arXiv cs.AI· Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye·· 2 天前AI 评分44
递归推理还是统计外推？多智能体相互依赖决策中的上下文学习
Recursive Reasoning or Statistical Extrapolation? In-Context Learning in Multi-Agent Interdependent Decision-Making
AI 导读
研究通过公共品博弈操纵历史反馈的统计结构，发现历史统计模式被破坏后，更长上下文带来的收益基本消失，决策质量退化至无上下文基线，且战略相互依赖越强退化越明显。结果表明此类战略环境中的上下文学习更符合统计外推而非战略推理。研究提出以理性预期均衡（REE）作为区分推理与外推的诊断工具。
正文 · 原文
Abstract:In-context learning (ICL) enables large language model (LLM) agents to improve decisions using interaction history, yet it remains unclear whether such improvement reflects refined internal reasoning or mere extrapolation of statistical patterns. To disentangle these mechanisms, we study LLM agents in multi-agent incomplete-information games that require recursive belief reasoning. By constructing a public goods game and manipulating the statistical structure of historical feedback, we evaluate decision quality against a history-independent rational expectations equilibrium (REE) benchmark. Our experiments reveal that when historical statistical patterns are disrupted, the benefits of longer context largely vanish, degrading decision quality to the no-context baseline in a way sharply amplified by stronger strategic interdependence. These results suggest that, in such strategic environments, ICL behavior is more consistent with statistical extrapolation than with strategic reasoning. Our work extends the mechanistic study of ICL to strategic multi-agent settings, introduces REE as a diagnostic tool for distinguishing reasoning from extrapolation, and provides a reusable framework for probing the boundaries of LLM reasoning in recursive belief tasks.
| Comments: | Findings of the Association for Computational Linguistics: EMNLP 2026 |
| Subjects: | Artificial Intelligence (cs.AI); General Economics (econ.GN) |
| Cite as: | arXiv:2609.18591 [cs.AI] |
| (or arXiv:2609.18591v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.18591 arXiv-issued DOI via DataCite |
Submission history
From: Yu Liu [view email]
[v1]
Wed, 16 Sep 2026 12:49:19 UTC (9,400 KB)
[v2]
Sat, 24 Oct 2026 07:42:51 UTC (9,400 KB)
来源：arXiv cs.AI · arxiv.org