GraphPI: Graph-based Peer Integration for Multi-Agent Collaboration

Multi-agent collaboration has emerged as a promising approach to improving LLM reasoning by enabling agents to exchange information and iteratively refine their solutions. However, peer information is often incorporated directly into a receiver's context, implicitly shaping its subsequent judgments. Such influence is difficult to attribute and control, potentially allowing misleading peer information to shift individual judgments and reinforce erroneous consensus through repeated interaction. To address this issue, we propose Graph-based Peer Integration (GraphPI), which preserves each receiver's reasoning state as a graph, making its dependencies and information gaps (e.g., missing supporting evidence for an agent's reasoning) explicit. It then integrates peer messages by establishing explicit cross-graph relations with the receiver's preserved state, grounding peer influence in the receiver's existing reasoning. The resulting graph then guides selective retrieval of complementary peer information to address remaining gaps before subsequent reasoning. Experiments on multi-agent collaboration benchmarks show that GraphPI consistently improves collaborative reasoning while reducing susceptibility to misleading peer information. Our code is available at https://anonymous.4open.science/r/GrapPI_review/.