LHAW is a modular, dataset-agnostic synthetic pipeline that transforms well-specified tasks into controllable underspecified variants by systematically removing information across four dimensions—Goals, Constraints, Inputs, and Context—at configurable severity levels.
This dataset release contains 285 underspecified task variants derived from TheAgentCompany, SWE-Bench Pro, and MCP-Atlas, and is used to study how current agents… See the full description on the dataset page:
https://huggingface.co/datasets/ScaleAI/lhaw.
