{
  "id": 4057,
  "url": "https://arxiv.org/abs/2605.19330v1",
  "title": "MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization",
  "summary": "LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves, and responds. Unlike monolithic prompts, skills are multi-field artifacts subject to hard platform constraints: description fields are truncated for routing, instruction bodies are compacted via progressive disclosure, and co-resident skills compete for limited context windows. These constraints make skill optimization inherently multi-objective: a skill must simult",
  "authors": "Md Mehrab Tanjim, Jayakumar Subramanian, Xiang Chen, Branislav Kveton, Subhojyoti Mukherjee, Anlan Zhang et al.",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-19T04:07:41.000Z",
  "fetched_at": "2026-07-14T16:30:41.584Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4057",
  "original_url": "https://arxiv.org/abs/2605.19330v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}