arXiv:2606.15024v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, transfers to LLM agents that may behave adversarially. Framing LLM agreement as a Byzantine consensus game, we run controlled experiments on complete and general communication graphs. We find that prompted LLM agents fail to reach agreement that is achievable in principle: consensus can fail even in settings where clas
The proliferation of LLM agents in multi-agent systems necessitates understanding their reliability and limitations in complex coordination tasks, particularly concerning resilient consensus.
This research highlights fundamental challenges in deploying autonomous AI agents that require consensus, revealing that current LLMs may fail foundational agreement protocols even in simple settings.
The assumption that classical resilient consensus theory directly transfers to LLM agents is challenged, implying a need for new frameworks or significant advancements in LLM robustness for multi-agent systems.
- · AI safety researchers
- · Developers of robust multi-agent coordination frameworks
- · Academic institutions studying AI reliability
- · Companies deploying unverified LLM multi-agent systems
- · Early adopters of critical LLM-based autonomous systems
- · Developers neglecting agent reliability and consensus mechanisms
This research suggests that unmitigated LLM agents are not reliable for critical consensus-based tasks in multi-agent systems.
It will likely drive increased investment into research on LLM robustness, adversarial AI, and new consensus protocols tailored for 'agentic AI'.
The findings could delay the deployment of fully autonomous AI systems in sensitive sectors or lead to a requirement for human-in-the-loop oversight in scenarios requiring high-stakes agreement.
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Read at arXiv cs.AI