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Publish Recursive Play benchmark artifacts

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.gitattributes CHANGED
@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ recursive-play-paper.pdf filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ pretty_name: Recursive-Play Bench
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+ language: [en]
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+ task_categories: [reinforcement-learning]
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+ tags: [interactive-environments, puzzles, agent-evaluation, environment-generation, rsi-research]
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+ size_categories: [1K<n<10K]
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+ configs:
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+ - config_name: tasks
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+ data_files:
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+ - split: test
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+ path: tasks.jsonl
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+ - config_name: results
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+ data_files:
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+ - split: test
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+ path: episodes.csv
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+ ---
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+
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+ # Recursive-Play Bench
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+
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+ **150 executable puzzle environments, six sequential levels per environment, and 1,650 independently eligible model/environment results across eleven configurations.** AI-assisted construction supplies witness-solvable challenges that tested agents do not yet finish. Fifteen tasks have no complete six-level win from any evaluated configuration; two have zero completed levels across all eleven configurations.
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+
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+ Author and contact: **EnvLoop Research — research@envloop.ai**.
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+
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+ The [paper](recursive-play-paper.pdf) explains the task construction, fixed-budget evaluation, matched-case examples, uncertainty and prospective improvement pathway. This release contains task metadata, executable environment source and selected outcome tables. It contains no model weights, complete training trajectories or measured training/RSI gain.
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+
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+ ## Files and configurations
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+
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+ | File | Contents |
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+ |---|---|
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+ | `tasks.jsonl` | 150 actual environment records with immutable IDs and canonical seeds, authored mechanics, source hashes, quality flags and reference action counts |
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+ | `episodes.csv` | Exact final 1,650-row outcome table; eleven configurations, 150 environments each |
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+ | `summary.json` | Unmodified final score and resource summary, including known/unknown use and eligibility accounting |
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+ | `original-930.csv` | Exact preserved earlier 930-row table; every field is retained in the final table |
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+ | `challenge-tasks.jsonl` | Fifteen tasks left unfinished by every configuration, with exact completion-depth vectors |
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+ | `challenge-inventory.json` | Existing curated evidence for those fifteen tasks and the canonical reference validation |
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+ | `environment-source.zip` | Ten source modules plus their standard-library-only interface, registry and generation plan |
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+ | `recursive-play-paper.pdf` | The current 24-page English paper |
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+ | `SHA256SUMS.txt` | File hashes for this release payload |
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+
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+ The Hub `tasks` configuration reads `tasks.jsonl`; `results` reads `episodes.csv`. Both use a `test` split label for organisation. This bank was exposed during development: **that label does not make it a sealed or independent held-out test set.** No train/validation partition or new training labels are fabricated.
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+
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+ ## What a task record means
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+
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+ `environmentId` and `canonicalSeed` reproduce the evaluated instance. Every environment has six levels. `family` is the paper's author category; `sourceFamily` preserves the implementation category. Mechanics and mechanism notes describe the authored rules, not independently certified novelty. `sourceArchiveMember` locates the exact module in the source ZIP; its SHA-256 is recorded beside it.
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+
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+ The author API implements `init(level, seed)`, `observe(state)`, `step(state, action)` and `solved(state)`. The source archive preserves its original module layout so imports resolve after extraction. It uses the project's Node runtime requirement (Node 26.7 or newer) and standard-library facilities. Source contains author state and constructive witnesses. A blind solver must receive only projected observations and neutral controls, never author metadata, seeds, source, state or witnesses.
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+
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+ All 900 canonical seed/level references were replayed successfully: 20,092 reference actions, with a maximum 347-action six-level curriculum. Separately, the construction audit exercised 2,700 level instances at three default seeds. These counts are different audits. A constructive path establishes reachability and action-budget feasibility; it is neither an optimal solution nor proof that a blind agent can discover it within the time limit.
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+
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+ The construction manifest declares **zero independently certified-hard environments** and retains quality flags for **42 environments**. All flags remain in the task records; human difficulty calibration and independent novelty validation remain absent. Easy candidates stay in the full 150-task denominator.
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+
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+ ## Scores and protocol
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+
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+ Each fresh episode uses the original model identifier, canonical seed and blind prompt, with **512 charged actions and 720 seconds across all six levels**. Reset costs an action; advancing after a confirmed level win is free. The first independently eligible outcome is selected regardless of score. Confirmed engineering interruptions are handled separately under evidence review; raw attempts remain preserved in the private audit store.
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+
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+ - Complete-task rate: six-level wins divided by **150 tasks** per configuration.
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+ - Cleared-level rate: attained levels divided by **900 offered levels** per configuration.
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+ - The complete table has **1,650 outcomes = 11 configurations × 150 tasks**.
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+ - It reuses the earlier **930 results unchanged** and adds **720 independently audited results**.
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+ - Equal complete-task counts retain tied ranks; cleared-level totals order tied rows for display.
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+
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+ An ordinary `time_budget` ending means the six-level curriculum was unfinished when the game clock expired. Partial progress remains credited. Among 1,255 ordinary time-budget endings, 484 completed at least one level. Waiting for model responses, tools and gameplay all consume the clock; that endpoint alone does not identify a cognitive or engineering cause. Ordinary time/action exhaustion remains in the score denominator. Zero completed levels does not prove that no meaningful observation occurred.
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+
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+ The strongest observed configuration completes 135/150 tasks. The remaining fifteen tasks have no full win from any of the eleven configurations. This is a result under the recorded budgets and serving/tool setups, not a universal impossibility claim or a model-only causal comparison. Known usage subtotals and retained unknown quantities are not silently converted into complete totals or zero cost.
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+
66
+ ## Intended use and limits
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+
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+ Use this exposed bank for environment research, reproducible result analysis, development diagnostics and prospective corrective-data experiments. The task source and privileged references are public author resources, so new confirmation experiments must freeze a distinct acceptance set and isolate solver access. Source hashes and replay check recorded consistency; they do not certify semantic fairness, human learnability or operating-system attestation.
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+
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+ The proposed improvement pathway is to select verified failures, obtain corrected trajectories, train a candidate and test it on independently frozen tasks with retained baselines, no-regression checks and rollback. **This release does not demonstrate trained improvement, persistent adaptation or recursive self-improvement.** It does not implement an autonomous general RSI system.
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+
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+ ## Rights and permissions
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+
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+ No project-wide redistribution license is declared in the source snapshot; contact **research@envloop.ai** for permissions. No new permissive or research-only license is invented here. No third-party runtime dependencies are bundled: the environment archive contains the project's authored modules and standard-library interface only. Bibliographic sources and figure references remain in the paper.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{envloop_recursive_play,
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+ title = {Recursive-Play Bench},
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+ author = {{EnvLoop Research}},
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+ url = {https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench}
83
+ }
84
+ ```
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+ "gptLedger": {
1410
+ "boundedUnknownTechnicalTokens": 320000000,
1411
+ "cachedReasoningSubsetsNotAddedAgain": true,
1412
+ "historicalUnknownUsageByModelAndEvidenceTier": {
1413
+ "earlierUnpinnedProbeTokens": "UNKNOWN_RETAINED_NOT_IMPUTED_ZERO",
1414
+ "firstFourEpisodes": {
1415
+ "gpt-6-luna": 0,
1416
+ "gpt-6-sol": 0
1417
+ },
1418
+ "latestPartial48H2Faults": {
1419
+ "excludedEpisodes": 2,
1420
+ "fullTechnicalReserves": 40000000,
1421
+ "knownExcludedNumericTokens": 971408,
1422
+ "unknownResponses": 2
1423
+ },
1424
+ "originalExcludedSlot22IndividualResponses": {
1425
+ "gpt-6-luna": 1,
1426
+ "gpt-6-sol": 0
1427
+ },
1428
+ "partial12BoundedTechnicalTokens": 40000000,
1429
+ "partial12NativeAggregateUnknownEpisodes": {
1430
+ "gpt-6-luna": 1,
1431
+ "gpt-6-sol": 1
1432
+ },
1433
+ "partial12ProviderUnknownResponses": {
1434
+ "gpt-6-luna": 1,
1435
+ "gpt-6-sol": 1
1436
+ },
1437
+ "partial6BoundedTechnicalTokens": 40000000,
1438
+ "partial6NativeAggregateUnknownEpisodes": {
1439
+ "gpt-6-luna": 2,
1440
+ "gpt-6-sol": 1
1441
+ },
1442
+ "partial6ProviderUnknownResponses": {
1443
+ "gpt-6-luna": 1,
1444
+ "gpt-6-sol": 1
1445
+ },
1446
+ "twentyPredeadlineGatewayFaults": {
1447
+ "episodeUnknownFlags": {
1448
+ "gpt-6-luna": 10,
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+ "gpt-6-sol": 10
1450
+ },
1451
+ "fullTechnicalReserves": 180000000,
1452
+ "numericPartialUseRetained": 176884
1453
+ },
1454
+ "v6Excluded3IndividualResponses": {
1455
+ "gpt-6-luna": 2,
1456
+ "gpt-6-sol": 3
1457
+ },
1458
+ "v6ExcludedCanaryEpisodes": {
1459
+ "gpt-6-luna": 1,
1460
+ "gpt-6-sol": 0
1461
+ },
1462
+ "v9CanaryEpisodes": {
1463
+ "gpt-6-luna": 0,
1464
+ "gpt-6-sol": 0
1465
+ }
1466
+ },
1467
+ "includesActual179Partial6Partial12And14Use": true,
1468
+ "knownTechnicalTokens": 346120731,
1469
+ "legacyUnknownIndividualProviderResponses": 5,
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+ "monetaryCnyStatus": "UNKNOWN",
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+ "providerHiddenRetries": "UNKNOWN",
1472
+ "usageGrainsSeparate": true
1473
+ },
1474
+ "inventoryRecordsAreNotUniqueNetworkRequestCounts": true,
1475
+ "legacyUnknownIndividualProviderResponsesIsGrandTotal": false,
1476
+ "legacyUnknownIndividualProviderResponsesScope": "v6-excluded3-individual-provider-responses-only",
1477
+ "originalSlot22AdditionalUnknownIndividualProviderResponses": 1,
1478
+ "originalSlot22AdditionalUnknownIndividualProviderResponsesScope": "original-slot22-individual-provider-response",
1479
+ "paceNote": "Wall time includes provider and gameplay latency; no decoder TPS is inferred.",
1480
+ "tokenCoverageNote": "Request/native-episode/provider-response grains and cached/reasoning subsets remain separate.",
1481
+ "unscoredInventoryByKind": {
1482
+ "early-direct-8080": 2,
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+ "early-isolated-direct-8080": 2,
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+ "early-scoped-v1-direct": 2,
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+ "early-scoped-v2-cli-v1-direct": 2,
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+ "early-scoped-v3-cli-v2-aborted-cli": 4,
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+ "early-scoped-v3-cli-v2-aborted-direct": 2,
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+ "early-scoped-v4-cli-v3-direct": 2,
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+ "failed-shell-env-native-turn-physical-expansion-UNKNOWN": 1,
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+ "gpt6-isolated-h1-four-post-independent-capacity-proof": 4,
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+ "gpt6-isolated-h1-two-model-independent-functional-proof": 2,
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+ "gpt6-next20-current-six-final-source-readiness": 1,
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+ "gpt6-normal-local-route-empty-pool-readonly-diagnostic": 1,
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+ "gpt6-paid-allowed-normal-scheduling-restoration": 1,
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+ "gpt6-post-h2-fault-independent-existing-request-numeric-readiness": 1,
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+ "gpt6-postexit276-readiness": 2,
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+ "gpt6-r07-isolated-binary-v2-sandbox-stage-observation": 1,
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+ "gpt6-r07-same-binary-simple-mode-synthetic-route-failure-candidate": 1,
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+ "gpt6-r08-shared-local-exact-model-source-only-amendment": 1,
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+ "gpt6-sol33-v1-nonscored-probe-independent-review": 1,
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+ "gpt6-sol33-v2-nonscored-probe-independent-offline-review": 1,
1504
+ "gpt6-v6-b04-readonly-network-stability-window": 1,
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+ "gpt6-v6-b04-stability-v2-r04-window-start": 1,
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+ "gpt6-v6-b04-stability-v3-r04-window-end-no-go": 1,
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+ "gpt6-v6-fresh-readonly-stability-window-end-20260928": 1,
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+ "gpt6-v6-fresh-readonly-stability-window-start-20260928": 1,
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+ "gpt6-v6-live-health-readonly-pin-observation": 1,
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+ "gpt6-v9-phase2-next-six-readiness-source-only": 1,
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+ "gpt6-v9-phase2-six-normal-local-api-probe-v1-source-only-handoff": 1,
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+ "independent-gpt6-normal-local-api-probe-v1-source-review-hold": 1,
1513
+ "local-r04-direct-sol-direct": 1,
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+ "local-r06-direct-luna-direct": 1,
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+ "local-read-only-window": 5,
1516
+ "non-scored-model-post": 17,
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+ "paid-restored-cap4": 4,
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+ "paid-restored-normal-api-pair": 2,
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+ "provider-usage-diagnostic-GET": 38,
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+ "proxy-local-reject-no-upstream-start": 1,
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+ "read-only-provider-get-observation": 20,
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+ "slot22-readiness-cli": 2,
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+ "slot22-readiness-direct": 1,
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+ "sol33-nonscored-probe-safety-addendum": 1,
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+ "sol33-offline-launch-source": 1,
1526
+ "source-only/read-only-scope": 2,
1527
+ "unexecuted-source-only-readiness": 4,
1528
+ "v6-round01-cli": 4,
1529
+ "v6-round01-direct": 2,
1530
+ "v6-round02-cli": 4,
1531
+ "v6-round02-direct": 2,
1532
+ "v6-round03-cli": 4,
1533
+ "v6-round03-direct": 2,
1534
+ "v9-normal-api-pair": 2
1535
+ },
1536
+ "unscoredInventoryRecords": 171,
1537
+ "unscoredProbeCount": 171
1538
+ },
1539
+ "previousRawOrPublishedRecordsModified": false,
1540
+ "schemaVersion": 4,
1541
+ "selectionPolicy": "First independently valid original/consumed attempt regardless score; no global ordinal overwrite.",
1542
+ "sourceHashes": {
1543
+ "frozen930Sha256": "6191f3669fc360aae3e01cce3216cb643ddb57affa5c2d118879c7515149af1e",
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+ "independentReviewSha256": "bbd28d7060febf598103686f626f13f9c22a1c1be0ab891c9b7e92e0b1ab1e29",
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+ "new720ManifestSha256": "fb8f2ca5fc7eae6fb80b5fb68d9dafe0dc502b1d2ebec7d41b17771f705c098d",
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+ "sourceReporterSha256": "764c091cf4796b7bd717e7721ce7a7ffc3e0ce05d5bb327f39ab2b916a2feb17"
1547
+ },
1548
+ "terminationPolicy": "Ordinary720-second and512-action endings are eligible model outcomes."
1549
+ }
tasks.jsonl ADDED
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