Targeted reflection improves multi-agent systems by locating decisive errors

DoCtOR attributes a failed trajectory to a specific agent and step, generates a counterfactual correction, and asks only that agent to reflect.

Chinese Tech
Xiaoqing Wang · Keman Huang · Bin Liang · Hongyu Li · Xiaoyong Du · Wuqiong Pan

Renmin University of China · Ant Group

Research Digest··2 min read

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Multi-agent systems improve 22-27% by identifying and reflecting only the agent that caused the failure.

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Multi-agent systems (MAS) powered by LLMs frequently fail, and current reflection methods force all agents to reflect on failures, which contaminates the memory of agents that behaved correctly. This paper introduces DoCtOR, a framework that first automatically attributes the failure to the decisive error agent (the one that led the task astray) using a process reward-based method (ProFA). It then generates a corrected decisive error step via and has only that agent reflect. Experiments on HotPotQA, ChartQAPro, and Mind2Web show DoCtOR improves success rates by 22%, 26%, and 27% respectively, outperforming existing methods like Reflexion and COPPER. This approach is interesting because it addresses a fundamental oversight in MAS self-reflection, potentially making more reliable.

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