[2602.10525] LHAW: Controllable Underspecification for Long-Horizon Tasks

Computer Science > Computation and Language arXiv:2602.10525 (cs) [Submitted on 11 Feb 2026 (v1), last revised 20 Mar 2026 (this version, v2)] Title:LHAW: Controllable Underspecification for Long-Horizon Tasks Authors:George Pu, Michael S. Lee, Udari Madhushani Sehwag, David J. Lee, Bryan Zhu, Yash Maurya, Mohit Raghavendra, Yuan Xue, Samuel Marc Denton View a PDF of the paper titled LHAW: Controllable Underspecification for Long-Horizon Tasks, by George Pu and 8 other authors View PDF HTML (experimental) Abstract:Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability to reason through ambiguous situations in which clarification seeking is necessary to ensure correct task execution. However, progress is limited by the lack of scalable, task-agnostic frameworks for systematically curating and measuring the impact of ambiguity across custom workflows. We address this gap by introducing LHAW (Long-Horizon Augmented Workflows), a modular, dataset-agnostic synthetic pipeline that transforms any well-specified task into controllable underspecified variants by systematically removing information across four dimensions - Goals, Constraints, Inputs, and Context - at configurable severity levels. Unlike approaches that rely on LLM predictions of ambiguity, LHAW validates variants through empirical agent trials, classifying them as outcome-critical, divergent, o...