NeurIPS 2026 Workshop on Dynamic Alignment in Human-AI Coupled Systems

Large language models, multimodal models, and agentic AI systems are increasingly becoming long-term interactive actors in education, healthcare, scientific discovery, robotics, and other high-stakes domains. In these settings, AI systems shape human trust, dependence, values, and behavior, while human feedback and behavior in turn shape agents’ objectives, reward signals, and policy updates. This workshop introduces Dynamic Alignment in Human–AI Coupled Systems as a research agenda for studying alignment as an evolving property of coupled human–AI systems, rather than a static property of AI models alone. Bringing together researchers across machine learning, AI safety, human–AI interaction, cognitive science, social computing, governance, and philosophy, the workshop will examine how human states, agent policies, feedback signals, objectives, and evaluation criteria co-evolve over time. Through keynotes, panels, papers, posters, and structured discussions, the workshop aims to advance foundations, evaluations, and interventions for modeling, measuring, and controlling alignment dynamics, and to catalyze an interdisciplinary agenda for safer human–AI futures.