[2603.22619] Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning

Computer Science > Artificial Intelligence arXiv:2603.22619 (cs) [Submitted on 23 Mar 2026] Title:Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning Authors:Jihyun Janice Ahn, Ryo Kamoi, Berk Atil, Renze Lou, WonWoo Kang, Heehyun Park, Sarkar Snigdha Sarathi Das, Zhuoyang Zou, Xiaoxin Lu, Yusen Zhang, Asfahan Shah, Ridwanul Hasan Tanvir, Lingxiao Zhao, Hongxi Huang, Vignesh Venkatesh, Dianjun Lin, Hamid Shah, Wentao Wang, Zhanpeng Song, Joshua Reed Bassin, Dax Patel, Ishan Appareddy Agrahar, Sahil Pardasani, Xin Dong, Fatemeh Rahbari, Benjamin David Rishel, Soochan Andrew Lee, Yuv Boghani, Ali B. AlNaseeb, Pranav Suby, Seokhyeon Bae, Shreya Buddharaju, Damien Kula, Soumyadeep Das, Hanyang Frank Liu, Faye Mo, Wenpeng Yin View a PDF of the paper titled Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning, by Jihyun Janice Ahn and 36 other authors View PDF HTML (experimental) Abstract: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 u...