MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911696953540608 |
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| author | Tanjim, Md Mehrab Subramanian, Jayakumar Chen, Xiang Kveton, Branislav Mukherjee, Subhojyoti Zhang, Anlan Kim, Sungchul Sarkhel, Somdeb Choudhury, Sunav |
| author_facet | Tanjim, Md Mehrab Subramanian, Jayakumar Chen, Xiang Kveton, Branislav Mukherjee, Subhojyoti Zhang, Anlan Kim, Sungchul Sarkhel, Somdeb Choudhury, Sunav |
| contents | 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 simultaneously maximize task performance and satisfy platform limits. Yet existing prompt optimizers either ignore these trade-offs or collapse them into a weighted sum, missing Pareto-optimal variants in non-convex objective regions. We introduce MOCHA (Multi-Objective Chebyshev Annealing), which replaces single-objective selection with Chebyshev scalarization - covering the full Pareto front, including non-convex regions - combined with exponential annealing that transitions from exploration to exploitation. In our experiments across six diverse agent skills - where all methods share the same multi-objective mutation operator and baselines receive identical per-objective textual feedback - existing optimizers fail to improve the seed skill on 4 of 6 tasks: 1000 rollouts yield zero progress. MOCHA breaks through on every task, achieving 7.5% relative improvement in mean correctness over the strongest baseline (up to 14.9% on FEVER and 10.4% on TheoremQA) while discovering twice as many more Pareto-optimal skill variants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19330 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization Tanjim, Md Mehrab Subramanian, Jayakumar Chen, Xiang Kveton, Branislav Mukherjee, Subhojyoti Zhang, Anlan Kim, Sungchul Sarkhel, Somdeb Choudhury, Sunav Artificial Intelligence Machine Learning Software Engineering I.2.7; I.2.6; I.2.4; I.2.8 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 simultaneously maximize task performance and satisfy platform limits. Yet existing prompt optimizers either ignore these trade-offs or collapse them into a weighted sum, missing Pareto-optimal variants in non-convex objective regions. We introduce MOCHA (Multi-Objective Chebyshev Annealing), which replaces single-objective selection with Chebyshev scalarization - covering the full Pareto front, including non-convex regions - combined with exponential annealing that transitions from exploration to exploitation. In our experiments across six diverse agent skills - where all methods share the same multi-objective mutation operator and baselines receive identical per-objective textual feedback - existing optimizers fail to improve the seed skill on 4 of 6 tasks: 1000 rollouts yield zero progress. MOCHA breaks through on every task, achieving 7.5% relative improvement in mean correctness over the strongest baseline (up to 14.9% on FEVER and 10.4% on TheoremQA) while discovering twice as many more Pareto-optimal skill variants. |
| title | MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization |
| topic | Artificial Intelligence Machine Learning Software Engineering I.2.7; I.2.6; I.2.4; I.2.8 |
| url | https://arxiv.org/abs/2605.19330 |