RIGOR: Risk-Gated Topology Adaptation for Robust LLM Multi-Agent Reasoning

LLM-based multi-agent systems (MAS) improve complex reasoning through collaborative decomposition, verification, and synthesis, but their performance can degrade sharply when compromised agents inject misleading intermediate messages. Existing topology methods rely on fixed communication patterns or query-adaptive graphs, but rarely infer or intervene runtime reliability risk, leaving them fragile when compromised agents vary across queries. We study stochastic adversarial topology adaptation, where compromised agents shift across graph positions and rare cascades dominate the high-loss tail. To address this setting, we propose RIGOR, a topology-adaptation framework that couples runtime risk inference with tail-aware training. RIGOR combines Risk-Gated Topology Construction (R-GTC), which probes agent behavior, estimates per-agent risk, and edits the graph through soft quarantine and edge masking, with CVaR-Guided Robust Optimization (C-GRO), which trains the editor on high-loss episodes rather than average gain alone. Across six benchmarks and two backbones, RIGOR consistently outperforms existing MAS baselines under query-varying misleading-message attacks, achieving an average 21.09\% accuracy improvement over the strongest baseline on Qwen3.5-35B-A3B. Tail-subset and position-wise analyses further show that these gains stem from improved robustness against worst-case cascades and dynamically changing compromised-agent positions. The code is available for anonymous access at https://anonymous.4open.science/r/RIGOR-0D7D.