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Gao & Dubé: Letting a machine do the first read of player-made math levels — Fukai Reads
An arXiv preprint by Jie Gao and Adam K. Dubé of McGill University. A children's math learning game has a Creative Mode in which advanced players build their own levels, but reading every submission by hand does not scale. The authors extracted features from 206 levels (86 by experts, 120 by players) and trained a classifier to do the first read. Random forest gave the best recall and F1, scoring 82.42±5.71% accuracy and 72.70±9.22% F1 in the outer loop.
Tarun Kumar S: What Happens When You Tell a Human-Move Predictor the Last 20 Moves and the Clock — Fukai Reads
A paper by Tarun Kumar S of Peargent Labs on Otter, a chess AI that predicts human moves. Where earlier models treated each position independently, Otter conditions on the last 20 moves and on remaining clock time, reaching 55.23% top-1 accuracy with 15.3M parameters — 1.98 points above Maia 2. Of the +7.62 point gain over a board-only baseline, history contributes +5.24 and the clock +2.38. An arXiv preprint posted 5 August 2026, not yet peer reviewed.
