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 29
Recursive Organization Improvement: A Modeling Specification for Human--Agent Organizations
Stronger AI agents do not automatically produce better organizations: teams must also learn which work arrangements to retain and when to reconsider them.Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation
Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent terminology and style.CollabFlow: Recursive Self-Improvement of Agent Collaboration
Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-agent systems, Agents refine one another within a task, and outcomes improve how they collaborate…ReplayLens: Auditing Agents' Use of Outcomes
When an agent reuses logged experience, a changed decision may reflect the recorded score, the action's name, or the record's position in storage.Same Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures
Evaluating how LLM agents recover from mid-task failures is central to deploying reliable agentic systems.Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication
Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should encode, what compression costs over a…MASTraceBench: Diagnosing Collaboration Gains through Proposal Trajectories in LLM-Based Multi-Agent Systems
LLM-based multi-agent systems (MAS) have shown promise in complex problem solving.CEO Arena: Evaluating Long-Horizon Multi-Agent Decision-Making in Competitive Markets
Long-horizon competition tests agents' ability to coordinate business decisions under uncertainty and adapt to changing rival strategies.Strategically Robust Game-Theoretic Multi-Agent Trajectory Optimization
Aviation authorities worldwide expect Advanced Air Mobility (AAM) traffic management to be decentralized among service providers, requiring AAM flights to autonomously plan trajectories by…From Migration to Calibration: Preserving Agent Capabilities across Models, Jurisdictions, and Scale
Agents need calibration when deployment conditions change: replacing a driving model, including a foundation-to-post-trained transition; crossing jurisdictions; or scaling across heterogeneous…Hybrid epidemic simulation framework coupling equation-based and individual-based models
Mass gathering events like concerts, sports matches, and festivals bring many people into close contact within a short period, creating localized bursts of infection that can shape epidemic outcomes…Persistent Partners Raise Prices Among Learning Agents
When pricing agents meet repeatedly on a platform, the platform decides who faces whom.Self-Adapting Group of Experts for Multi-Agent Reasoning
Multi-agent systems bring together language model agents with different roles to propose, review, and refine solutions.Positional choice and robust collective behavior in fish schools: biohybrid experiments and modeling
Collective behavior of fish schools is usually modeled on the assumption that each individual follows specific rules of motion that depend on its position and velocity relative to its neighbors.Denoising Multi-Robot Trajectories
Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multimodal, and high-dimensional nature.Reconstructing Implicit Scientific Knowledge: Evaluating LLM Agents through End-to-End Reproduction of Astronomy
The integration of large language models (LLMs) into scientific workflows is accelerating, yet their ability to reconstruct the reasoning underlying published research remains unexplored.Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance…VehicleArena: A Realistic Urban Environment for Multi-Agent Driving
Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions faced by others.Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems
Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantly impairs cooperation.Bregman Consensus
Consider a community of agents who are seeking consensus on a set of parameters.