Otter: A Time-Aware, History-Conditioned Human Chess AI

• URL: http://arxiv.org/abs/2608.05206v1
• Date: Wed, 05 Aug 2026 07:47:30 GMT
• Title: Otter: A Time-Aware, History-Conditioned Human Chess AI
• Abstract summary: Otter predicts human move selection by modeling play as a time-aware, sequential process.<n>Otter is trained on 6.1 billion positions from 117 million Lichess rapid games over 30 days on a single T4 GPU.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Otter is a 15.3M-parameter human chess AI that predicts human move selection by modeling play as a time-aware, sequential process rather than treating each position in isolation. It combines two conditioning signals: (1) a move history encoder that conditions predictions on the last 20 moves, capturing opening preferences, positional drift, and intra-game behavioral tendencies; and (2) a time control module that modulates predictions based on clock pressure. Otter is trained on 6.1 billion positions from 117 million Lichess rapid games over 30 days on a single T4 GPU. Otter achieves 55.23% top-1 and 90.95% top-5 move-prediction accuracy, surpassing the prior state-of-the-art human chess model, Maia 2, with far fewer parameters and less training data. Across 11 Elo brackets (<1100 to >=2000), accuracy peaks at 57.38% in the 1900-1999 bracket. These results show that modeling chess as a time-aware, sequential activity yields more human-accurate move prediction than position-only approaches, using a smaller model. Code, trained models, and complete training logs are publicly released.

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