arXiv cs.MA· Fabian Tritthart, Tom Zehle·· 13 小时前AI 评分29
CATS：面向多智能体系统的对比归因拓扑搜索
Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
AI 导读
研究者提出对比归因拓扑搜索（CATS），在固定智能体超图中搜索紧凑团队，由 LLM 归因器对比执行轨迹为单个智能体分配信用，并据此更新超图边、逐步剪枝弱边与弱智能体。
正文 · 原文
Abstract:An LLM multi-agent system (MAS) is only as good as the agents it chooses to trust: its accuracy and cost hinge on its topology, meaning which agents are included and how they communicate. A single task score, however, does not reveal which agents help or hinder performance. We propose Contrastive Attribution Topology Search (CATS), which searches for compact teams within a fixed supergraph of specialized agents. An LLM attributer compares execution traces across candidate topologies and assigns credit to individual agents. CATS uses these credits to update supergraph edges while progressively pruning weak edges and agents, yielding a small team for deployment. With small open-weight models and matched candidate evaluations, CATS improves mean accuracy on BigCodeBench by 5.3 percentage points over score-only heuristic search while using 54\% fewer deployment tokens. On MBPP, CATS matches the strongest baselines with fewer tokens than the other searches. In a HotpotQA prompt-poisoning stress test, CATS excludes all adversarially prompted agents in all three runs, whereas random search retains some at comparable accuracy. Per-agent credit thus supports both team optimization and the pruning of potentially harmful agents.
| Subjects: | Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.29292 [cs.CL] |
| (or arXiv:2610.29292v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29292 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tom Zehle [view email]
[v1]
Wed, 21 Oct 2026 06:46:11 UTC (957 KB)
来源：arXiv cs.MA · arxiv.org