About
About MoveLens
An independent research prototype that tests whether a general-purpose language model provides a move-preference signal that is not already captured by chess statistics or by a human-move model.
All views
- 10-minute research demoThe guided six-step presentation.
- Research overviewThe earlier long-form overview: statistical modeling, Maia-3 and the LLM experiment on one featured position.
- Compare modelsOne position, every legal move, original move vs controlled substitute.
- Where the models disagreeSix rule-selected held-out decisions and two reserved Qwen slots.
- Eight-step walkthroughThe longer tour with evaluation and provenance detail.
- Hybrid demoThe validated A/B/C/D hybrid experiment on prepared games.
- Full game reportPer-move report for a prepared game.
- ResultsEvery evaluation table with intervals.
- MethodologyDefinitions, protocol, provenance, downloads.
Independent Prototype
MoveLens was developed from publicly available research, documentation, open-source chess engines, released models and independently constructed experiments. It does not use Professor Regan's private reference files, adjudication records or proprietary calibrations.
The purpose of the prototype is to determine which modern AI signals may be worth validating against the full reference framework.
Limitations
- The statistical baseline is an independent reconstruction from publicly documented concepts, not Professor Regan's production system or calibration.
- Controlled engine substitutions are not identical to real-world cheating: substitutes are Stockfish lines inserted into otherwise human decisions and evaluated in the original positions (games are not replayed).
- Maia-3 models human move preference, not cheating. A low Maia probability means a move is unusual for that rating, nothing more.
- Qwen3-8B-Base is a general language model, not a chess-specialized detector. It provides a move-preference signal that can be evaluated for predictive value; it does not detect cheating.
- Tokenization and the way moves are written (UCI strings, ordering, prompt wording) can change LLM likelihoods; the prompt is frozen and hashed but other formulations were not explored.
- Results depend on the dataset (one month of online Lichess rapid/classical games, presumed human), the engine settings (Stockfish 19, depth 12, MultiPV 5) and the substitution protocol; bootstrap intervals condition on the frozen models and apply no multiplicity correction.
- Classification performance or correlation is not proof of misconduct and must not be used alone for adjudication.
This is an independent reconstruction based on publicly documented concepts and is not Professor Regan's production analyzer.