RAPO: Responsibility Attribution-Driven Prompt Optimization for LLM-Based Multi-Agent Systems

LLM-based multi-agent systems show strong potential for complex collaborative tasks, but their performance depends heavily on the design and optimization of role prompts. Existing optimization methods typically rely on label-free feedback and randomly sampled task cases, which can introduce misaligned signals and agent-irrelevant noise, leading to unstable prompt updates. We propose a Responsibility Attribution-driven Prompt Optimization (RAPO) framework for multi-agent systems. Its two key designs—replacing unreliable LLM self-evaluation with gold-answer guidance and constructing agent-specific sample sets via error attribution—jointly enable stable prompt updates with smaller sample budgets. A formal bias–variance analysis justifies their complementary roles. Extensive experiments on code generation, mathematical reasoning, and scientific QA show that our method outperforms the state-of-the-art MASPO, and ablation studies confirm each component’s contribution. Cross-backbone evaluations further demonstrate the stability and generality of the approach.