Non-Gaussianity Reveals Interpretable Directions in Language Models

arXiv:2606.11722 · cs.LG, cs.AI, cs.CL · Submitted 2026-06-10 · Read on arXiv

cs.LG, cs.AI, cs.CL

Submitted: 2026-06-10

Updated: 2026-10-19

Terminology

• Interpretability — 2× in this paper · explained in 15 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Sparse Autoencoders (SAEs) — 1× in this paper · explained in 4 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks