LLM agents fail Byzantine consensus where classical theory guarantees success
A study by Sribalaji C. Anand and George J. Pappas frames LLM multi-agent agreement as a Byzantine consensus game and finds that prompted LLM agents fail to reach consensus even in settings where classical theory guarantees a convergent algorithm exists.
Score breakdown
Classical resilient consensus filters demonstrably improve LLM agent agreement, showing that formal distributed-systems theory can directly inform the safety design of multi-agent AI systems.
- 01Authors: Sribalaji C. Anand and George J. Pappas
- 02LLM multi-agent agreement is framed as a Byzantine consensus game
- 03Experiments run on complete and general communication graphs
Sribalaji C. Anand and George J. Pappas investigate whether classical resilient consensus theory — originally developed for deterministic agents — can be applied to LLM-based multi-agent systems where agents must coordinate and agree on shared decisions under potentially adversarial behavior. They frame the problem as a Byzantine consensus game and conduct controlled experiments on both complete and general communication graphs.
This failure is not an artifact of specific hyperparameter choices: it persists across varying temperatures and time horizons.
Their core finding is that prompted LLM agents fail to reach agreement even in settings where classical theory guarantees that a convergent algorithm exists. This failure is not an artifact of specific hyperparameter choices: it persists across varying temperatures and time horizons. At the same time, the paper shows that wrapping LLM agents with classical resilient consensus filters measurably improves agreement rates, with the magnitude of improvement depending on how much robustness the underlying communication topology already provides.
The authors conclude that classical resilient consensus theory offers a useful analytical lens for evaluating the safety of agentic AI systems, suggesting that formal tools from distributed systems theory can inform the design and evaluation of multi-agent LLM deployments.
Key facts
- 01Authors: Sribalaji C. Anand and George J. Pappas
- 02LLM multi-agent agreement is framed as a Byzantine consensus game
- 03Experiments run on complete and general communication graphs
- 04Prompted LLM agents fail to reach consensus even where classical theory guarantees a convergent algorithm exists
- 05Consensus failure persists across temperatures and horizons
- 06Wrapping agents with classical resilient consensus filters improves agreement
- 07The benefit of filtering depends on the robustness already provided by the network topology
Topics
Summary and scoring are generated automatically from the original article. We always link back to the publisher and never republish images or paywalled content. Last processed Jun 16, 2026 · 23:11 UTC. How this works →