MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
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
Record details
Published: 19 May 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 14 July 2026
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ethics.ai (19 May 2026), “MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization,” evidence record 4057, https://ethics.ai/record/4057 (originally published by arXiv).
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