Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative approaches and then guiding parameter-efficient Supervi...
arXiv:2606. 09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL.
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.
arXiv:2606.09396v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement...
S^3martCirc is a self‑supervised framework that jointly discovers and interprets neural circuits in large language models, rather than treating circuit discovery and functional interpretation as separate stages. It abstracts node behavior into two general computational roles that generalize across tasks and introduces a quantitative metric for assigning these roles, enabling simultaneous identification of importance and function. Experiments demonstrate that S^3martCirc outperforms existing methods in circuit discovery.
arXiv:2602. 20062v2 Announce Type: replace Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems.
arXiv:2609.15533v1 Announce Type: cross Abstract: Mechanistic interpretability has become the dominant lens for understanding frontier language models, as their inner workings are complex and inheren...
The paper introduces a controlled auditing framework using parametric CAD to separate failures in encoding, expression, and activation-level control in frozen large language models (LLMs). Applied to eight pretrained models and random controls, it finds that pretraining mainly improves linear decoding of local relations, while global constraint status is already highly decodable from random representations. Despite strong local decodability, the models’ zero‑shot predictions lag far behind supervised probe performance, and simple activation steering fails to provide reliable control; however, low‑rank fine‑tuning with only 400 labeled relation pairs can substantially improve generation on three models, indicating that the representation–behavior gap can be narrowed through adaptation.
arXiv:2609.24379v1 Announce Type: cross Abstract: Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entan...
The paper introduces a post‑hoc Feature Selection Layer (FSL) that can be added to frozen pre‑trained neural networks on tabular data to highlight the most important features. This lightweight, trainable module fine‑tunes feature weights based on the original model’s outputs without altering its architecture or parameters. Experiments on synthetic and real‑world datasets show that the post‑hoc FSL preserves predictive performance while improving interpretability, outperforming other post‑hoc methods such as Integrated Gradients and SHAP.
arXiv:2505. 17630v4 Announce Type: replace-cross Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis.
Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration. Finding the regime and boundaries of successful steering typically requires expensive grid searches and post-hoc evaluation of full autoregressive rollouts.