用大模型生成约束精简ASP求解，最快提升5倍效率。
Streamliners for Answer Set Programming
- 用大模型自动生成可删减解空间的约束规则
- 在3个基准测试中实现最高5倍求解加速
- 适合需要快速求解组合问题的研究者
流线化约束通过排除解空间部分区域来缩小组合问题的搜索范围。本文将StreamLLM方法（利用大语言模型生成约束）应用于答案集编程（ASP）。给定一个ASP编码和少量训练实例，通过多个大模型生成候选约束；剔除导致语法错误、使可满足实例不可满足或在所有训练实例上性能下降的候选。剩余流线化约束与原编码一起评估，报告虚拟最佳编码（VBE）结果——对每个实例选择原编码及其精简变体中最快的一个。在三个ASP竞赛基准（合作伙伴单元问题、推箱子、汉诺塔）上，VBE相较原编码最高提速达4至5倍。
原文摘要 · Abstract (English)
Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the StreamLLM approach, which uses Large Language Models (LLMs) to generate streamliners for Constraint Programming, to Answer Set Programming (ASP). Given an ASP encoding and a few small training instances, we prompt multiple LLMs to propose candidate constraints. Candidates that cause syntax errors, render satisfiable instances unsatisfiable, or degrade performance on all training instances are discarded. The surviving streamliners are evaluated together with the original encoding, and we report results for a virtual best encoding (VBE) that, for each instance, selects the fastest among the original encoding and its streamlined variants. On three ASP competition benchmarks (Partner Units Problem, Sokoban, Towers of Hanoi), the VBE achieves speedups of up to 4 to 5 times over the original encoding.
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