,465 | `3cc54096682d13b54def2f7f978f39beb5804d086a971a96aed3ad7854f41b9e` |
| `relic_alfworld_b0_textdqn_smoke.py` | implementation | 12,105 | `af00a82503223802dd4c89e18481d5aa79a77e1e90da69201fcc6562f41d07d1` |
| `relic_alfworld_b1_intrinsic_calibration.py` | implementation | 13,615 | `c9616c7e968be0f204bf38bc74776369997fe6a7d1007c0956e540d7b104c091` |
| `relic_alfworld_b2_learning_onset.py` | implementation | 12,268 | `ab5650e49b4e5b05cfda793fb035d3b5dcd0a9af798e2c404112a5314526f734` |
| `relic_alfworld_b2_train_wrapper.py` | implementation | 4,851 | `602f141658f8761ddeb18bb3dabeea3b23b99b6eecae0155f6383edbed92f570` |
| `relic_alfworld_b3_recovery_launcher.py` | implementation | 4,441 | `c5c29387d4213d95da191b24160cce4099b491e5c8e9daf2a2f7aeaa248c3b7f` |
| `relic_alfworld_b3_schema_accommodation.py` | implementation | 19,806 | `0d101f5d23ae96462137c9f7fa7f171395344bf079cb3a0d07dcd4d5252fae1d` |
| `relic_alfworld_b4_composition_construction.py` | implementation | 23,286 | `0c78924286051cf2018b5b0aa54d8a103f007ef740ddbad264952cdc0b9f5dab` |
| `relic_alfworld_b4_recovery.py` | implementation | 8,628 | `4bf91457b7a36992a6302cc4ee8a36a57fd3db678c6a318e03849af07d666264` |
| `relic_alfworld_b51_active_counterwitness.py` | implementation | 20,201 | `21b5da06759480314044baf33e9baf728f98e41831edcb24c45b5f67d97d0fc7` |
| `relic_alfworld_b52_behavioral_quotient.py` | implementation | 20,651 | `07e9e7f4e573bbcf61ab9592a447529cb102c10b34de2da047ae7789f97d95a7` |
| `relic_alfworld_b5_stage_construction.py` | implementation | 22,480 | `a7ba9b32a6d19799775a17be45bdcbf74ca809614a7f24e81c4310b4f3f1582b` |