arXiv:2610.20139v1 Announce Type: new
Abstract: Sparse autoencoders (SAEs) have emerged as a promising approach to mechanistic interpretability by decomposing language-model activations into sparse,...
By Salima Lamsiyah, Adelaide Danilov, Oleksandr Marchenko Breneur, Aria Nourbakhsh
arXiv:2608. 02879v1 Announce Type: new Abstract: The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability.
By Maryam Rezaee, Pooriya Safaei, Maryam Asgarinezhad, Fatemeh Seyyedsalehi
arXiv:2607. 19386v1 Announce Type: new Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores.
By Sinie van der Ben, Neele Roch, Anna Hedstr\"om, Mennatallah El-Assady
The Stream Recursion Model (SRM) is a new architecture that modifies the Hierarchical Reasoning Model to make internal computational structures more interpretable. SRM organizes computation into multiple interacting latent streams that are updated through recursive refinement, allowing direct analysis of stream dynamics, causal contribution, and routing behavior. The model achieves performance comparable to GPT‑2 on a per‑parameter basis and shows consistent, distinct behavior across streams, indicating structured specialization and interaction.
By Asael Sorensen, Charles Brock, David Chamberlain, Jennifer Minnich, Matthew Hoffman, Ramyaa Ramyaa
arXiv:2603. 21014v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information.
By Florent Draye, Vedant Palit, Abir Harrasse, Tung-Yu Wu, Jiarui Liu, Punya Syon Pandey, Roderick Wu, Chih-Hao Hsu, Terry Jingchen Zhang, Zhijing Jin, Bernhard Sch\"olkopf
arXiv:2607. 00089v1 Announce Type: new Abstract: Mechanistic interpretability has produced a rich inventory of component-level analyses that characterise what neural-network components encode and how they interact.
By Hussein Chouman, Wataru Sasaki, Tomokazu Matsui, Hirohiko Suwa, Keiichi Yasumoto