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arXiv · 2609.37056

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

Amal Seddas·Vladyslav Shashkov·Maryna Viazovska·Emmanuel Abbe
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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.

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Amal Seddas, Vladyslav Shashkov, Maryna Viazovska, Emmanuel Abbe. 2026-09-29. Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes. https://arxiv.org/abs/2609.37056

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