Researchers propose IF-MAP, an influence-function-inspired method for prioritizing which agent to improve in an LLM-based multi-agent system, arguing that improving every agent can be prohibitively costly. The approach uses failure attribution to identify the agent whose improvement yields the greatest system-level gain under limited resources, though the source notes that an agent's responsibility for a failure alone does not fully determine that choice.
Improving every agent in an LLM-based multi-agent system can be prohibitively costly, making it important to identify which agent should be prioritized to achieve the greatest system-level gain with limited resources. Failure attribution can inform this decision, but an agent's responsibility for fa
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