{
  "id": 19193,
  "url": "https://arxiv.org/abs/2608.12720v1",
  "title": "ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval",
  "summary": "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",
  "authors": "Haolong Chen, Liang Zhang, Zhuo Li, Lei Xue, Guanrxu Zhu",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T02:06:01.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19193",
  "original_url": "https://arxiv.org/abs/2608.12720v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}