[2603.21022] Knowledge Boundary Discovery for Large Language Models

Computer Science > Artificial Intelligence arXiv:2603.21022 (cs) [Submitted on 14 Jan 2026] Title:Knowledge Boundary Discovery for Large Language Models Authors:Ziquan Wang, Zhongqi Lu View a PDF of the paper titled Knowledge Boundary Discovery for Large Language Models, by Ziquan Wang and 1 other authors View PDF HTML (experimental) Abstract:We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowledge boundary by automatically generating two types of questions: (i) those the LLM can confidently answer (within-knowledge boundary) and (ii) those it cannot (beyond-knowledge boundary). Iteratively exploring and exploiting the LLM's responses to find its knowledge boundaries is challenging because of the hallucination phenomenon. To find the knowledge boundaries of an LLM, the agent interacts with the LLM under the modeling of exploring a partially observable environment. The agent generates a progressive question as the action, adopts an entropy reduction as the reward, receives the LLM's response as the observation and updates its belief states. We demonstrate that the KBD detects knowledge boundaries of LLMs by automatically finding a set of non-trivial answerable and unanswerable questions. We validate the KBD by comparing its generated knowledge boundaries with manually crafted LLM benchmark datasets. Experiments show that our KBD-generated question ...