KnowThyself: An Agentic Assistant for LLM Interpretability

We develop KnowThyself, an agentic assistant that advances large language model (LLM) interpretability. Existing tools provide useful insights but remain fragmented and code-intensive. KnowThyself consolidates these capabilities into a chat-based interface, where users can upload models, pose natural language questions, and obtain interactive visualizations with guided explanations. At its core, an orchestrator LLM first reformulates user queries, an agent router further directs them to specialized modules, and the outputs are finally contextualized into coherent explanations. This design lowers technical barriers and provides an extensible platform for LLM inspection. By embedding the whole process into a conversational workflow, KnowThyself offers a robust foundation for accessible LLM interpretability.

Authors

• Suraj Prasai Wake Forest University
• Mengnan Du New Jersey Institute of Technology
• Ying Zhang School of Computer Science, Northwestern Polytechnical University
• Fan Yang Nanyang Technological University

Keywords

No keywords are indexed for this paper.