AI & Machine Learning · Computer Science & AI
Multiagent Systems
The newest papers in Multiagent Systems that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
829 papers, from 4 September 2026 to 5 November 2026: 829 arXiv preprints.
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Newest papers, page 28
Human-AI Collaboration: From Paradoxes to Patterns
Evidence shows that humans and AI systems perform better together, by collaborating, than alone.Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning
We study reinforcement learning (RL) methods for cooperative multi-agent Vision-Language-Action (VLA) models.Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions
Most successes of superhuman game-playing algorithms are in games with discrete actions, yet in auctions, robotics, sports, or trading, actions are nearly continuous.GitHarness: Git Init Your Harness Working Memory for Perpetual User Requirements
LLM-based agents increasingly collaborate with users on long-horizon tasks, accumulating evidence, code, and drafts through extensive search, reasoning, and execution.An LLM-powered Agent Framework for Heterogeneous Evacuation Behavior Modeling under a Moving Threat in a Public Plaza
Modeling heterogeneous evacuation behavior under a moving threat is difficult because human perception, memory, and evidence evaluation are not well captured by fixed rules.Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization
Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization.LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective
As large language models (LLMs) evolve from standalone models into collaborative agents embedded in physical systems, their reasoning and execution are increasingly distributed across wireless edge…Adversarially Robust Geometric Safety Certificates for Nonholonomic Robots Against Maneuvering Obstacles
Safe navigation against obstacles that can actively maneuver within bounded capabilities remains challenging: robust control barrier function methods typically treat obstacle actions as generic…FlowMAS: Learning Multi-Agent Workflow Topology via Information-guided Generative Flow Network
Automated multi-agent systems offer clear advantages over manually designed ones in scalability and adaptability, but existing workflow topology methods still face important limitations.PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
Power markets are a natural testbed for multi-agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids.RAVEN: Receiver-Conditioned Action-Value Encoding for Finite-Alphabet Multi-Agent Communication
A message drawn from a small alphabet helps a teammate only if it keeps the distinctions that change that teammate's next decision.Rational Clarification by Assistive Agents via Value-of-Information Reasoning
Users of language-based assistive agents often make ambiguous requests.IMPACT: Modeling Socially Interdependent Movement in a Generative Multi-Agent Simulation of a Pompeian Household
Simulations of archaeological sites can make interpretations of past cultural practices observable and examinable.Multi-Agent Flow Matching with Decoupled Generative Guidance
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions.VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined…Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences.Multi-agent discussion gains less when dissent is withheld
Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable.Absorbing State Phase Transitions in Multi-Agent Search
Nontrivial dynamics can emerge in large language model (LLM)-based multi-agent systems, and preliminary evidence exists that formalisms from statistical mechanics can be effective at modeling and…PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems
Existing architectures for LLM-based multi-agent systems (MAS) cannot reliably and efficiently solve multi-step tasks at scale: they struggle to support large numbers of agents and concurrent tasks…From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs
Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work together on complex problems.