arXiv：cs.AI· Hanbo Xie, Yue Zhao, Robert C. Wilson·· 21 天前
超越成功博弈：诊断大语言模型的对手建模能力
Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models
AI 导读
研究团队提出一套重复捉迷藏基准与诊断框架，用具有显式递归信念更新的算法对手来区分自适应博弈、动作预测与信念追踪。在 Qwen2.5-7B-Instruct 上，告知对手类型和参数设置能提升预测准确率，但仅靠历史推断对手时表现下降；GPT-5.4 同样受益于正确的对手信念，而更深入的显式信念报告与自生成记忆未能稳定提升预测能力。
正文 · 原文
Abstract:Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral patterns rather than theory of mind (ToM): the ability to infer others' mental states, including beliefs and intentions. We introduce a benchmark and diagnostic framework for repeated hide-and-seek against algorithmic opponents with explicitly defined recursive belief updates. Their ground-truth beliefs and action probabilities let us distinguish adaptive play, action prediction, and belief tracking through live interaction, fixed-history evaluation, and controlled information interventions. With structured elicitation and behavioral supervised fine-tuning (SFT), we find improved play in several tested settings, but diagnostics reveal limitations in opponent prediction and belief tracking. We test whether supervising opponent action probabilities can address this predictive deficit in Qwen2.5-7B-Instruct. Predictions improve when the model is told the opponent's type and parameter settings, but become less accurate when it must infer the opponent from history without this information. Training on examples that also omit these details reduces the gap, although improvements vary across opponents. Is this limitation specific to smaller open-weight models, or does it also appear in a frontier model? GPT-5.4 also benefits from correct opponent beliefs: supplying them improves its predictions and choices, whereas deeper explicit belief reports and self-generated memory do not consistently improve forecasting from interaction history. This contrast suggests that using useful belief information is easier than inferring it autonomously, even for a frontier model. Together, our findings distinguish improved play and action prediction from reliable belief tracking, providing a framework for evaluating mentalizing and testing targeted improvements.
| Subjects: | Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2610.17310 [cs.AI] |
| (or arXiv:2610.17310v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17310 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hanbo Xie [view email]
[v1]
Mon, 12 Oct 2026 19:08:43 UTC (4,098 KB)
来源：arXiv：cs.AI · arxiv.org