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cs.AI2026
DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems
Zehao Wang, Lanjun Wang, Shilong Jin +2
Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile, frequently exhibiting reason…
cs.AI2026
When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Zehao Wang, Shilong Jin, Zhao Cao +1
LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon w…
cs.AI2025
Key Decision-Makers in Multi-Agent Debates: Who Holds the Power?
Qian Zhang, Yan Zheng, Jinyi Liu +2
Recent studies on LLM agent scaling have highlighted the potential of Multi-Agent Debate (MAD) to enhance reasoning abilities. However, the critical aspect of role allocation strat…