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Notes on multi-agent systems
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DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
A new system called DentAgent is introduced that aims to improve the accuracy and reliability of dental assessments by integrating various forms of evidence from different modalities. The system uses an…
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
The authors studied how AI coding agents coordinate while working on programming tasks. They developed a method to measure this coordination by representing each run as a network with nodes (agents and files) connected…
Multi-Agent AI Safety as an Institutional Design Problem
The paper discusses AI safety as an institutional design problem in multi-agent systems. The research focuses on understanding which parts of an AI institution ensure safety and how they do it. The study involves a…
NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
Researchers propose NeuroRefiner, a multi-agent system for improving the accuracy of 3D neuron segmentation in fluorescence microscopy. The system addresses challenges posed by the sparse and elongated morphology of…
The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
A study introduces a new game called Hierarchical Game (HG) to test LLMs behavior in multi-agent organizations. The research finds distinct behavioral profiles among six frontier models, with some promising and lying…
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
CoPlan is a new AI system designed to help with care planning for clinicians, patients, caregivers, and care teams. It allows stakeholders to inspect, challenge, and revise plans if they conflict with clinical…
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
The paper introduces CoPlan, a new AI system designed to aid in care planning. CoPlan allows various stakeholders like clinicians, patients, and caregivers to collaborate on complex decisions by generating potential…
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
Researchers propose NSF-HRPT, a framework that uses a Neural Semantic Field (NSF) to model scene semantics, trajectory predictions, and probabilistic Time-to-Collision (TTC) distributions from simulation data. During…
When Does Latent Communication Pay? A Causal Audit of Relayed KV Caches in Multi-Agent LLMs
The study investigates how effective relaying key-value caches between multi-agent language models is, particularly when the receiver does not need the sender's private information. The research shows that when the…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for adapting communication structures in multi-agent systems during inference time. This allows for more efficient problem solving by enabling agents to adjust their roles…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during inference time. This…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during deployment. The goal is to…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
Researchers present PAIChecker, a system designed to detect misalignment between Pull Requests (PRs) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models (LLMs). The system identifies…
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
Researchers have introduced a new framework for ontology matching called AgentMap. This framework can simultaneously discover both equivalent and subsuming mappings in ontologies, which existing systems cannot do. The…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers have developed a method to evaluate if latent channels in large language models effectively communicate task-relevant information in multi-agent systems. The study applies this method to Qwen3 and Qwen3-8B…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers introduce a method to study how latent channels in multi-agent systems, which transmit information without using text, impact the receiver's performance. They apply this method to Qwen3-4B and Qwen3-8B on…
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
Researchers propose an approach called CE-CM (Capability Estimation via Contextual Models) to help autonomous agents collaborate with diverse partners in a multi-task setting. The method infers task-invariant…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
The paper introduces a new deep reinforcement learning architecture called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections. The MAPS system uses a centralized…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers proposed a new system called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections using deep reinforcement learning. MAPS outperformed state-of-the-art…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers present MAPS, a deep reinforcement learning architecture for coordinating autonomous vehicles at unsignalized intersections. The system uses a central Master agent that generates a global coordination…
Explainable Belief Harmonization under Dynamic Epistemic Partitions
This paper presents a formal framework for handling dynamic changes in epistemic partitions (groups of agents with shared information) during the execution of multi-agent systems. The framework uses a hybrid approach…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors present GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating updates across multiple agents in the pipeline. The system uses feedback from an Evaluator to update…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across different components of the pipeline. It uses feedback from an Evaluator to…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework that improves Retrieval-Augmented Generation (RAG) systems by coordinating improvements across the pipeline. Instead of optimizing components in isolation, GRADRAG models the…
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