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sparse-autoencoders

topic7 events
papersTODAY 04:00 UTC

arXiv paper studies how reinforcement learning reshapes LLMs using mechanistic interpretability

A new arXiv preprint examines what large language models actually learn during reinforcement learning training, approaching the question through mechanistic interpretability rather than behavior alone. The authors argue that earlier explanations of RL's effects have mostly been behavioral, and they propose using sparse autoencoders to analyze internal changes. The work is released under a fixed-SAE track.

interpretability/reinforcement-learning/llm/sparse-autoencoders
papersTODAY 04:00 UTC

Sparse autoencoders used to probe physics-informed neural network internals

A new arXiv paper introduces PhysSAE, a method that applies sparse autoencoders to inspect what hidden layers in physics-informed neural networks actually represent. The authors aim to determine whether these networks learn localized, physically meaningful features tied to the PDE residuals they are trained on. The work falls within mechanistic interpretability research for scientific machine learning.

interpretability/sparse-autoencoders/pinns/research
papersTODAY 04:00 UTC

Sparse Autoencoders Applied to Interpret Whisper Speech Encoder Internals

A new arXiv paper examines the internal representations of Whisper, an automatic speech recognition model, by applying sparse autoencoders to its encodings. The authors note that interpretability research has focused mostly on text-based transformers, leaving speech systems comparatively unstudied. Their work aims to make the features learned by Whisper's encoder more understandable.

interpretability/speech-recognition/sparse-autoencoders/research
papersTODAY 04:00 UTC

Sparse Autoencoders Can Preserve Different Readouts at Equal Reconstruction Error

A new arXiv paper argues that matching reconstruction error and sparsity levels does not guarantee two sparse autoencoders capture the same linearly decodable information from model activations. The authors formalize this gap as a matrix-valued distortion between optimal ridge readouts and propose decoder-preserving training objectives. The work offers a way to evaluate which downstream signals survive sparse compression.

interpretability/sparse-autoencoders/research/evaluation
papersTODAY 04:00 UTC

Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models

A new arXiv paper proposes using transcoders, an alternative to sparse autoencoders, to study how vision-language models turn image inputs into text. The authors argue that sparse autoencoders decompose static representations and miss the flow of information, while transcoders can follow that transformation more directly. The method is used to locate where visual grounding happens and where hallucinated content originates in these models.

interpretability/vision-language/hallucination/research
papersSEP 10 04:00 UTC

Sparse Autoencoder Phase Diagram Shows Dominant Diffuse Phase

A new arXiv preprint maps out a phase diagram for sparse autoencoders, the tools widely used to pull interpretable features out of neural network activations. The work reports that a diffuse phase dominates the diagram, which helps explain why distinct features can be absorbed or merged when feature co-occurrence is systematic. It also engages with the MAIS-O43 open problem on controlling such feature merging.

interpretability/sparse-autoencoders/research/arxiv