用大模型自动匹配最优求解器策略，一试多用
DaSAThco: Data-Aware SAT Heuristics Combinations Optimization via Large Language Models
- 基于问题特征生成多样化求解策略组合
- 在多个数据集上性能超越传统方法，跨域泛化能力强
- 适合需要快速适配新类型问题的自动化求解场景
冲突驱动子句学习求解器的性能依赖于内部启发式策略，但SAT问题的多样性使得单一通用配置难以实现。以往自动化方法虽能为特定问题族找到优化配置，但缺乏泛化能力，且对新问题类型需重新优化。我们提出DaSAThco框架，通过大型语言模型结合系统定义的问题原型，学习从实例特征到定制化启发式组合的通用映射，实现一次训练、广泛适应。该框架生成多样化的专用启发式组合，并学习自适应选择机制形成最终映射。实验表明，DaSAThco不仅整体性能更优，更在跨域测试中表现出显著鲁棒性，而传统非自适应方法在此类场景下表现受限。本工作为复杂可配置系统的自动化设计提供了更可扩展、更实用的路径。
原文摘要 · Abstract (English)
The performance of Conflict-Driven Clause Learning solvers hinges on internal heuristics, yet the heterogeneity of SAT problems makes a single, universally optimal configuration unattainable. While prior automated methods can find specialized configurations for specific problem families, this dataset-specific approach lacks generalizability and requires costly re-optimization for new problem types. We introduce DaSAThco, a framework that addresses this challenge by learning a generalizable mapping from instance features to tailored heuristic ensembles, enabling a train-once, adapt-broadly model. Our framework uses a Large Language Model, guided by systematically defined Problem Archetypes, to generate a diverse portfolio of specialized heuristic ensembles and subsequently learns an adaptive selection mechanism to form the final mapping. Experiments show that DaSAThco achieves superior performance and, most notably, demonstrates robust out-of-domain generalization where non-adaptive methods show limitations. Our work establishes a more scalable and practical path toward automated algorithm design for complex, configurable systems.
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