arXiv AI
The Standard Interpretable Model: Design interpretable machine learning methods from elementary symmetries
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The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
arXiv:2606. 12289v1 Announce Type: cross Abstract: As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations.
arXiv Machine Learning
Jul 2
The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology
arXiv:2607. 01033v1 Announce Type: new Abstract: Model organisms (MOs) - language models trained to exhibit undesired or unnatural behaviours - are frequently used as testbeds for evaluating white-box interpretability techniques.
arXiv Machine Learning
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How to train your model organism
arXiv:2610.10203v1 Announce Type: new Abstract: Model organisms of alignment-relevant behaviors (e.g., backdoors, sycophancy, spurious correlations) have emerged as a key tool for evaluating whitebox...
arXiv Machine Learning
Jun 26
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
arXiv:2606. 26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics.
Hugging Face Trending Papers
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How to train your model organism
Model organisms of alignment-relevant behaviors (e.g., backdoors, sycophancy, spurious correlations) have emerged as a key tool for evaluating whitebox interpretability techniques. We argue that the p...
arXiv Machine Learning
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From Mechanistic to Compositional Interpretability
arXiv:2605. 08934v2 Announce Type: replace Abstract: Mechanistic interpretability aims to explain neural model behaviour by reverse-engineering learned computational structure into human-understandable components.