Interactive Task Alignment as a POMDP

Current benchmarks for language models primarily evaluate execution on fully specified tasks. However, real user tasks are often ambiguous. Users arrive with incomplete, exploratory, or even inconsistent goals, requiring the assistant to first determine the intended task before carrying it out. We study this problem as task alignment: the ability to align with a user on their intended task. We introduce a general framework for converting specified tasks into underspecified interactions, formalized as a POMDP in which the model must infer a latent task from partial and evolving user intent. We validate our user simulator post hoc with a human user study. Across shopping, coding, and professional work settings, we find that while models often perform well once the task is specified, they still struggle with task alignment: current models act prematurely, interact ineffectively, and fail to resolve ambiguous requests. Models on average recover the user's intended task 16-22% of the time under ambiguity. Compared with humans on a random subset of tasks, humans recover 48% of intended tasks versus a 26% model average, beating all evaluated models. Given the gap, we evaluate two approaches to improving task alignment: (1) belief-state planning, and (2) post-training with supervised fine-tuning and reinforcement learning. We notice while all tested methods can improve task alignment, they still generally lag behind humans, suggesting that current models still lack key interaction abilities required for reliable agency.