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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...
Darwin‑Claw is a new framework for large language model agents that enables environment‑adaptive, self‑evolving behavior. It systematically distills episodic experiences into prototype vectors through a hierarchical experience consolidation system, providing context‑sensitive perception patches and explicit optimization signals for skill refinement. The method is deployable with mainstream agent systems and, according to experiments on WildClawBench, delivers significant performance gains while maintaining controllability and stability.
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
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...
The paper introduces SAGA, a framework that enables large language model agents to evolve by abstracting experiences into reusable principles, procedures, and episodic descriptions. SAGA transforms interaction trajectories into hierarchical knowledge with explicit applicability conditions, linking them back to execution evidence. Experiments on ScienceWorld and ALFWorld show that this execution–abstraction feedback loop improves task performance, and ablation studies confirm the importance of contextual instantiation and action regulation.
arXiv:2602. 06052v4 Announce Type: replace-cross Abstract: Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation.