Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or question answering, with limited focus on how to integrate these two modes. In this work, we present a comprehensive analysis using FaultyScience, a newly constructed large-scale, cross-disciplinary benchmark of faulty scientific questions. We show t...
| Authors | Jihyun Janice Ahn, Ryo Kamoi, Berk Atil et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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