ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval
While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this memory are rarely treated as evolvable components. This static approach limits performance on heterogeneous memory queries, which often demand diverse evidence construction strategies. To address this, we introduce \textbf{ERSkill}, a retrieval-centric framework for self-evolving, skill-guided memory access. ERSkill compiles interaction histories into
Record details
Published: 13 August 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 14 August 2026
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ethics.ai (13 August 2026), “ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval,” evidence record 19193, https://ethics.ai/record/19193 (originally published by arXiv).
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