Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
We propose Contrastive Attribution Topology Search (CATS), which searches for compact teams within a fixed supergraph of specialized agents.
ProofPaper ↗
Key points
- 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.
- An LLM attributer compares execution traces across candidate topologies and assigns credit to individual agents.
- 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.
- Per-agent credit thus supports both team optimization and the pruning of potentially harmful agents.
Sources (1)
- [1]Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 06:46 AM
We propose Contrastive Attribution Topology Search (CATS), which searches for compact teams within a fixed supergraph of specialized agents.
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.
Extractive summary: sentences quoted from the sources.
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