AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills
Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable paradigm, checking correctness, and refining complexity. Existing large language model (LLM) methods often rely on direct generation or generic self-refinement, leaving these steps implicit. We propose AlgoSkill, which models algorithm design as sequential decision-making over a typed library of algorithmic skills, including abstraction, constraint analysis, state design, data-structure selection, proof checking, counterexample construction, and complexity refinement. A learned scheduler proposes skills from the current design state, while a...
| Authors | Xinyuan Song, Zekun Cai, Liang Zhao |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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