Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
Quick Answer
This study presents a framework for multi-agent AI systems to enhance decision-making by sharing reasoning traces and revising answers, while addressing the risks of error propagation.
Quick Take
This study presents a framework for AI systems to enhance decision-making by sharing reasoning traces and revising answers, while addressing the risks of error propagation. Numerical experiments across domains like cybersecurity and networking show improved accuracy and reliability in decision-making processes.
Key Points
- Agents independently answer questions and then share reasoning for revisions.
- Numerical experiments show improved accuracy across various domains.
- The framework helps identify when multi-agent reasoning enhances reliability.
- Error propagation risks are highlighted in the communication process.
- Results indicate a balance between correcting mistakes and introducing new errors.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 process improves accuracy, produces more positive than negative answer transitions, and remains effective across domains such as cybersecurity, networking, and general knowledge. The results help identify when multi-agent reasoning improves reliability and when it may propagate errors.
| Subjects: | Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2606.29026 [cs.AI] |
| (or arXiv:2606.29026v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29026 arXiv-issued DOI via DataCite |
Submission history
From: Anindya Bijoy Das [view email]
[v1]
Sat, 27 Jun 2026 17:44:24 UTC (3,490 KB)
— Originally published at arxiv.org
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