A new arXiv preprint introduces DeepDiscovery, a framework aimed at helping language models understand large industrial code repositories at the task level. The authors argue that current retrieval approaches surface only isolated fragments and miss broader repository context. The work targets software engineering tasks where whole-repository comprehension matters.
A new arXiv paper studies what happens when developers repeatedly delegate bug fixing to large language models, including cases where each model output is used without close human inspection. The research is motivated by the growing reliance on LLM-based automated program repair tools in code review workflows. As its title suggests, the authors argue that applying model-generated fixes to code that is not actually buggy can be counterproductive.
A newly posted arXiv paper outlines a software delivery model called A-JIT, in which AI agents assemble applications on the fly rather than shipping pre-built, fixed artifacts. The authors argue that construction should move from a static step completed before execution to a dynamic process performed at the moment code is actually needed.
A new arXiv paper investigates whether large language models can add exception-related code to existing software, covering throw statements, the conditions that guard them, and try/catch blocks. The goal is to automatically equip codebases with better handling of exceptional states, an area the authors identify as essential but often underdeveloped. The work is aimed at developers seeking AI-assisted ways to strengthen error handling in mature systems.
A new study examines vibe coding, an approach in which developers create software by conversing with large language models in natural language. Using a mixed-methods design, the researchers assess how the practice affects programming performance and developer cognition. The work also offers guidance on adopting AI-led conversational programming responsibly.
A new arXiv preprint examines the coordination challenges that arise when an AI agent is embedded inside an existing application, where users may change goals or edit shared objects while the agent is still acting on earlier instructions. The authors argue that reliable integration depends on explicit interaction contracts plus ongoing verification of the agent's behavior during execution. The work frames this as a continuous assurance problem rather than a one-time setup step.