arXiv AI
Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 8
LLM-Guided Search for Deletion-Correcting Codes
arXiv:2504. 00613v2 Announce Type: replace Abstract: Finding deletion-correcting codes of maximum size has been an open problem for over 70 years, even for a single deletion.
arXiv AI
3d ago
Coding Agents for Coding Theory
The authors report that a large‑scale experiment using a language‑model coding agent over five weeks produced new lower bounds for DNA‑barcode‑style codes. By restricting searches to codes with a prescribed symmetry, the agent improved the best known code of length 6 and minimum edit distance 3 from 114 to 120 words, and similarly raised lower bounds for lengths 6–9 and distances 3–6. The study also documents failures and the limitations of the verification protocol, noting that intermediate results were never rechecked and could lead to erroneous conclusions.
arXiv AI
Aug 11
Multi-agent discovery of practical quantum LDPC codes
arXiv:2608. 08996v1 Announce Type: cross Abstract: Quantum low-density parity-check (qLDPC) codes can encode multiple logical qubits using sparse parity checks, yet searching for useful finite-length instances remains a challenging design problem because code performance must be optimized while satisfying practical constraints.
arXiv AI
Jun 2
Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search
arXiv:2606. 02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found.
Hugging Face Trending Papers
Jun 1
Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search
Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found. We introduce an LLM-guided evolutionary workflow in which language models mutate Python programs that generate bivariate-bicycle and perturbed bivariate-bicycle code ansätze.
arXiv Machine Learning
Jul 3
DecompRL: Solving Harder Problems by Learning Modular Code Generation
arXiv:2607. 02390v1 Announce Type: new Abstract: How can Large Language Models (LLMs) solve problems they currently cannot?