Add Tang (Maia 2) paper

Author: cakiki

lichess.bib

@@ -1474,6 +1474,23 @@ @inproceedings{stockl:2021:watching-language-model-learning-chess
   abstract      = {We analyse how a transformer-based language model learns the rules of chess from text data of recorded games. We show how it is possible to investigate how the model capacity and the available number of training data influence the learning success of a language model with the help of chess-specific metrics. With these metrics, we show that more games used for training in the studied range offers significantly better results for the same training time. However, model size does not show such a clear influence. It is also interesting to observe that the usual evaluation metrics for language models, predictive accuracy and perplexity, give no indication of this here. Further examination of trained models reveals how they store information about board state in the activations of neuron groups, and how the overall sequence of previous moves influences the newly-generated moves.},
 }
 
+@inproceedings{tang:2024:maia-2-unified-model-human-ai-alignment-chess,
+  title         = {Maia-2: a unified model for human-AI alignment in chess},
+  author        = {Tang, Zhenwei and Jiao, Difan and McIlroy-Young, Reid and Kleinberg, Jon and Sen, Siddhartha and Anderson, Ashton},
+  year          = {2025},
+  booktitle     = {Proceedings of the 38th International Conference on Neural Information Processing Systems},
+  location      = {Vancouver, BC, Canada},
+  publisher     = {Curran Associates Inc.},
+  address       = {Red Hook, NY, USA},
+  series        = {NeurIPS '24},
+  isbn          = {9798331314385},
+  abstract      = {There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools. Our implementation is available https://github.com/CSSLab/maia2.},
+  articleno     = {659},
+  numpages      = {26},
+  github        = {https://github.com/CSSLab/maia2},
+  keywords      = {Human-AI Alignment, Action Prediction, Chess, Skill-aware Attention},
+}
+
 @misc{tang:2025:is-elo-rating-reliable-study-under-model-misspecification,
   title         = {Is Elo Rating Reliable? A Study Under Model Misspecification},
   author        = {Shange Tang and Yuanhao Wang and Chi Jin},