Synthetic Semantic Supervision for Contrastive Code Representation Learning in S

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2604.04990v1 Announce Type: cross Abstract: AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural

arXiv:2603.15159v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in

arXiv:2610.03984v1 Announce Type: new Abstract: Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra

arXiv:2603.22294v1 Announce Type: cross Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an

arXiv:2603.18532v1 Announce Type: cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar

<blockquote> <p>Researchers introduce ReCUBE, a benchmark isolating LLMs' ability to use repository-wide context for code generation. GPT-5 achieves just a 37.57% strict pass