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 9
A Hyper-Logical View on Multi-Agent Path Planning
Multi-Agent Path Planning (MAPP) requires collision-free paths for multiple agents.EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis
Deploying LLM agents in specialized domains often require fine-grained task-specific procedures and knowledge a general model lacks, yet parameter training is costly and non-interpretable, and…The Coordination Tax and the Heterogeneity Dividend: Break-Even Conditions for Multi-Agent LLM Pipelines
Multi-agent LLM pipelines built on interoperability protocols such as the Open Floor Protocol (OFP) or A2A are usually justified on capability grounds, with their runtime cost treated as an overhead…Agent-to-Agent Synthetic Social Engineering on the Web
An AI agent can prepare an accurate report and still request an action its user has not authorized.Secure and Functional Code Generation with Self-Evaluation
Large Language Models (LLMs) can generate functionally correct code that remains insecure.Build, Don't Play: Self-Improving LLM Research Agents Develop a Hearthstone Battlegrounds AI That Rivals Top Players
Can LLM agents carry a hard real-world problem from raw data to a system that holds its own against people?PASS Is a Claim, Not an Observation: Mechanical Re-verification of 10 Self-Asserted Fix Claims in a Live Multi-Agent Audit Layer
When agents claim to have fixed their own defects, how do we know the fix is real?Network Topology Does Not Matter: The Breakdown of Classical Consensus in LLM Populations
As multi-agent language model networks become widely deployed, predicting their collective behavior is a critical challenge.Markets of Minds: A 51-Billion-State Economy of Intelligence
When intelligence is shared, reasoning becomes a constrained allocation problem.Empirically Calibrated Differential Games for Simulating Driver Interactions
Game-theoretic models can resolve vehicle interactions without hand-written priority rules, but whether they describe human drivers once calibrated has rarely been tested.PULSES: multi-agent AI for autonomous excited-state model building and dynamics
Simulating molecular excited-state processes requires resolving electronic and nuclear dynamics, but the cost of direct quantum-chemical calculations limits accessible system sizes and timescales.HGWM: Hierarchical Graph-Guided World Model for Zero-Shot Object Navigation via Graph Matching
Zero-shot object navigation requires an agent to reason about where a target may be found while retaining evidence from previous observations.Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization
Calibrating agent-based models (ABMs) is computationally demanding, as traditional black-box optimization typically requires hundreds or thousands of independent simulation runs.J-Comm: Jacobian-Guided Latent Communication between LLM Agents
Latent communication requires both an effective continuous reasoning process and an interface through which an external model can leverage its intermediate computation.Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion Simulations
In LLM-agent opinion simulations, each agent reports the probability that a claim is true after seeing its own previous probability and the probabilities reported by its neighbors, and that number…Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments
Safe navigation in congested environments requires both collision avoidance and coordinated passage.Coral Discovery: The Division of Cognitive Labor in Autonomous Research
A research community is only as strong as the way it divides its cognitive labour.Estimation of Propagated Risk in LLM-based Multi-Agent Systems
Large Language Model (LLM) based multi-agent systems introduce a distinctive reliability challenge in which actions taken by upstream agents can affect the correctness of downstream agents.Governed Self-Evolution for Autonomous Investing: A Multi-Agent LLM System with an Invariant Core
LLM agents that act on live Web data must combine heterogeneous evidence, remember what they have done and improve from outcomes without weakening their own safeguards.LLM Multi-Agent Systems Are Vulnerable to Herding and Runaway Cascades
Large language models (LLMs) increasingly operate in multi-agent systems where they collaborate by responding to others' choices, creating endogenous feedback loops with vulnerability to social…