Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling
The paper introduces SIGMA, a new framework for multi-agent reasoning that addresses the limitations of existing systems. It focuses on modeling trust and conflict among agents using a signed relational graph. Experimental results show that SIGMA outperforms current state-of-the-art methods in accuracy and conflict resilience.
- ▪SIGMA captures trust, conflict, and neutral relations among agents via a signed relational graph.
- ▪The framework uses conflict-aware signed message passing to enhance reliable information while minimizing conflicting signals.
- ▪Extensive experiments demonstrate SIGMA's superior performance across multiple datasets and configurations.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19418 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.19418 (cs) [Submitted on 19 May 2026] Title:Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling Authors:Longgang He, Longzhu He, Daojing He, Chaozhuo Li View a PDF of the paper titled Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling, by Longgang He and 3 other authors View PDF HTML (experimental) 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.
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