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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 An arXiv preprint posted 5 August 2026, not yet peer reviewed.
Turning “what makes a good puzzle” into a formula: DeepMind quantifies the counter-intuitiveness of chess puzzles
One piece today. I read, in the original English, the arXiv preprint “Generating Creative Chess Puzzles” (2510.23881, October 2025) by Xidong Feng and colleagues at Google DeepMind. Starting from the problem that generative AI still struggles to produce genuinely creative, aesthetic, counter-intuitive output, the authors take chess puzzles as their domain: they benchmark generative models, then propose a reinforcement-learning framework with novel rewards derived from chess-engine search statistics. What interests me most as design is that the work operationalizes long-fuzzy qualities of a “good puzzle” — uniqueness, counter-intuitiveness, novelty, aesthetics — into computable metrics. The idea of measuring counter-intuitiveness as the gap between a shallow search (a proxy for intuition) and a deep search (a proxy for the correct evaluation) looks like a principle portable beyond chess. I take it up as a pre-review preprint, with its date made explicit.
Feng et al.: Can AI Generate Counter-Intuitive Chess Puzzles? — Fukai Reads
A study, led by a Google DeepMind team, on generating creative chess puzzles with AI. A generative model trained on Lichess data is tuned with reinforcement learning, raising the rate of counter-intuitive puzzles from 0.22% to 2.5% (about tenfold). The highlight is how they reduce creativity to numbers a machine can measure.
