Datasets:
Add native model, family and completion-depth views
Browse files- README.md +92 -2
- SHA256SUMS.txt +4 -1
- completion-depths.jsonl +77 -0
- family-summary.jsonl +110 -0
- model-summary.jsonl +11 -0
README.md
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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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data_files:
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- split: test
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path: episodes.jsonl
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---
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# Recursive-Play Bench
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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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Author and contact: **EnvLoop Research — research@envloop.ai**.
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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. The [editable source](paper-source.zip) places `main.tex` at the archive root and preserves its relative `staging/` and `figures/` paths. It contains no authoring runtime, operational evidence or private checks. 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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## Files and configurations
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| File | Contents |
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| `episodes.csv` | Exact final 1,650-row outcome table; eleven configurations, 150 environments each |
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| `episodes.jsonl` | Viewer-compatible JSONL projection of the same 1,650 CSV rows; all 14 cells per row remain strings, including empty cells |
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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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| `paper-source.zip` | Exactly 38 LaTeX compilation inputs: `main.tex`, included sections, bibliography, five vector figures and twenty recorded PNGs |
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| `SHA256SUMS.txt` | File hashes for this release payload |
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-
The Hub `tasks`
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## What a task record means
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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: [rl-environment, tabular, 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: models
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default: true
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data_files:
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- split: test
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path: model-summary.jsonl
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- config_name: tasks
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data_files:
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- split: test
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data_files:
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- split: test
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path: episodes.jsonl
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- config_name: challenges
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data_files:
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- split: test
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path: challenge-tasks.jsonl
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- config_name: families
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data_files:
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- split: test
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path: family-summary.jsonl
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- config_name: depths
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data_files:
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- split: test
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path: completion-depths.jsonl
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---
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# Recursive-Play Bench
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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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Author and contact: **EnvLoop Research — research@envloop.ai**.
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The [Recursive Play collection](https://huggingface.co/collections/EnvLoop/recursive-play) brings together the dataset, practice Space and links to the paper and its sources.
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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. The [editable source](paper-source.zip) places `main.tex` at the archive root and preserves its relative `staging/` and `figures/` paths. It contains no authoring runtime, operational evidence or private checks. 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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## Explore the dataset on Hugging Face
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The native viewer provides six configurations. Each uses JSONL, so the configurations share a compatible loader while retaining their separate schemas.
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| Configuration | Rows | What you can inspect |
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|---|---:|---|
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| [models](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/models/test) | 11 | Ranked configuration results and selected-case resource coverage |
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| [tasks](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/tasks/test) | 150 | Actual task identities, canonical seeds, mechanics and quality flags |
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| [results](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/results/test) | 1,650 | Every selected episode, with the original cell strings preserved |
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| [challenges](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/challenges/test) | 15 | Tasks no evaluated configuration completed under the recorded budgets |
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| [families](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/families/test) | 110 | Eleven configurations across ten authored families |
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| [depths](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/depths/test) | 77 | Counts at all seven completion depths for every configuration |
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`models` is the default view. The three aggregate tables are calculated from the frozen final summary and outcome matrix. They are additional views of the same 1,650 episodes, not new trials. Every model offers 150 episodes and 900 levels; every model/family pair offers 15 episodes and 90 levels. Depth counts sum to 150 per model. `primaryRank` preserves ties, while `displayOrder` records the paper's stable ordering of tied configurations.
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Numeric columns support the native viewer's distribution charts and value filters. Missing token totals and unpriced costs remain JSON `null`. Known token and monetary figures remain subtotals; unknown-use reservations are not invoices. Request-grain and episode-grain missingness remain separate in `tokenUsageGrain` and must not be added into a single missingness rate. Family percentages and depth shares are calculated directly from their stated denominators without changing any counts.
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### Load a configuration reproducibly
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For the aggregate views, copy the current commit hash from [Files and versions](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/tree/main) after checking that the revision includes `model-summary.jsonl`, `family-summary.jsonl` and `completion-depths.jsonl`. Passing that hash records exactly which view you used.
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```python
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from datasets import load_dataset
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repo = "EnvLoop/Recursive-Play-Bench"
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revision = input("Dataset commit SHA from Files and versions: ").strip()
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models = load_dataset(repo, name="models", split="test", revision=revision)
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families = load_dataset(repo, name="families", split="test", revision=revision)
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depths = load_dataset(repo, name="depths", split="test", revision=revision)
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```
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The original task and result configurations, together with the V14 paper, are also available at the existing audited revision `cb12d923f369109626b083ae6e44a3139abedca9`. That revision predates the aggregate views.
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```python
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raw_revision = "cb12d923f369109626b083ae6e44a3139abedca9"
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tasks = load_dataset(repo, name="tasks", split="test", revision=raw_revision)
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results = load_dataset(repo, name="results", split="test", revision=raw_revision)
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```
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The [V14 paper](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/blob/cb12d923f369109626b083ae6e44a3139abedca9/recursive-play-paper.pdf) and [LaTeX source archive](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/blob/cb12d923f369109626b083ae6e44a3139abedca9/paper-source.zip) use the same recorded revision. No DOI or arXiv identifier is claimed.
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The `test` label organises these exposed research records. It does not turn them into an independently hidden acceptance set. See the [official loading guide](https://huggingface.co/docs/datasets/loading) for configuration and revision parameters.
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### Query an aggregate in Data Studio
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Open `models/test`, then open its SQL Console and check the table name shown in the console. The example below assumes that the selected view is named `test`. If the console shows a different name, replace `test` in the query with that name before running it.
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```sql
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SELECT primaryRank, displayName, fullWins, fullWinRatePercent,
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levelsCompleted, levelClearRatePercent
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FROM test
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ORDER BY displayOrder;
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```
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To inspect the challenge table, select `challenges/test` and check its view name in the SQL Console. This query uses `test` for that selected view and reproduces the paper's challenge ordering:
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```sql
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SELECT environmentId, family, maximumCompletedLevelsAcrossEleven,
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astraCompletedLevels, referenceTotalActions
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FROM test
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ORDER BY maximumCompletedLevelsAcrossEleven,
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referenceTotalActions, environmentId;
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```
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The [native SQL Console](https://huggingface.co/docs/hub/main/en/datasets-viewer-sql-console) can share query results and export tables. The [Data Studio guide](https://huggingface.co/docs/hub/en/data-studio) explains filtering, search, missing-value distributions and row sharing. These facilities inspect the recorded outcomes; they do not run a solver or create a new benchmark score.
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## Files and configurations
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| File | Contents |
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| `episodes.csv` | Exact final 1,650-row outcome table; eleven configurations, 150 environments each |
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| `episodes.jsonl` | Viewer-compatible JSONL projection of the same 1,650 CSV rows; all 14 cells per row remain strings, including empty cells |
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| `summary.json` | Unmodified final score and resource summary, including known/unknown use and eligibility accounting |
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| `model-summary.jsonl` | Eleven typed model aggregates with tied ranks and unchanged resource coverage |
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| `family-summary.jsonl` | 110 typed model/family aggregates with explicit 15-episode and 90-level denominators |
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| `completion-depths.jsonl` | 77 typed model/depth counts with a 150-episode denominator |
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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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| `paper-source.zip` | Exactly 38 LaTeX compilation inputs: `main.tex`, included sections, bibliography, five vector figures and twenty recorded PNGs |
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| `SHA256SUMS.txt` | File hashes for this release payload |
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The six Hub configurations all read JSONL. `tasks` reads `tasks.jsonl`, while `results` reads `episodes.jsonl` as a lossless view of the original outcome CSV. `episodes.csv` remains the unmodified authoritative CSV. The JSONL is a lossless cell-for-cell projection: no fields, identifiers, scores or empty values are changed or coerced. All configurations 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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## What a task record means
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SHA256SUMS.txt
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-
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362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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274f17ae926aacc603083810582940ae9c7194b923596117eb81a0017291523c environment-source.zip
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4494147848379325cfdf46712ce0ef8f9e19fcb0afc06f1937f9500a8680d430 episodes.csv
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ab43c34eab505dad12eadb4859f2f154d28d8338d1bc1041796d1467b0f5f1be episodes.jsonl
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aa3da0943fdde15ab8cca6748a3b6821f2a608926942c4221bee3c24eff133a1 original-930.csv
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483ed0877115545a72be397b67fae29750d63959f2c1a3064cf9ee8dca344df0 paper-source.zip
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ac85fad43badb07de15b05cd90cbf89483d83bfe564553ca9d7097937714937f recursive-play-paper.pdf
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da4491c4a753e18e7dca339afcabe488868e7932db1f6a9af341b33266f03b83 README.md
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362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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5261f6c28b14ebd3e9a5a10139729c717b2792d24ab363713bd3381220273696 completion-depths.jsonl
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274f17ae926aacc603083810582940ae9c7194b923596117eb81a0017291523c environment-source.zip
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4494147848379325cfdf46712ce0ef8f9e19fcb0afc06f1937f9500a8680d430 episodes.csv
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ab43c34eab505dad12eadb4859f2f154d28d8338d1bc1041796d1467b0f5f1be episodes.jsonl
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d0a24139b0ff9da6048078b9c6ffafc95e178495e8eb5e474710b6b25d576d7d family-summary.jsonl
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d7577ec8af018434812d932082115289df402eb90d0005edc15eed6947f0d970 model-summary.jsonl
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aa3da0943fdde15ab8cca6748a3b6821f2a608926942c4221bee3c24eff133a1 original-930.csv
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483ed0877115545a72be397b67fae29750d63959f2c1a3064cf9ee8dca344df0 paper-source.zip
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ac85fad43badb07de15b05cd90cbf89483d83bfe564553ca9d7097937714937f recursive-play-paper.pdf
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completion-depths.jsonl
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":0,"episodeCount":2,"episodesOffered":150,"episodeSharePercent":1.3333333333333333}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":1,"episodeCount":2,"episodesOffered":150,"episodeSharePercent":1.3333333333333333}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":2,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":3,"episodeCount":3,"episodesOffered":150,"episodeSharePercent":2.0}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":4,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":5,"episodeCount":3,"episodesOffered":150,"episodeSharePercent":2.0}
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{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"completedLevels":6,"episodeCount":135,"episodesOffered":150,"episodeSharePercent":90.0}
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{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":0,"episodeCount":50,"episodesOffered":150,"episodeSharePercent":33.333333333333336}
|
| 9 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":1,"episodeCount":22,"episodesOffered":150,"episodeSharePercent":14.666666666666666}
|
| 10 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":2,"episodeCount":16,"episodesOffered":150,"episodeSharePercent":10.666666666666666}
|
| 11 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":3,"episodeCount":12,"episodesOffered":150,"episodeSharePercent":8.0}
|
| 12 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":4,"episodeCount":12,"episodesOffered":150,"episodeSharePercent":8.0}
|
| 13 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":5,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 14 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"completedLevels":6,"episodeCount":29,"episodesOffered":150,"episodeSharePercent":19.333333333333332}
|
| 15 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":0,"episodeCount":32,"episodesOffered":150,"episodeSharePercent":21.333333333333332}
|
| 16 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":1,"episodeCount":27,"episodesOffered":150,"episodeSharePercent":18.0}
|
| 17 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":2,"episodeCount":27,"episodesOffered":150,"episodeSharePercent":18.0}
|
| 18 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":3,"episodeCount":21,"episodesOffered":150,"episodeSharePercent":14.0}
|
| 19 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":4,"episodeCount":12,"episodesOffered":150,"episodeSharePercent":8.0}
|
| 20 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":5,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 21 |
+
{"model":"gpt-6-sol","displayName":"GPT-6 Sol","primaryRank":3,"displayOrder":3,"completedLevels":6,"episodeCount":22,"episodesOffered":150,"episodeSharePercent":14.666666666666666}
|
| 22 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":0,"episodeCount":68,"episodesOffered":150,"episodeSharePercent":45.333333333333336}
|
| 23 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":1,"episodeCount":18,"episodesOffered":150,"episodeSharePercent":12.0}
|
| 24 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":2,"episodeCount":15,"episodesOffered":150,"episodeSharePercent":10.0}
|
| 25 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":3,"episodeCount":11,"episodesOffered":150,"episodeSharePercent":7.333333333333333}
|
| 26 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":4,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 27 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":5,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 28 |
+
{"model":"gpt-5.6-terra","displayName":"GPT-5.6 Terra","primaryRank":4,"displayOrder":4,"completedLevels":6,"episodeCount":20,"episodesOffered":150,"episodeSharePercent":13.333333333333334}
|
| 29 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":0,"episodeCount":83,"episodesOffered":150,"episodeSharePercent":55.333333333333336}
|
| 30 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":1,"episodeCount":22,"episodesOffered":150,"episodeSharePercent":14.666666666666666}
|
| 31 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":2,"episodeCount":21,"episodesOffered":150,"episodeSharePercent":14.0}
|
| 32 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":3,"episodeCount":12,"episodesOffered":150,"episodeSharePercent":8.0}
|
| 33 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":4,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
|
| 34 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":5,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
|
| 35 |
+
{"model":"gpt-5.6-luna","displayName":"GPT-5.6 Luna","primaryRank":5,"displayOrder":5,"completedLevels":6,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
|
| 36 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":0,"episodeCount":95,"episodesOffered":150,"episodeSharePercent":63.333333333333336}
|
| 37 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":1,"episodeCount":22,"episodesOffered":150,"episodeSharePercent":14.666666666666666}
|
| 38 |
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{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":2,"episodeCount":18,"episodesOffered":150,"episodeSharePercent":12.0}
|
| 39 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":3,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 40 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":4,"episodeCount":3,"episodesOffered":150,"episodeSharePercent":2.0}
|
| 41 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":5,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 42 |
+
{"model":"qwen3.8-flash","displayName":"Qwen 3.8 Flash","primaryRank":6,"displayOrder":6,"completedLevels":6,"episodeCount":2,"episodesOffered":150,"episodeSharePercent":1.3333333333333333}
|
| 43 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":0,"episodeCount":102,"episodesOffered":150,"episodeSharePercent":68.0}
|
| 44 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":1,"episodeCount":27,"episodesOffered":150,"episodeSharePercent":18.0}
|
| 45 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":2,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 46 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":3,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 47 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":4,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 48 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":5,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 49 |
+
{"model":"gpt-6-luna","displayName":"GPT-6 Luna","primaryRank":6,"displayOrder":7,"completedLevels":6,"episodeCount":2,"episodesOffered":150,"episodeSharePercent":1.3333333333333333}
|
| 50 |
+
{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":0,"episodeCount":100,"episodesOffered":150,"episodeSharePercent":66.66666666666667}
|
| 51 |
+
{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":1,"episodeCount":29,"episodesOffered":150,"episodeSharePercent":19.333333333333332}
|
| 52 |
+
{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":2,"episodeCount":10,"episodesOffered":150,"episodeSharePercent":6.666666666666667}
|
| 53 |
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{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":3,"episodeCount":7,"episodesOffered":150,"episodeSharePercent":4.666666666666667}
|
| 54 |
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{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":4,"episodeCount":3,"episodesOffered":150,"episodeSharePercent":2.0}
|
| 55 |
+
{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":5,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 56 |
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{"model":"qwen3.8-max-0902","displayName":"Qwen 3.8 Max 0902","primaryRank":8,"displayOrder":8,"completedLevels":6,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 57 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":0,"episodeCount":111,"episodesOffered":150,"episodeSharePercent":74.0}
|
| 58 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":1,"episodeCount":26,"episodesOffered":150,"episodeSharePercent":17.333333333333332}
|
| 59 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":2,"episodeCount":6,"episodesOffered":150,"episodeSharePercent":4.0}
|
| 60 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":3,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
|
| 61 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":4,"episodeCount":2,"episodesOffered":150,"episodeSharePercent":1.3333333333333333}
|
| 62 |
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{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":5,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 63 |
+
{"model":"deepseek-v4.1-flash","displayName":"DeepSeek V4.1 Flash","primaryRank":9,"displayOrder":9,"completedLevels":6,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 64 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":0,"episodeCount":126,"episodesOffered":150,"episodeSharePercent":84.0}
|
| 65 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":1,"episodeCount":13,"episodesOffered":150,"episodeSharePercent":8.666666666666666}
|
| 66 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":2,"episodeCount":9,"episodesOffered":150,"episodeSharePercent":6.0}
|
| 67 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":3,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 68 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":4,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 69 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":5,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 70 |
+
{"model":"kimi-k3","displayName":"Kimi K3","primaryRank":9,"displayOrder":10,"completedLevels":6,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 71 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":0,"episodeCount":128,"episodesOffered":150,"episodeSharePercent":85.33333333333333}
|
| 72 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":1,"episodeCount":17,"episodesOffered":150,"episodeSharePercent":11.333333333333334}
|
| 73 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":2,"episodeCount":4,"episodesOffered":150,"episodeSharePercent":2.6666666666666665}
|
| 74 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":3,"episodeCount":1,"episodesOffered":150,"episodeSharePercent":0.6666666666666666}
|
| 75 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":4,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 76 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":5,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
| 77 |
+
{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"completedLevels":6,"episodeCount":0,"episodesOffered":150,"episodeSharePercent":0.0}
|
family-summary.jsonl
ADDED
|
@@ -0,0 +1,110 @@
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|
| 1 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"a","family":"Spatial","familyOrder":1,"episodesOffered":15,"levelsOffered":90,"fullWins":13,"fullWinRatePercent":86.66666666666667,"levelsCompleted":84,"levelClearRatePercent":93.33333333333333,"normal720SecondEndings":1,"normal512ActionEndings":1,"abandoned":0}
|
| 2 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"b","family":"Circuits","familyOrder":2,"episodesOffered":15,"levelsOffered":90,"fullWins":13,"fullWinRatePercent":86.66666666666667,"levelsCompleted":79,"levelClearRatePercent":87.77777777777777,"normal720SecondEndings":0,"normal512ActionEndings":2,"abandoned":0}
|
| 3 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"c","family":"Objects","familyOrder":3,"episodesOffered":15,"levelsOffered":90,"fullWins":12,"fullWinRatePercent":80.0,"levelsCompleted":79,"levelClearRatePercent":87.77777777777777,"normal720SecondEndings":2,"normal512ActionEndings":1,"abandoned":0}
|
| 4 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"d","family":"Temporal","familyOrder":4,"episodesOffered":15,"levelsOffered":90,"fullWins":15,"fullWinRatePercent":100.0,"levelsCompleted":90,"levelClearRatePercent":100.0,"normal720SecondEndings":0,"normal512ActionEndings":0,"abandoned":0}
|
| 5 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"e","family":"Relations","familyOrder":5,"episodesOffered":15,"levelsOffered":90,"fullWins":15,"fullWinRatePercent":100.0,"levelsCompleted":90,"levelClearRatePercent":100.0,"normal720SecondEndings":0,"normal512ActionEndings":0,"abandoned":0}
|
| 6 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"f","family":"Resources","familyOrder":6,"episodesOffered":15,"levelsOffered":90,"fullWins":15,"fullWinRatePercent":100.0,"levelsCompleted":90,"levelClearRatePercent":100.0,"normal720SecondEndings":0,"normal512ActionEndings":0,"abandoned":0}
|
| 7 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"g","family":"Memory","familyOrder":7,"episodesOffered":15,"levelsOffered":90,"fullWins":14,"fullWinRatePercent":93.33333333333333,"levelsCompleted":89,"levelClearRatePercent":98.88888888888889,"normal720SecondEndings":0,"normal512ActionEndings":1,"abandoned":0}
|
| 8 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"h","family":"Coordination","familyOrder":8,"episodesOffered":15,"levelsOffered":90,"fullWins":13,"fullWinRatePercent":86.66666666666667,"levelsCompleted":82,"levelClearRatePercent":91.11111111111111,"normal720SecondEndings":0,"normal512ActionEndings":2,"abandoned":0}
|
| 9 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"i","family":"Topology","familyOrder":9,"episodesOffered":15,"levelsOffered":90,"fullWins":13,"fullWinRatePercent":86.66666666666667,"levelsCompleted":83,"levelClearRatePercent":92.22222222222223,"normal720SecondEndings":1,"normal512ActionEndings":1,"abandoned":0}
|
| 10 |
+
{"model":"gpt-6-astra","displayName":"GPT-6 Astra","primaryRank":1,"displayOrder":1,"familyId":"j","family":"Composition","familyOrder":10,"episodesOffered":15,"levelsOffered":90,"fullWins":12,"fullWinRatePercent":80.0,"levelsCompleted":82,"levelClearRatePercent":91.11111111111111,"normal720SecondEndings":2,"normal512ActionEndings":1,"abandoned":0}
|
| 11 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"familyId":"a","family":"Spatial","familyOrder":1,"episodesOffered":15,"levelsOffered":90,"fullWins":3,"fullWinRatePercent":20.0,"levelsCompleted":52,"levelClearRatePercent":57.77777777777778,"normal720SecondEndings":9,"normal512ActionEndings":3,"abandoned":0}
|
| 12 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"familyId":"b","family":"Circuits","familyOrder":2,"episodesOffered":15,"levelsOffered":90,"fullWins":4,"fullWinRatePercent":26.666666666666668,"levelsCompleted":31,"levelClearRatePercent":34.44444444444444,"normal720SecondEndings":6,"normal512ActionEndings":5,"abandoned":0}
|
| 13 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"familyId":"c","family":"Objects","familyOrder":3,"episodesOffered":15,"levelsOffered":90,"fullWins":0,"fullWinRatePercent":0.0,"levelsCompleted":6,"levelClearRatePercent":6.666666666666667,"normal720SecondEndings":5,"normal512ActionEndings":10,"abandoned":0}
|
| 14 |
+
{"model":"gpt-5.6-sol","displayName":"GPT-5.6 Sol","primaryRank":2,"displayOrder":2,"familyId":"d","family":"Temporal","familyOrder":4,"episodesOffered":15,"levelsOffered":90,"fullWins":1,"fullWinRatePercent":6.666666666666667,"levelsCompleted":37,"levelClearRatePercent":41.111111111111114,"normal720SecondEndings":12,"normal512ActionEndings":2,"abandoned":0}
|
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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|
| 104 |
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{"model":"ZHIPU/GLM-5.3-Flash","displayName":"GLM 5.3 Flash","primaryRank":9,"displayOrder":11,"familyId":"d","family":"Temporal","familyOrder":4,"episodesOffered":15,"levelsOffered":90,"fullWins":0,"fullWinRatePercent":0.0,"levelsCompleted":3,"levelClearRatePercent":3.3333333333333335,"normal720SecondEndings":15,"normal512ActionEndings":0,"abandoned":0}
|
| 105 |
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|
| 106 |
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|
| 107 |
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| 108 |
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| 109 |
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| 110 |
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model-summary.jsonl
ADDED
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@@ -0,0 +1,11 @@
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|
| 1 |
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|
| 2 |
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| 3 |
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| 5 |
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| 9 |
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