arXiv Machine Learning
Amadeus: When Models of People Meet
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Play Like Champions: Counterfactual Feedback Generation in Latent Space
arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.
arXiv AI
Sep 17
What Counts as Strategic Reasoning? A Systematic Mapping of Chess Research on Humans, Engines, and Language Models
The paper presents a systematic mapping of recent chess research involving humans, engines, neural and reinforcement‑learning systems, large language models (LLMs), and hybrid approaches. It identifies 84 core study families and classifies them by agent type, strategic‑reasoning stages, and evaluation dimensions, highlighting a strong focus on situation assessment, evaluation, and action selection while noting gaps in planning, explanation, metacognition, and human–AI collaboration. The study also distinguishes hybrid systems by integration timing and cautions that improved human performance in evaluations does not automatically imply human–AI synergy.
arXiv AI
Sep 12
The Convention Gap: Towards Measuring Implicit Communication in Cooperative AI Evaluation
The paper introduces the "convention gap" as a metric for measuring implicit communication in cooperative AI, defined as the difference between predicted failure probability from literal messages and observed failure rates. Using the card game Hanabi, the authors analyze 101,000 play actions from human-human, AI-AI, and human-AI datasets, finding a +26.2pp gap in human pairs, a -0.7pp gap in AI pairs, and a +16.4pp gap in human-AI pairs, with the largest gaps occurring on plays with no hints. The study shows that convention compatibility, rather than raw AI-AI performance, may better predict an AI’s effectiveness with human partners.
arXiv AI
Aug 11
Social Gym and SPaRTan: Benchmarking and Improving LLM Social Reasoning via Multi-Agent Game Tournaments
arXiv:2608. 09128v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in multi-agent social settings where they must cooperate, negotiate, and adapt to other agents.
arXiv AI
2d ago
High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination
arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...
arXiv AI
2d ago
Aligned but Not Partner-Specific: How Multimodal LLM Agents Succeed in Reference Games Without Forming Conceptual Pacts
arXiv:2606.08081v2 Announce Type: replace-cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific expressions grou...