ArticleScience advances2026
Discovering algorithms with computational language processing.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
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Abstract
We present a framework automating algorithm discovery by bootstrapping their natural conceptualization 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 guided by reinforcement learning explores token chaining and drives the creation of new tokens via byte-pair encoding. This methodology rediscovers, improves, and generates new algorithms that substantially outperform existing methods for strongly nondeterministic polynomial-time-hard combinatorial optimization problems and foundational quantum computing approaches such as Grover's and the 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.