Mixture-of-Agents

Latest papers

9 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
Sep 16, 2026Jiaxuan Jiang, Liyuan He, Zhixuan FangContinual Learning for LLM Agents Multi-Agent Systems
IIIS, Tsinghua University, Beijing, China · Shanghai Qi Zhi Institute, Shanghai, China · School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China
Role-Specialized Mixture-of-Agents with Open-Weight LLMs for Clinical Prediction
Aug 23, 2026Jun Hou, Yi Fang, Xuan WangMixture-of-Agents Clinical Prediction
Virginia Tech
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Jun 23, 2026Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +5Multi-Agent System Optimization LLM Agent Memory
University of Southern California · Iowa State University · Cisco AI Research
A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs
Jun 2, 2026Cuong Vuong Tuan, Trang Mai Xuan, Tien-Cuong Nguyen +2Multi-Agent LLM Systems Text Summarization
Faculty of Artificial Intelligence and Data Science, Phenikaa University, Duong Noi, Ha Noi, Viet Nam · VNPT AI, VNPT Group, Ha Noi, Viet Nam · MobiFone Research and Development Center, MobiFone Corporation, Ha Noi, Viet Nam +1
Beyond Consensus: Trace-Level Synthesis in Mixture of Agents
May 27, 2026Shreyas Fadnavis, Praitayini Kanakaraj, Felix WyssLanguage Model Ensembles Multi-Agent Reasoning
Bioscope AI
MMoA: An AI-Agent framework with recurrence for Memoried Mixure-of-Agent
May 18, 2026Rui ChuLLM Inference Acceleration Multi-Agent Systems
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models
May 15, 2026Xingjian Wu, Junkai Lu, Siyu Yan +4Multi-Agent Coordination Multi-Agent Systems
East China Normal University
ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms
May 4, 2026Alexandria K. Vail, Marcelo Cicconet, Katie Aafjes-van Doorn +2Depression Detection Mental Health
Deliberate Solutions, Inc. New York, New York, USA
From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution
Mar 1, 2026Kanata Suzuki, Kazuki Hori, Haruka Miyoshi +2Mixture-of-Agents Large Language Model-Based Robot Planning
Waseda University, Tokyo 169-8050, Japan. · NII Research and Development Center for Large Language Models. · The Graduate University for Advanced Studies (SOKENDAI), Kanagawa 240-0193, Japan. +1

Sep 16, 2026J/K move · Enter open · S save

CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents

Jiaxuan Jiang, Liyuan He, Zhixuan Fang

IIIS, Tsinghua University, Beijing, China · Shanghai Qi Zhi Institute, Shanghai, China · School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China

Continual Learning for LLM Agents Multi-Agent Systems RL Post-Training RL for LLM Agents LLM Agent Routing Mixture-of-Agents

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Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities. This disconnect prevents routing strategies from adapting to evolving agent capabilities during post-training and prevents agents from achieving synergistic data-driven specialization. To resolve this, we introduce CERA-MoA (Co-Evolving Router with continually learning Agents for Mixture-of-Agents), an iterative reinforcement learning framework where the dynamic router and independent agent policies co-evolve. We design a predictive familiarity estimator that leverages mid-layer hidden states to evaluate semantic competence among agents, avoiding the overhead of full rollouts. Based on these familiarity scores, a cumulative-threshold adaptive routing mechanism dynamically activates a tailored minimal agent subset, achieving a trade-off between task performance and efficiency. By proactively allocating targeted training samples to agents based on their evolving competence, CERA-MoA promotes capability differentiation. Extensive experiments across various domains demonstrate that CERA-MoA outperforms state-of-the-art static-agent routing and fix-workflow fine-tuning baselines.