arXiv AI
Jun 16
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.
Bregman Consensus
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arXiv AI
Jun 6
Benchmarking Emergent Coordination in Large-Scale LLM Populations: An Evaluation Framework on the MoltBook Archive
arXiv:2603. 03555v3 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical.
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
Jun 16
HCP-MAD:Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate
arXiv:2604. 09679v2 Announce Type: replace-cross Abstract: Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and alternating critique cycles.
arXiv Computation and Language
3d ago
Multi-agent discussion gains less when dissent is withheld
arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
arXiv AI
Sep 10
Robust Conformal Consensus: Multi-Agent LLM-as-a-Judge Interval Evaluation with Conformal Prediction
arXiv:2609.06367v1 Announce Type: cross Abstract: LLM-as-a-Judge has emerged as a promising paradigm for evaluating natural language generation. However, the uncertainty associated with such evaluati...
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.
arXiv AI
Aug 18
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
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 Machine Learning
Jun 18
Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
arXiv:2605. 25929v2 Announce Type: replace-cross Abstract: The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collaborate.
arXiv AI
Sep 4
Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge
The paper introduces AgentAuditor, a method that improves multi-agent large language model (LLM) reasoning by structuring agent outputs into a Reasoning Tree that captures agreements and divergences, rather than relying on simple majority voting. AgentAuditor resolves conflicts by comparing evidence at key divergence points, enabling efficient localized verification. The authors also propose Anti-Consensus Preference Optimization (ACPO) to train the adjudicator with evidence-verified supervision, reducing reliance on misleading majority cues. Across four MAS frameworks and multiple reasoning benchmarks, AgentAuditor consistently outperforms majority voting, achieving up to 5% absolute accuracy gains while remaining token‑efficient.
arXiv AI
4d ago
Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems
arXiv:2609.35928v1 Announce Type: cross Abstract: Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantl...