Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes

Abstract:Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and beyond. We apply this approach to the longstanding problem of improving the best-known bounds for binary linear codes. Building on the EvoTune evolutionary framework and the ShinkaEvolve codebase, we introduce LinCodeEvolve, which evolves code-construction programs against an exact minimum-distance evaluator. A strategy loop combines diversity-driven search and expert supervision: when progress plateaus, new strategies are used to redirect the search. LinCodeEvolve discovers seven record-breaking codes, $[172,21,66]$, $[173,20,68]$, $[176,21,68]$, $[181,21,70]$, $[184,21,72]$, $[189,22,72]$ and $[200,21,77]$, six of which have concise quasi-cyclic descriptions. With standard code modification techniques, they improve $22$ entries of the tables. Every code is verified by exhaustive enumeration. These results suggest that LLM-guided search can help find improved codes and complement existing methods in coding theory.

Information Theory (cs.IT); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)

arXiv:2609.37056 [cs.IT]

(or arXiv:2609.37056v1 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2609.37056 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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