Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attribution 事件

Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attribution arXiv:2605.27621v1 Announce Type: cross Abstract: As multi-agent systems (MAS) become increasingly complex, identifying the contributions of individual agents is critical for system optimization. However, existing approaches lack a rigorous, unified framework for credit assignment. In this work, we formalize agent attribution as a cooperative game, parameterized by the coalition distribution, removal protocol, and tar