Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features

Computer Science > Artificial Intelligence arXiv:2604.23829 (cs) [Submitted on 26 Apr 2026] Title:Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features Authors:John Winnicki, Abeynaya Gnanasekaran, Eric Darve View a PDF of the paper titled Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features, by John Winnicki and 2 other authors View PDF HTML (experimental) Abstract:Sparse autoencoders (SAEs) extract millions of interpretable features from a language model, but flat feature inventories aren't very useful on their own. Domain concepts get mixed with generic and weakly grounded features, while related ideas are scattered across many units, and there's no way to understand relationships between features. We address this by first constructing a strict domain-specific concept universe from a large SAE inventory using contrastive activations and a multi-stage filtering process. Next, we build two aligned graph views on the filtered set: a co-occurrence graph for corpus-level conceptual structure, organized at multiple levels of granularity, and a transcoder-based mechanism graph that links source-layer and target-layer features through sparse latent pathways. Automated edge labeling then turns these graph views into readable knowledge graphs rather than unlabeled layouts. In a case study on a biology textbook, these graphs recover coherent chapter and subchapter-level structure, reveal concepts that bridge neighboring topics, and transform messy sentence-level activity containing thousands of features into compact, readable views that illustrate the model's local activity. Taken together, this reframes a flat SAE inventory as an internal knowledge graph that converts feature-level interpretability into a global map of model knowledge and enables audits of reasoning faithfulness. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.23829 [cs.AI] (or arXiv:2604.23829v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23829 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: John Winnicki [view email] [v1] Sun, 26 Apr 2026 18:17:56 UTC (17,138 KB) Full-text links: Access Paper: View a PDF of the paper titled Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features, by John Winnicki and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers…

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