arXiv AI
Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems
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EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems
arXiv:2610.34450v1 Announce Type: cross Abstract: Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other age...
arXiv AI
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MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems
arXiv:2602.13671v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, existing works of...
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SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
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arXiv AI
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CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems
arXiv:2610.33539v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration...
arXiv AI
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ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
ST‑EVO is a new framework for Large Language Model‑powered Multi‑Agent Systems that introduces a spatio‑temporal evolution of communication topologies. It uses a flow‑matching based scheduler to enable dialogue‑wise communication scheduling and incorporates uncertainty perception and self‑feedback to refine its scheduling over time. Experiments on nine benchmarks show that ST‑EVO outperforms existing methods, improving accuracy by roughly 5% to 25%.
arXiv Machine Learning
Aug 6
EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement
arXiv:2608. 04968v1 Announce Type: new Abstract: The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from failure.