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Running
Running
| title: Recursive Play | |
| emoji: 🧩 | |
| colorFrom: green | |
| colorTo: yellow | |
| sdk: static | |
| app_file: index.html | |
| pinned: false | |
| short_description: Play real puzzle rules and explore 11 model configurations. | |
| # Recursive Play | |
| By **EnvLoop Research** — research@envloop.ai | |
| A browser-only practice demo and results explorer for Recursive-Play: 150 authored candidate environments, six levels each, with 1,650 independently eligible outcomes from 11 configurations. | |
| - **Try four real environments.** The existing deterministic TypeScript rules run locally in the browser. Reset restores the exact initial instance and costs one action. A practice session allows 512 actions and 720 seconds across the six sequential levels. New practice sessions are separate from the published model results. Neutral numbered controls preserve the puzzle interface; button 6 can also receive a click on the pixel grid. | |
| - **Explore verified results.** The rankings, four vector plots, selected actual episode frames and 24-page paper reuse the independently reviewed V14 artifacts. No new model, game-server or training requests are made by this Space. | |
| - **Practice is source-exposed.** This static demo is not a secure blind-evaluation environment and does not submit scores to the benchmark. Solver results in the paper were collected separately with withheld authored rules and constructive witnesses. | |
| [Dataset and benchmark artifacts](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench) · [Paper PDF](recursive-play-paper.pdf) | |
| The work measures task construction and solving outcomes. The illustrated learning loop is a proposed future experiment; training gains or recursive improvement have not been measured here. All 150 environments remain authored candidates: quality flags and the limits of difficulty calibration are documented in the paper and dataset. | |
| ## Files and reproduction | |
| `index.html`, `styles.css` and prebuilt `app.js` need no server-side build. `app-source.ts` and the four `engine-*.ts` files expose the local practice implementation. `benchmark.json` holds selected display data; `asset-provenance.json` records source hashes. The PDF, SVG figures and episode PNGs are unmodified copies of the reviewed artifacts. | |
| All files use relative paths. No external fonts, analytics, paid APIs or user credentials are required. Requests concerning source permissions or reuse: research@envloop.ai. No project-wide license is asserted by this card. | |