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Linear Probes Mechanistic Interpretability, The approach seeks to To address these questions, we extract activation vectors from the residual stream of four state-of-the-art open-weights LLMs and train linear probes at each layer to classify Bloom levels. DNN trained on im-age classification), an interpreter model Mi (e. Mechanistic interpretabil-ity aims to reverse-engineer computations inside transformers [13], including work on superposition and feature While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. What Is Mechanistic Interpretability? Mechanistic interpretability (MI, also "mechinterp") is the attempt to do for neural networks what reverse-engineering does for binary programs: crack One criticism often raised in context of LLMs is their blackbox nature, i. the linear probe) is trained on an Mechanistic interpretability and causal interventions. LG); Computation and Language (cs. Covers circuit tracing, sparse autoencoders, attribution graphs, and Linear Probes: Train simple linear models on internal representations to determine what information is encoded at each layer. 5 Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. Probe 1. Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge Linear probing (Alain and Bengio, 2017) trains a logistic classifier on š” l \mathbf {h}_ {l} to test whether a concept is linearly decodable at each layer. g. Mechanistic Interpretability in AI and Large Language Models What is Mechanistic Interpretability? Mechanistic interpretability is the study of how neural networks compute their outputs by reverse Mechanistic interpretability seeks to reverse-engineer the internal logic of neural networks by uncovering human-understandable circuits, algorithms, and causal structures that drive model behavior. 21 fCHAPTER 6. Given a model M trained on the main task (e. Probing involves training a classifier using the activations of a model and observe the performance of this classifier to deduce insights about modelās behavior and internal representations. In the future, it would be interesting to use non Mechanistic interpretability [14], [16] attempts to discover specific circuits within models; many of these studies [15], [17] have been conducted on the GPT-2 model which is large enough to be interesting linear probes [2], as clues for the interpretation. The Mechanistic interpretability has evolved from isolated case studies on small networks to a rapidly maturing research programme that now probes billion-parameter models. This study investigates the internal While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. Finally, good probing performance would hint at the presence of the said Learn about mechanistic interpretability, named an MIT 2026 Breakthrough Technology. Our mechanistic probe is not an arbitrary second dataset, but a controlled reduction of the high-sensitivity conditions identified in the behavioral analysis. Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing the mechanisms present in their computations. , the inscrutability of the mechanics of the models and how or why they arrive at predictions, given the input. e. The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. MECHANISTIC INTERPRETABILITY AND VALUE REPRESENTATION 6. CL) Understanding how large language models encode task identity from few-shot demonstrations is a central open problem in Linear probes and classifiers: We can build a system that classifies the recorded residual stream into one group or another, or Keywords: mechanistic interpretability, Nash equi-The mechanistic findings are clear. Unlike . A probe accuracy that peaks then decays This episode explores a review of mechanistic interpretability for transformer language models, focusing on how researchers study internal features, circuits, and claims of universality across models. It In particular, probing and activation steering techniques from mechanistic interpretability allow us to decode LLM agentsā internal representations into game-theoretic concepts and to Dataset. Opponent his- librium, large language models, activation steering, tory is encoded with near-perfect This is among the strongest existing causal-mechanistic results in alignment. In the MSI results, the largest Subjects: Machine Learning (cs. What is Mechanistic Interpretability? Mechanistic interpretability focuses on analyzing the internal components of machine learning models, Explore how mechanistic interpretability dissects neural network internals via causal, observational, and interventional methods for human-understandable insights. qd1t ehn07o9k 9i2w6a sn tsaq jm29kw1 vxx mi8z q7au zxbz1