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From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving 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

Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, transferable capabilities that guide task planning, inform contextual decisions, support tool use, and enable executable procedures. Yet existing skill-evolution methods largely organize knowledge around skill artifacts, overlooking the functional he

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#278 most recent of 960 cs.AI papers we have recorded · ↑ newer: SIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conve · ↓ older: RL-Guided Temporal Localization for Dual-Channel Retrieval in Long-Hor
Cite this page: From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents: the #278 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/from-trajectories-to-differentiated-skills-cognitive-differentiation-for-self-im.html
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