Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees
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TL;DR - This paper formulates context-budgeted skill selection for LLM agents as submodular optimization and introduces Best Prefix Selection (BPS), a polynomial-time algorithm with a provably optimal bicriteria approximation. BPS substantially improves task success while using fewer tokens than the strongest released router tested.
- Models skill selection as maximizing monotone submodular benefit minus context penalty under a hard token budget.
- Proves BPS achieves a bicriteria ((1-1/e, 1)) approximation, with a benefit coefficient that is optimal in polynomial time.
- On a contamination-controlled BigCodeBench variant, BPS reaches 0.73 task success versus 0.20–0.52 for skill routers, text retrievers, and executor-based selection.
- BPS uses 28% fewer tokens than the strongest released router.
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Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees
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TL;DR - This paper formulates context-budgeted skill selection for LLM agents as submodular optimization and introduces Best Prefix Selection (BPS), a polynomial-time algorithm with a provably optimal bicriteria approximation. BPS substantially improves task success while using fewer tokens than the strongest released router tested.
- Models skill selection as maximizing monotone submodular benefit minus context penalty under a hard token budget.
- Proves BPS achieves a bicriteria ((1-1/e, 1)) approximation, with a benefit coefficient that is optimal in polynomial time.
- On a contamination-controlled BigCodeBench variant, BPS reaches 0.73 task success versus 0.20–0.52 for skill routers, text retrievers, and executor-based selection.
- BPS uses 28% fewer tokens than the strongest released router.