Darwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents
Paper recorded by Signals 4 on 2026-10-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19
Category: cs.AI · 人工智能 · first seen 2026-10-19
Abstract
While Large Language Model (LLM) agents have demonstrated remarkable capabilities across complex tasks, they predominantly operate in isolated sessions, lacking the ability to continually learn from historical interactions or adapt to user-specific environments. Existing self-evolution paradigms often indiscriminately inject raw interaction trajectories into memory or skill modules, leading to obs
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Cite this page: Darwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents: the #280 most recent of 960 cs.AI papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/darwin-claw-environment-adaptive-experience-consolidation-for-recursively-self-e.html
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