Deep Learning for Code: Towards Human-Centered Coding Agents

AI coding agents have rapidly improved in their ability to perform complex software engineering tasks autonomously. However, as these systems advance, the main bottleneck to real-world usefulness is shifting from task-solving capability to challenges in communication, oversight, and trust between humans and agents. This year, the 5th Deep Learning for Code (DL4C) workshop at ICML will focus on human-centered coding agents: systems designed not only to complete tasks, but to collaborate effectively with humans. Building on previous DL4C editions (ICLR '22, '23, '25; NeurIPS '25; https://dl4c.github.io), the workshop will highlight interaction-level questions such as task alignment, verifiability, steerability, and adaptability in human-agent workflows. We aim to bring together researchers from ML, NLP, HCI, and SE to develop shared evaluation methods, user-involved coding environments, and scalable approaches to studying human-AI collaborative coding. By emphasizing human-centered design, the workshop seeks to advance coding agents that are more controllable, interpretable, and broadly useful in practice.