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

2026-02-01

AI Summary

This study addresses the limitations of existing static evaluation methods in systematically probing the boundaries and depth of knowledge embedded within large language models (LLMs). To this end, the authors propose an interactive agent framework that integrates four adaptive exploration strategies—among which recursive categorization is introduced for the first time—and a three-stage knowledge processing pipeline involving vector deduplication, LLM-mediated arbitration of semantic overlaps, and domain relevance filtering. Experimental results reveal a strong scaling law between model size and extractable knowledge volume, measurable differences in knowledge distribution across model families attributable to distinct training data, and superior performance of the recursive categorization strategy over alternatives. Furthermore, a clear trade-off emerges between domain-specific and general-purpose models in knowledge extraction efficacy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundaries extend. Existing benchmarks are mostly static and provide limited support for systematic knowledge probing. In this paper, we propose an interactive agentic framework to systematically extract and quantify the knowledge of LLMs. Our method includes four adaptive exploration policies to probe knowledge at different granularities. To ensure the quality of extracted knowledge, we introduce a three-stage knowledge processing pipeline that combines vector-based filtering to remove exact duplicates, LLM-based adjudication to resolve ambiguous semantic overlaps, and domain-relevance auditing to retain valid knowledge units. Through extensive experiments, we find that recursive taxonomy is the most effective exploration strategy. We also observe a clear knowledge scaling law, where larger models consistently extract more knowledge. In addition, we identify a Pass@1-versus-Pass@k trade-off: domain-specialized models achieve higher initial accuracy but degrade rapidly, while general-purpose models maintain stable performance during extended extraction. Finally, our results show that differences in training data composition lead to distinct and measurable knowledge profiles across model families.

Problem

• knowledge boundary
• large language models
• knowledge probing
• systematic extraction
• knowledge coverage

Innovation

• interactive agentic framework
• knowledge boundary probing
• adaptive exploration policies
• knowledge extraction pipeline
• knowledge scaling law