arXiv AI
1d ago
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.
Hidden Anchors in Multi-Agent LLM Deliberation
arXiv:2606. 19494v1 Announce Type: new Abstract: Multi-agent LLM deliberation, where agents exchange and revise answers over several rounds, is increasingly used to improve reasoning and accuracy, yet how and why it works is rarely modelled.
Related stories
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 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
Jul 3
Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry
arXiv:2607. 01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration.
arXiv AI
Jun 29
Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs
arXiv:2606. 27909v1 Announce Type: cross Abstract: Theory-of-mind evaluations of large language models typically use dyadic social-deduction games, where every observable cue points to a single hidden side, so a model with strong language priors can score well without ever simulating opponents' incentives.
Hugging Face Trending Papers
Jul 2
Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry
Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration. Yet existing approaches overlook a critical design choice: what information each agent receives.
arXiv AI
Jun 8
Don't Make the LLM Read the Graph: Make the Graph Think
arXiv:2604. 23057v2 Announce Type: replace Abstract: We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning.
arXiv AI
Jun 2
On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
arXiv:2603. 12109v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons.
arXiv AI
2d ago
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
arXiv:2608. 14375v1 Announce Type: new Abstract: Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer.
arXiv AI
Jul 14
Auditing Belief-Conditioned LLM Agents in Hidden-Information Social Deduction Games
arXiv:2607. 10814v1 Announce Type: cross Abstract: Evaluating LLM agents in hidden-information multi-agent settings is hard: final outcomes are high-variance and rarely reveal why an agent decided as it did.
arXiv AI
1d ago
The Fragility of Strategic Thinking in Large Language Models
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
arXiv AI
Jul 7
Attributing Emergence in Million-Agent Systems
arXiv:2605. 11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents.