Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction

A new interactive agentic framework aims to systematically extract and quantify the knowledge stored within Large Language Models, revealing how far their parametric knowledge extends and how it degrades under pressure. The framework, detailed in a paper revised on 26 May 2026, treats LLMs as compressed knowledge bases and deploys four adaptive exploration policies to probe their contents at varying levels of granularity [1][2]. A three-stage knowledge processing pipeline then filters the output, using vector-based filtering to remove strict duplicates, LLM-based adjudication to resolve ambiguous semantic overlap, and domain relevance auditing to retain only valid knowledge units [2]. The paper’s authors, including Yuheng Yang, report that a strategy called Recursive Taxonomy proved the most effective exploration method across their experiments [1][2]. A key finding is a clear knowledge scaling law: larger models consistently recover more knowledge than smaller ones [1][2]. The research also documents a Pass@1 versus Pass@k trade-off. Domain-specialized models achieve higher initial accuracy but experience rapid degradation, while general-purpose models maintain stable performance over extended extraction runs [2]. The team further found that differences in training data composition produce distinct and measurable knowledge profiles across model families, reflecting how pretraining shapes each model’s parametric knowledge [2]. This work addresses a known limitation in the field. High-quality labeled training datasets for supervised and semi-supervised machine-learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data [3]. The proposed framework offers a method to audit what a model has already internalized without requiring new static benchmarks, which the authors argue provide limited support for systematic knowledge probing [1][2]. The paper was submitted on 1 Feb 2026 and its latest version is 226 KB [1].