Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision. However, such communication may also introduce reliability risks: reasoning from one agent can correct another agent's mistake, but it can also mislead an agent that was initially correct. This paper studies reliable multi-agent AI communication through reasoning exchange and runtime answer revision. We develop a framework in which agents first answer multiple-choice questions independently, then share reasoning traces and revise their decisions. We conduct numerical experiments where we evaluate whether this proc...
| Authors | Shahnewaz Karim Sakib, Anindya Bijoy Das |
|---|---|
| Primary category | cs.AI |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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