README.md

metadata

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 · 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.