mlsanityData to 14 Oct 2026

KnowThyself: An Agentic Assistant for LLM Interpretability

arXiv 2511.03878 ↗First posted 5 Nov 2025cs.AI cs.IR cs.LGcited by 5

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 13 of its 21 references that are in the map. First posted on arXiv 5 Nov 2025.

Builds on

13 of 21 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  3. 2023A Comprehensive Overview of Large Language Modelscited by 648
  4. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 541
  5. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 412
  6. 2023Explainability for Large Language Models: A Surveycited by 333
  7. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  8. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 265
  9. 2024Transcoders Find Interpretable LLM Feature Circuitscited by 200
  10. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 111
  11. 2023A Survey of Large Language Models Attributioncited by 71
  12. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 46
1 more
  1. 2024LLM Attributor: Interactive Visual Attribution for LLM Generationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Modelscited by 10
  2. 2026NeuronScope: A Multi-Agent Framework for Explaining Polysemantic Neurons in Language Modelscited by 3
  3. 2026SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoringcited by 0
  4. 2026Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programmingcited by 0
  5. 2026What Process Evaluation of Coding Agents Actually Measures: Action, Task, and Step Are Three Different Levelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Because we have LLMs, we Can and Should Pursue Agentic Interpretability
  2. 2024InsightLens: Augmenting LLM-Powered Data Analysis with Interactive Insight Management and Navigation
  3. 2024AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data
  4. 2025An LLM-based Agentic Framework for Accessible Network Control
  5. 2025LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
  6. 2025Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
  7. 2025Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools
  8. 2025ChatGraPhT: A Visual Conversation Interface for Multi-Path Reflection with Agentic LLM Support