{
  "id": 16605,
  "url": "https://arxiv.org/abs/2607.28048",
  "title": "SKILL-KD: Contrastive Skill Distillation for LLM Agents",
  "summary": "Skill-based prompting has become a practical mechanism for improving large language model (LLM) agents, yet existing skill acquisition methods often treat skills as experience summaries, memory entries, or direct summaries of successful demonstrations. This creates a mismatch for weaker student agents: when a student fails because it lacks task knowledge or operational strategy, its failed trajectory may not contain enough evidence to infer the missing behavior, while the teacher trajectory may",
  "authors": "Qiming Shi, Yibo Dou, Jiawen Zhu, Yulong Tao, Linbo Jin, Zhaolu Kang",
  "category": "research",
  "topics": "children-education,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T20:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/16605",
  "original_url": "https://arxiv.org/abs/2607.28048",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}