arXiv AI
Jun 30
Robust Multi-Agent LLMs under Byzantine Faults
arXiv:2605. 09076v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability.
Resilient Consensus in Agentic AI
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.
Related stories
arXiv AI
Aug 10
Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
arXiv:2605. 19035v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution.
arXiv Machine Learning
Jul 7
Social Networks of LLM Agents
arXiv:2607. 03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively.
arXiv AI
Aug 6
Is Monitoring Enough? Strategic Agent Selection For Stealthy Attack in Multi-Agent Discussions
arXiv:2603. 21194v2 Announce Type: replace-cross Abstract: Multi-agent discussions have been widely adopted, motivating growing efforts to develop attacks that expose their vulnerabilities.
arXiv Machine Learning
Jul 15
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
arXiv:2510. 20963v2 Announce Type: replace Abstract: Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs.
arXiv AI
Jun 29
Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
arXiv:2505. 23847v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership.
arXiv AI
Jun 4
SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
arXiv:2606. 04202v1 Announce Type: new Abstract: As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation.
arXiv AI
Jul 7
Seven Security Challenges in Cross-domain Multi-agent LLM Systems
arXiv:2505. 23847v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership.
arXiv AI
Jul 9
Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
arXiv:2607. 07368v1 Announce Type: cross Abstract: AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent.
arXiv Machine Learning
Jun 9
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
arXiv:2606. 07790v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints remains poorly understood.
arXiv AI
Jul 17
Provable Coordination for LLM Agents via Message Sequence Charts
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv:2606. 29026v1 Announce Type: new Abstract: 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.