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.
Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes
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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?
arXiv AI
Aug 14
CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language
arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.
arXiv AI
Sep 4
Evolving Excellence: Automated Optimization of LLM-based Agents
The paper introduces ARTEMIS, a no-code evolutionary optimization platform that automatically tunes large language model (LLM) agents by jointly optimizing prompts, tool descriptions, and parameters using semantically-aware genetic operators. Starting from a benchmark script and natural language goals, ARTEMIS discovers configurable components, extracts performance signals from execution logs, and evolves configurations without architectural changes. Experiments on four agent systems show significant gains: a 13.6% increase in acceptance rate for the ALE Agent, a 10.1% performance boost for the Mini‑SWE Agent, a 36.9% token‑reduction for the CrewAI Agent, and a 22% accuracy improvement for the MathTales‑Teacher Agent using a smaller open‑source model.
arXiv AI
Jun 2
BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
arXiv:2606. 01286v1 Announce Type: cross Abstract: The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal.
arXiv AI
Jul 2
AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation
arXiv:2607. 00062v1 Announce Type: cross Abstract: High pass rates on established programming benchmarks such as HumanEval and LiveCodeBench do not always show whether a model can reason about algorithms.
arXiv AI
Jul 3
XSearch: Explainable Code Search via Concept-to-Code Alignment
arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.
arXiv AI
Jul 22
SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation
arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.