arXiv cs.AI· Tianqiang Yan, Lizhen Qu·· 12 小时前AI 评分31
面向大语言模型驱动智能体的行为分析框架
Behaviour analysis for large language model-driven agents
AI 导读
研究者提出一个通用框架，将数据驱动的行为发现与统计分析和实验验证连接起来，用于理解语言智能体的行为如何沿轨迹组织并与上下文和结果关联。框架通过记录活动定义测量类别，保留行为在过程中的位置并将关系表达为可解释假设，再在留出上下文中检验证据强度与范围，并通过提示词引导等实验测试行为与结果的变化。迭代自我反思研究展示了发现、依赖学习与提示词比较如何相互印证。
正文 · 原文
Abstract:Understanding language-agent behaviour requires learning how actions are organised across a trajectory and how this organisation relates to context and outcomes. We propose a general framework that connects data-driven behavioural discovery with analysis and experimental investigation. Categories developed from recorded activity define what is measured. Analyses preserve the behaviours' positions in the process and express their relationships as interpretable hypotheses. Statistical assessment and tests in held-out contexts examine the strength and scope of the evidence. Experiments, including prompt steering, then test how behaviour and outcomes change under deliberately varied conditions. A study of iterative self-reflection illustrates how discovery, dependency learning and prompt comparisons can inform one another. We develop this approach for trajectories of varying length, event order and agent roles, and identify advances in coding, analysis and experimentation needed to support its wider use.
| Comments: | Under review by Nature Machine Intelligence |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.32087 [cs.AI] |
| (or arXiv:2610.32087v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32087 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianqiang Yan [view email]
[v1]
Fri, 23 Oct 2026 02:48:45 UTC (464 KB)
来源：arXiv cs.AI · arxiv.org