1 citations · 2 across the 8 of their papers we have counts for
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A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
Shengji Tang, Jianjian Cao, Weihao Lin +7
Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose…
StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
Xiangyuan Xue, Yifan Zhou, Zidong Wang +5
Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely…
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
Xiangyuan Xue, Yifan Zhou, Guibin Zhang +7
Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witn…
ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems
Xiangyuan Xue, Zeyu Lu, Di Huang +3
Much previous AI research has focused on developing monolithic models to maximize their intelligence, with the primary goal of enhancing performance on specific tasks. In contrast,…
The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants
Yiqun Zhang, Hao Li, Chenxu Wang +11
Proprietary giants are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this p…
Nature-Inspired Population-Based Evolution of Large Language Models
Yiqun Zhang, Peng Ye, Xiaocui Yang +5
Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction. Inspired by this principle, this paper formall…