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 25
Formalizing the Moral Evaluation of Speech Acts: Truthfulness, Lies and Ethical Dilemmas
In life-or-death situations, a benevolent lie may appear more moral than telling the truth.When Robots Crash: Optimal Asynchronous Gathering at Weber Meeting Nodes
We study the \textit{optimal gathering} problem over a finite set of designated \textit{meeting nodes} for \textit{asynchronous, anonymous,} and \textit{oblivious} mobile robots on an infinite grid…WebUIProof: Benchmarking WebUI Code Generators with UI-Agent Execution Harness
Evaluating WebUI code generation at scale is difficult: outputs may compile and look plausible yet fail under user interaction, and prior benchmarks largely rely on free-form prompts with static…Performance of Zero-determinant Strategies in Repeated Games without Discounting
Zero-determinant (ZD) strategies are a class of strategies in repeated games, which unilaterally control payoffs.Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents
Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it.Who Went Where When on the Lunar Surface: Forensic Trajectory Analysis to Identify Byzantine Rovers
Future planetary surface missions are likely to involve multiple independently operated rovers sharing the same deployment region, raising the need to verify compliance with operational constraints…Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premise
Multi-agent debate is increasingly used to reach consensus among LLM agents, yet agents often yield to a unanimous majority.Turnover-Orthogonal Credit Assignment for Open-Team Multi-Agent Reinforcement Learning
Open-team multi-agent reinforcement learning studies cooperative systems in which agents may join, leave, or be replaced during an episode.Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies
Transformer policies are attractive for multi-agent robot learning because self-attention can model interactions among agents.Multi-Agent AI as a Nested Principal-Agent Problem in Private Wealth Management: Mandate Representation and Evidence Control in Switzerland, Germany and Austria
In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's agent and the system's principal.SceneFactory-3D: Lifting 2D Traffic Scenes into 3D Physical Counterfactuals for Scalable Physically Grounded Safety Evaluation
Scalable driving simulators typically execute vehicle commands using prescribed behavioral or kinematic rules, overlooking the physics of tire-road interfaces, thereby limiting their ability to…Evaluator-in-the-Loop Monte Carlo Tree Search via LLM Agents for Motif Scaffolding in Protein Design
Motif-scaffolding systems commonly follow a generate-then-filter paradigm, in which candidate proteins are generated independently and structural evaluation is used primarily for terminal screening…Dynamic Expert Pruning for Multi-Agent Systems
Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to computation: every expert must stay resident on the…Engineering Sustainable Agents: A Systematic Comparison of Agentic LLMs for Developer Workflows
Large language models (LLMs) are increasingly used in software engineering, including agentic systems that coordinate multiple agents, but impose higher computational and environmental costs.FinNextAssist: Towards Professional Financial Deep Research Assistant
Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and structured reporting.Defense-in-Depth at the Perception-Reasoning Interface of LLM-Centric Agentic UAV Swarms
Large Language Models (LLMs) increasingly support Uncrewed Aerial Vehicle (UAV) swarm operations such as data collection scheduling, where the model reads structured sensor reports and decides which…EdgeAgent: Orchestrating On-Device LLM inference for End-User Multi-Agent Systems on CPU-GPU Unified Memory Architectures
Emerging multi-agent LLMs demand privacy-preserving edge deployment, yet current inference systems struggle with these collaborative workflows.Resilient Multi-Agent Target Localization and Tracking via BAG-Aware Mutual Information Maximization under False Data Injection Attacks
Cooperative multi-agent networks deployed for target localization and tracking remain critically vulnerable to malicious cyberattacks, since a single compromised agent can corrupt the centralized…$Q$ Can Play That Game: Online Fitted $Q$-Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation
Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties.HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols
Large language models (LLMs) have recently been applied in systems research as a tool to reduce human-intensive engineering effort through cost-efficient automation.