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.
Get the few papers worth your time in Multiagent Systems every morning.
Follow Multiagent Systems and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 17
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordinating complementary capabilities.Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes
Large language model (LLM)-based multi-agent systems (MAS) are commonly organized around two coordination regimes.ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning
Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference.Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made
Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world.Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables
Inside organizations, a question's data often sit with several owners, who query their own tables but never hand them over.When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability
Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent.Strategic Multi-Agent Resource Allocation via Statistical Contracts
We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources among competing agents who possess data or data-generating mechanisms that may be relevant…Choosing LLMs for Strategic Agent Interactions
Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions.CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning
Cooperative aerial manipulation is difficult under wind, payload motion, and noisy goal broadcasts because multiple Unmanned Aerial Vehicles (UAVs) are coupled through a shared payload.On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems
Understanding agent heterogeneity can inform the design of shared and agent-specific policy representations, however existing criteria provide no unified structural test.PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning
A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence.Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives.CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world.Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams
Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences.CLASHarness: Benchmarking Multi-Agent Conflict in Computer Use via a Controllable and Verifiable Generation Pipeline
Vision-language models (VLMs) are increasingly deployed in multi-agent systems to automate complex computer-use workflows, yet existing benchmarks mostly assume consistent, cooperative environments.Narrowing the Collaboration Gap, Probably
Large language models are increasingly deployed as teams of agents that hold different private information about a shared task.Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again.Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems
Reliable deployment of LLM-based multi-agent systems calls for runtime auditors that predict terminal outcomes from observed execution prefixes.GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines
Maintaining up-to-date clinical practice guidelines (CPGs) is essential for evidence-based medicine, but rapid evidence growth and complex expert consensus formation limit timely updates.Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues
LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary expertise.