chess

Memory Chess 在 Product Hunt 推出 2.0 版本：展示棋局几秒后清空棋盘，让用户凭记忆复原，新增每日棋盘、双向变动的技能评分、记忆进度面板和 24 种语言支持，免费且无需注册。

作者以 AlphaGo 之后围棋界的发展为镜，探讨数学家应如何适应 AI 生成的难以理解的证明：像围棋高手那样转向研究 AI 结果来提升人类集体认知，并接受 AI 让数学知识民主化的未来。

Princeton researchers train a 4B-parameter LLM to reach 2700 Elo chess strength using a natural-language analogue of the AlphaZero algorithm, with architectural and algorithmic innovations that also allow it to accurately explain its moves. The authors say the technique generalizes to other games, robotics, and computer use.

Luke Salamone distilled Stockfish evaluations into 3.9 billion chess positions and trained a hybrid ResNet-Transformer value network on 1 billion of them, releasing the open Gigafish 3.8B state-value dataset on Hugging Face — reportedly the largest open chess state-value dataset to date.

Researchers introduce a chess-puzzle benchmark (1,118 Lichess puzzles) for automatic prompt optimization of frozen LLMs, evaluating six APO algorithms on eight models with cheap, deterministic, renewable scoring at a total cost of around $800.

The paper introduces Queen, a 4B-parameter chess-language model that explains its moves while playing at Grandmaster level, using a silent-expert chess encoder fused with an instruction-tuned LM via cross-attention and an iterative Bellman-style distillation loop that gains over 900 Elo points.

ChessLFM, an AI model, has been featured on the home page of Lichess, a popular chess platform, where the model's seed data was collected.

A systematic mapping study analyzing chess research across humans, engines, and language models to identify gaps and future directions in strategic reasoning.

A browser extension called ChessInsights AI has been developed for real-time chessboard detection and analysis using 100% client-side computer vision, ensuring privacy by processing all data locally without server uploads.

This paper introduces LLAMIA-Bench and a method called latent state internalization to enhance collaboration between language models and non-language agents, showing that internalizing continuous representations outperforms verbalization and matches frontier models like GPT-5.1.

The paper introduces LLAMIA-Bench, a benchmark for collaborative chess tasks between language models and non-language agents, and proposes latent state internalization to outperform text-based verbalization, with a 14B model matching or exceeding frontier models like GPT-5.1.

Chess.com has launched Gambit, a free online poker site emphasizing learning and social play without real-money gambling, and plans to expand into more classic games.

The article compares blindfold chess skills to effective AI-assisted programming, arguing that pattern recognition and attention control are key for both.

This paper leverages offline reinforcement learning to automatically generate high-quality chess puzzles, enhancing pedagogical value for beginners based on extensive user data from platforms like Chess.com and Lichess.

A demo of chessformer_lens shows that ablating a single attention head in a chess transformer causes it to stop recognizing Morphy's queen sacrifice, demonstrating the concentration of specific capabilities in individual heads.

Otter is a 15.3M-parameter human chess AI that extends Maia 2 by conditioning move predictions on game history and time pressure, achieving higher accuracy than Maia 2 with fewer parameters. Trained on 6.1 billion positions from Lichess games, it demonstrates that treating chess as a time-aware, sequential activity improves prediction of human play.

This paper introduces ACT-Eval, a tool-augmented evaluation framework for LLM chess commentary, and releases a benchmark of 325 position-move pairs. It finds that factual hallucinations remain pervasive in LLM chess commentary, and tool augmentation improves factual correctness but not expert-level strategic coverage.

A reflection on how Deep Blue's 1997 victory over Kasparov did not harm chess, but instead led to increased interest and more professional players.

This video builds a chess simulator and tests the reasoning abilities of multiple LLMs using puzzles and tournaments. Results show Gemini 3.1 wins, and there is a clear gap between open-source models and top closed-source models.

Researchers investigate how reinforcement learning (RL) interacts with pretraining quality, showing that stronger pretrained models benefit more from RL and that RL cannot fully compensate for weak pretraining. A joint scaling law is identified for pretraining and post-training compute allocation.