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    <abstract>Large Language Models (LLMs) are impactful yet opaque artifacts. Subsymbolic by design, they are defined by billions of numeric weights that interact in a largely inscrutable manner. Current analysis paradigms are either black-box benchmarks that test model performance on pre-defined tasks, or mechanistic interpretability approaches that trace back outputs to specific weights. Both analysis methods are limited by the experimenter’s hypothesis space - one must know what to look for to find it. In this article, we argue for a third, radically different analysis paradigm: Epistemic Twins. We propose constructing large-scale symbolic approximations of LLMs in human-readable formats. This enables the comprehensive inspection of factual knowledge (or beliefs) inherent in the model without predefining hypotheses, facilitating large-scale auditing towards better understanding and explainability.</abstract>
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%0 Conference Proceedings
%T Epistemic Twins: Enabling a Symbolic Science of Language Model Knowledge
%A Razniewski, Simon
%A Ghosh, Shrestha
%A Giordano, Luca
%A Hu, Yujia
%A Kowalzik, Josua
%A Nguyen, Tuan-Phong
%Y Kalo, Jan-Christoph
%Y Nakashole, Ndapa
%Y Razniewski, Simon
%Y Suchanek, Fabian M.
%Y Vlachos, Andreas
%Y McCallum, Andrew
%S Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-464-4
%F razniewski-etal-2026-epistemic
%X Large Language Models (LLMs) are impactful yet opaque artifacts. Subsymbolic by design, they are defined by billions of numeric weights that interact in a largely inscrutable manner. Current analysis paradigms are either black-box benchmarks that test model performance on pre-defined tasks, or mechanistic interpretability approaches that trace back outputs to specific weights. Both analysis methods are limited by the experimenter’s hypothesis space - one must know what to look for to find it. In this article, we argue for a third, radically different analysis paradigm: Epistemic Twins. We propose constructing large-scale symbolic approximations of LLMs in human-readable formats. This enables the comprehensive inspection of factual knowledge (or beliefs) inherent in the model without predefining hypotheses, facilitating large-scale auditing towards better understanding and explainability.
%U https://aclanthology.org/2026.akbc-1.1/
%P 1-7

Markdown (Informal)

[Epistemic Twins: Enabling a Symbolic Science of Language Model Knowledge](https://aclanthology.org/2026.akbc-1.1/) (Razniewski et al., AKBC 2026)

• Epistemic Twins: Enabling a Symbolic Science of Language Model Knowledge (Razniewski et al., AKBC 2026)

ACL

• Simon Razniewski, Shrestha Ghosh, Luca Giordano, Yujia Hu, Josua Kowalzik, and Tuan-Phong Nguyen. 2026. Epistemic Twins: Enabling a Symbolic Science of Language Model Knowledge. In Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), pages 1–7, Budapest, Hungary. Association for Computational Linguistics.