IF-MAP：用影响函数思路给多智能体系统排序优化优先级
IF-MAP: Influence-Function-Inspired Agent Prioritization in Multi-Agent Systems
多智能体系统预算有限时，这篇论文教你怎么算出该先优化哪个 agent，六个工作流实测收益翻倍。
arXiv 论文提出 IF-MAP，一种基于影响函数思路的方法，用于判断多智能体系统中哪个 agent 优先改进收益最大。它通过受控语义提示扰动测量端到端 loss 变化，在低维 prompt 嵌入子空间拟合局部响应模型，以响应范数作为优先级分数，全程黑盒、不需要模型参数或梯度。在涵盖代码生成、问答和数学推理的六个多智能体工作流上评估，按其优先级做 prompt 优化的宏观平均增益为 3.81 个百分点，均匀选择只有 1.85。对五个目标模型做升级时，选定 agent 带来 7.62-16.42 个百分点的宏观平均增益，均匀选择为 3.81-8.62。相比优化所有 agent，IF-MAP 让 Qwen3-8B 和 DeepSeek-V3.2 的 token 消耗分别减少 74.2% 和 35.2%。
IF-MAP: Influence-Function-Inspired Agent Prioritization in Multi-Agent Systems
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 failures does not directly reveal the system-level gains achievable by improving it. We therefore introduce IF-MAP, an influence-function-inspired method for identifying agents with high potential for system-level improvement. For each agent, IF-MAP measures end-to-end loss changes under controlled semantic prompt perturbations, fits a regularized local response model in a low-dimensional prompt-embedding subspace, and uses the estimated response norm as a priority score. This black-box procedure requires no access to model parameters or gradients and produces agent priorities that can guide subsequent improvement effort. We evaluate IF-MAP across six multi-agent workflows spanning code generation, question answering, and mathematical reasoning, using the same priorities for prompt optimization and model upgrades. Optimizing the selected agents' prompts achieves a macro-average gain of 3.81 percentage points, compared with 1.85 for uniform agent selection. Across five target models, upgrading the selected agents yields macro-average gains of 7.62-16.42 percentage points, compared with 3.81-8.62 for uniform agent selection. Despite its probing overhead, IF-MAP reduces Qwen3-8B and DeepSeek-V3.2 token use by 74.2% and 35.2%, respectively, relative to optimizing every agent, and reduces target-model tokens by 63.6-64.9% relative to upgrading every agent. These results support the use of IF-MAP to identify promising agents and direct improvement effort toward greater end-to-end performance gains.
