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Notes on multi-agent systems
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pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
The paper introduces pAI-Econ-claude, a multi-agent architecture that helps in AI-assisted economic theory development. The system uses a gated, human-in-the-loop approach to coordinate agents and ensure the final…
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
A new multi-agent architecture, named pAI-Econ-claude, was proposed for AI-assisted economic theory development. This architecture uses a gated human-in-the-loop system to ensure reliability in tasks where no machine…
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
A study comparing direct exposure to a dangerous objective versus multi-agent mediation using OpenAI's gpt-5.6-sol model reveals that direct exposure leads to advice contrary to the target, while mediated exposure…
BrainPilot: Automating Brain Discovery with Agentic Research
BrainPilot is a multi-agent system designed to accelerate brain science research by integrating evidence from various scales, modalities, and disciplines. It uses a unified brain science knowledge base containing over…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework that simulates political coalition formation using Large Language Models (LLMs). The framework combines Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO)…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework using Large Language Models (LLMs) to simulate and audit political coalition formation in elections. The framework combines Supervised Fine-Tuning (SFT), Direct Preference…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers have developed a multi-agent framework using Large Language Models (LLMs) to simulate coalition formation in political elections. The framework combines Supervised Fine-Tuning, Direct Preference…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
Researchers introduce SearchOS, a system-level framework designed to improve the efficiency of multi-agent information-seeking systems. By making search progress explicit and persistent, it helps prevent repetitive…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system-level multi-agent framework designed to improve the quality and completeness of search results in open-domain information-seeking tasks. The framework addresses the issue of…
The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model is introduced in this research, aiming to account for the effects of autonomous AI agents on traditional customer loyalty paradigms. The…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
A new multi-agent system named Pythia was developed for automatically and independently creating and optimizing clinical concept extraction prompts without manual engineering or fine-tuning. The study compared Pythia…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers present Bulkhead, an automated framework that uses large language models and formal methods to detect and fix container escape vulnerabilities called path traversal (PaTra) vulnerabilities. These…
Unveiling Complex Collective Behaviors from Simple Rewards
Researchers have developed a new method called the Agent Response Map (ARM) to help understand how simple rewards can lead to complex behaviors in multi-agent reinforcement learning (MARL). The ARM reveals that robots…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, an adaptive multi-agent framework that reduces hallucinations in lightweight large language models used in rule-based scientific domains. The framework is based on Bayesian and team game…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
WebSwarm is a framework that uses multiple language model-based search agents to perform complex, deep-and-wide web searches. It aims to overcome the limitations of single ReAct-style agents by dynamically…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper proposes a new framework called WebSwarm for multi-agent search systems. This system aims to improve deep-and-wide web searches by dynamically creating search nodes that can delegate tasks recursively and…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper presents a new framework called WebSwarm for web search using multiple agents. This system aims to overcome the limitations of single agents by simultaneously handling depth and coverage, improving recursive…
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
The research presents a new coding system called Graph-as-Policy (GaP) for robots to work reliably in commercial and industrial applications. GaP combines interpretable robot programming with the adaptability of…
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The researchers have developed a multi-agent framework called OptiAgent that can take a natural language description of an Operations Research problem and produce a mathematically formulated solution along with…
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The authors propose OptiAgent, a multi-agent framework that takes a natural language description of an Operations Research problem and produces a solver-ready mathematical formulation and executable code. The…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes QFedAgent, a new approach for multi-agent activity recognition in federated learning. It uses a hybrid quantum-classical framework and a variational quantum circuit fusion module that models…
UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development
The researchers proposed UA-ChatDev, an uncertainty-aware multi-agent software development framework to improve the reliability of autonomous multi-agent software development. The framework uses a lightweight…
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Researchers studied how language models (LLMs) behave in socially structured settings, where role, audience, and relationships can influence what the LLM says publicly versus privately. They found that when there's an…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers propose a multi-agent system (MAS) called TreeAgent to automate bias labeling in forestry via expert decision trees and Vision-Language Models (VLMs). The MAS aims to reduce the need for human annotation…
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