Discovering Algorithms with Computational Language Processing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909685038186496 |
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| author | Bourdais, Theo Gnanasekaran, Abeynaya Owhadi, Houman Sahai, Tuhin |
| author_facet | Bourdais, Theo Gnanasekaran, Abeynaya Owhadi, Houman Sahai, Tuhin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering Algorithms with Computational Language Processing Bourdais, Theo Gnanasekaran, Abeynaya Owhadi, Houman Sahai, Tuhin Artificial Intelligence Data Structures and Algorithms Machine Learning es: 68T05, 68T20, 68Q12, 90C27 I.2.6; I.2.8; F.2.2; F.1.2; G.2.1 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. |
| title | Discovering Algorithms with Computational Language Processing |
| topic | Artificial Intelligence Data Structures and Algorithms Machine Learning es: 68T05, 68T20, 68Q12, 90C27 I.2.6; I.2.8; F.2.2; F.1.2; G.2.1 |
| url | https://arxiv.org/abs/2507.03190 |