arXiv cs.CL· Haocheng Jiang, Herun Wan, Yongyuan Peng, Minnan Luo·· 12 小时前AI 评分31
RIVER：量化多智能体 LLM 交互中的声明级信息动态
Beyond Final Outcomes: Quantifying Claim-Level Information Dynamics in Multi-Agent LLM Interaction
AI 导读
研究者提出 RIVER 框架，将智能体输出拆解为原子声明，从收敛、探索、保留三个维度评估多智能体 LLM 交互中的信息动态。在 40 个受控新闻生成案例上测试五种交互策略发现，反复池化式交互提升收敛但收窄信息空间，分布式接力则带来更广但共享更少的信息。方法特定约束会提升探索，却削弱收敛与保留。
正文 · 原文
Abstract:When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task outcomes. We study such interaction as claim-level information dynamics and propose RIVER, which decomposes agent outputs into atomic claims and evaluates three complementary dimensions: convergence, exploration, and retention. Convergence captures cross-agent claim correspondence and stance alignment; exploration captures semantic breadth and extension beyond the source; and retention tracks source preservation and the persistence of new claims across stages and lineages. We evaluate five interaction strategies on 40 controlled news-generation cases. Repeatedly pooled interaction promotes convergence but narrows the information space, whereas distributed relay yields broader, less shared information. Further comparisons show that method-specific constraints increase exploration while weakening convergence and retention. Our findings suggest that collective information dynamics are better explained by underlying information-flow mechanisms than by simple downstream performance.
| Comments: | 23 pages, 3 figures. Code and data: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.32333 [cs.CL] |
| (or arXiv:2610.32333v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32333 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haocheng Jiang [view email]
[v1]
Fri, 23 Oct 2026 08:28:47 UTC (328 KB)
来源：arXiv cs.CL · arxiv.org