# Robust Multi-Agent LLMs under Byzantine Faults

arXiv: https://arxiv.org/abs/2605.09076 · HTML: https://mlsanity.com/paper/2605.09076/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 9 May 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 22 of 22)

- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2304.01933/) (2023)
- [Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2410.02506/) (2024)
- [Evil Geniuses: Delving into the Safety of LLM-based Agents](https://mlsanity.com/paper/2311.11855/) (2023)
- [NetSafe: Exploring the Topological Safety of Multi-agent Networks](https://mlsanity.com/paper/2410.15686/) (2024)
- [Distributed Optimization Under Adversarial Nodes](https://mlsanity.com/paper/1606.08939/) (2016)
- [Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance](https://mlsanity.com/paper/2511.10400/) (2025)
- [Byzantine-Robust Decentralized Coordination of LLM Agents](https://mlsanity.com/paper/2507.14928/) (2025)
- [A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network](https://mlsanity.com/paper/2505.05103/) (2025)
- [Distributed Resilience-Aware Control in Multi-Robot Networks](https://mlsanity.com/paper/2504.03120/) (2025)
- [Determining r- and (r,s)-Robustness of Digraphs Using Mixed Integer Linear Programming](https://mlsanity.com/paper/1901.11000/) (2019)
- [Minimal Construction of Graphs with Maximum Robustness](https://mlsanity.com/paper/2507.00415/) (2025)

## Built on by (most cited first, 10 of 10)

- [SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems](https://mlsanity.com/paper/2609.00595/) (2026)
- [Certifiable Semantic Agreement Among LLM Agents: What the Admissibility Instrument Decides](https://mlsanity.com/paper/2606.07316/) (2026)
- [PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations](https://mlsanity.com/paper/2609.07910/) (2026)
- [Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges](https://mlsanity.com/paper/2609.23894/) (2026)
- [Fully Byzantine-Resilient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.25701/) (2026)
- [MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks](https://mlsanity.com/paper/2609.29015/) (2026)
- [Reinforcement Learning of Communication in a Mesh of Small Language Models](https://mlsanity.com/paper/2609.30578/) (2026)
- [Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs](https://mlsanity.com/paper/2609.32259/) (2026)
- [Re-derivability Decides What a Staged Agent Pipeline Recovers After an Upstream Fault](https://mlsanity.com/paper/2609.32802/) (2026)
- [PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems](https://mlsanity.com/paper/2609.38482/) (2026)

## Abstract (arXiv)

Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability. However, these same interactions can also introduce vulnerability to unreliable or Byzantine agents that can propagate incorrect information and degrade overall system performance. To address this, we propose Self-Anchored Consensus (SAC), a fully decentralized filter-and-refine protocol in which agents iteratively exchange responses, locally evaluate and filter unreliable messages, and refine their own outputs. We present (F+1)-robustness conditions on the communication graph that ensure honest agents preserve and propagate reliable information despite Byzantine influence. Experiments across diverse open- and closed-weight LLMs on mathematical and commonsense reasoning benchmarks show that SAC effectively suppresses Byzantine influence and consistently improves performance across diverse communication topologies, whereas prior methods degrade significantly under Byzantine attacks.