用语言模型思维自动发现新算法，解决难解优化问题。
Discovering Algorithms with Computational Language Processing
- 将算法视为操作序列，用语法链式生成新方法。
- 在组合优化和量子算法上超越现有方法，显著提升性能。
- 适合研究算法设计与智能计算的学者和工程师。
算法是可复现问题求解的核心。我们提出一种框架，将算法概念化为操作序列，并以标记（tokens）表示。这些计算标记通过语法连接，形成越来越复杂的流程。基于强化学习引导的集成蒙特卡洛树搜索（ensemble MCTS）探索标记链式结构，驱动新标记的生成。该方法重新发现、改进并生成了新算法，在强NP难组合优化问题及基础量子计算方法（如格罗弗算法和量子近似优化算法）上表现显著优于现有方法。该框架在计算层面而非代码生成层面运作，可针对具体问题实例定制算法，而不仅限于问题类别。
原文摘要 · Abstract (English)
Algorithms are the engine for reproducible problem-solving. We present a framework automating algorithm discovery by conceptualizing them as sequences of operations, represented as tokens. These computational tokens are chained using a grammar, enabling the formation of increasingly sophisticated procedures. Our ensemble Monte Carlo tree search (MCTS) guided by reinforcement learning (RL) explores token chaining and drives the creation of new tokens. This methodology rediscovers, improves, and generates new algorithms that substantially outperform existing methods for strongly NP-hard combinatorial optimization problems and foundational quantum computing approaches such as Grover's and Quantum Approximate Optimization Algorithm. Operating at the computational rather than code-generation level, our framework produces algorithms that can be tailored specifically to problem instances, not merely classes.
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