Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling

2026-05-19

AI Summary

Existing large language model (LLM)-driven multi-agent systems often lack explicit modeling of conflicting relationships during aggregation, leading to error propagation and unreliable reasoning. To address this limitation, this work proposes SIGMA, a novel framework that introduces signed graphs into multi-agent reasoning for the first time. SIGMA explicitly captures trust, conflict, and neutral relationships among agents by constructing a signed relational graph and incorporates a conflict-aware message-passing mechanism alongside a structure- and conflict-aware weighted aggregation strategy. Extensive experiments demonstrate that SIGMA significantly outperforms current state-of-the-art methods across six benchmark datasets, achieving higher accuracy and greater robustness against conflicting information under diverse LLM backbones and multi-agent configurations.

Abstract

LLM-based multi-agent systems (MAS) have demonstrated strong reasoning and decision-making capabilities that consistently surpass those of single LLM agents. However, their performance often suffers from naive aggregation mechanisms that assume uniformly cooperative interactions. Upon close inspection, we observe that existing graph-based MAS frameworks (1) propagate errors when conflicting signals arise without control, and (2) lack explicit modeling of conflicting inter-agent relations as well as structural awareness, failing to identify reliable interaction patterns. To bridge this gap, we introduce SIGMA, a novel SIgned Graph-informed Multi-Agent reasoning framework that explicitly captures trust, conflict, and neutral relations among agents via a signed relational graph. Specifically, given a query, SIGMA first selects a set of relevant and diverse agents, then constructs a structured signed interaction graph with confidence-weighted edges. Reasoning proceeds through conflict-aware signed message passing, which reinforces information from trustworthy agents while suppressing conflicting signals, and terminates with a structure- and conflict-aware weighted aggregation to yield globally consistent and conflict-resilient predictions. Extensive experiments on six benchmark datasets, across multiple LLM backbones and diverse multi-agent configurations, demonstrate that SIGMA consistently outperforms state-of-the-art baselines, achieving notable gains in both accuracy and conflict-resilient performance.

Problem

• multi-agent systems
• conflict modeling
• signed graphs
• trust relations
• reasoning aggregation

Innovation

• Signed Graph
• Multi-Agent Reasoning
• Conflict-Resilient
• LLM-based MAS
• Trust Modeling