AI & Machine Learning · Computer Science & AI
Multiagent Systems
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829 papers, from 4 September 2026 to 5 November 2026: 829 arXiv preprints.
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How language model agents reshape price competition
Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices…The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance
Many collective-action systems are neither fully decentralized nor fully centralized: agents voluntarily organize into institutions that share organizational costs and delegate decisions to…MetaShare: Failure-Aware Collective Self-Improvement for Open-Ended Agents
Humans often learn fastest not by repeating success, but by remembering which failures not to repeat.Learning Rewards in Strategic Games
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another.Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
An agent that learns a user's preferences from their choices faces a cold start problem: before any choices are observed, it must rely solely on a prior.Believed but Not Said: Measuring Hidden Belief Collapse in Multi-Agent LLM Discussions
LLM systems increasingly rely on groups of agents that discuss, critique, and check one another, assuming that peers will catch individual errors.Robust Multi-Agent Pursuit with Preemptive Communication via Reachability-Based Control Barrier Functions
Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state sharing.Decentralized LLM Workflows via Sequential Auctions
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents.Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines
When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while adhering to legal constraints.Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems
We study rational synthesis for stochastic multi-agent systems with unknown dynamics, where each agent is equipped with its own probabilistic temporal objective specified using Probabilistic Linear…Learning to Steer LLM Agents and Multi-Agent Systems
Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration.Learning Self-Orchestration in Decentralized Agent Swarms
The capabilities of an agent swarm depend on both its individual members and how they coordinate.Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually…Gossip Agents: Reputation through Opinion Dynamics over Evolving Networks
Sustained cooperation among self-interested learning agents is essential in many shared environments.Whose Polarization Is It? Auditing Fairness in Network Polarization Measures
A polarization score is supposed to describe a community, not its surroundings.Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management
Indivisible resources allocated in advance may need to be reallocated when capacity reductions make existing assignments infeasible, yet monetary compensation is often impractical or undesirable.Emergent Discrimination and Polarization in Multi-Agent Systems
We study theoretically how discrimination and polarization can emerge in a population of learning agents.Federation over Harness
Modern LLM-powered agents increasingly improve through experience sharing, where distributed agents distill local reasoning experience into reusable knowledge, such as a shared insight library.Why Do Agent Teams Fail Across Boundaries? A Failure Taxonomy for Agent-to-Agent Systems with Private States
Agents are increasingly interacting with other agents that act for different principals (people or organizations on whose behalf they work), such as a buyer's personal agent negotiating with an…When Is Smaller Greener? Right-Sizing Language Models for Agentic AI
AI agents use large language models (LLMs) to plan and act over multiple steps, yet models are usually compared per call rather than per completed task.