mlsanityData to 7 Oct 2026

IBGP: Imperfect Byzantine Generals Problem for Zero-Shot Robustness in Communicative Multi-Agent Systems

arXiv 2410.16237 ↗First posted 21 Oct 2024cs.MAcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As large language model (LLM) agents increasingly integrate into our infrastructure, their robust coordination and message synchronization become vital. The Byzantine Generals Problem (BGP) is a critical model for constructing resilient multi-agent systems (MAS) under adversarial attacks. It describes a scenario where malicious agents with unknown identities exist in the system-situations that, in our context, could result from LLM agents' hallucinations or external attacks. In BGP, the objective of the entire system is to reach a consensus on the action to be taken. Traditional BGP requires global consensus among all agents; however, in practical scenarios, global consensus is not always necessary and can even be inefficient. Therefore, there is a pressing need to explore a refined version of BGP that aligns with the local coordination patterns observed in MAS. We refer to this refined version as Imperfect BGP (IBGP) in our research, aiming to address this discrepancy. To tackle this issue, we propose a framework that leverages consensus protocols within general MAS settings, providing provable resilience against communication attacks and adaptability to changing environments, as validated by empirical results. Additionally, we present a case study in a sensor network environment to illustrate the practical application of our protocol.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 7 of its 26 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

7 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 6

  1. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 372
  2. 2019Deep Coordination Graphscited by 96
  3. 2019Learning Nearly Decomposable Value Functions Via Communication Minimizationcited by 85
  4. 2020Incorporating Pragmatic Reasoning Communication into Emergent Languagecited by 15
  5. 2023Robust Communicative Multi-Agent Reinforcement Learning with Active Defensecited by 4
  6. 2024Animating the Past: Reconstruct Trilobite via Video Generationcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Resilient Consensus in Agentic AI
  2. 2026Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
  3. 2025Byzantine-Robust Decentralized Coordination of LLM Agents
  4. 2026Robust Multi-Agent LLMs under Byzantine Faults
  5. 2025Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance
  6. 2026Can AI Agents Agree?
  7. 2026ResMAS: Resilience Optimization in LLM-based Multi-agent Systems
  8. 2025G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems