arXiv:2606. 17514v1 Announce Type: cross Abstract: Large Language Models have shown remarkable capabilities in code generation.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated algorithms or complex implementations.
arXiv:2607. 04537v1 Announce Type: cross Abstract: Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the instructions they act on are correct.
Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the instructions they act on are correct. We study what happens when that assumption breaks.
arXiv:2507. 22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation.
arXiv:2512. 02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or language translation.
arXiv:2608. 14953v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, and it has since emerged as one of the most fundamental tasks for LLMs to perform; however, at present, LLMs struggle to apply wide-ranging code optimization tasks due to both the complexity of the code and the inability to independently verify the correctness of the transformations.