3 papers
cs.CV2026
LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
James Flora, Kowshik Thopalli, Akshay R. Kulkarni +2
We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By combining sparse autoencode…
cs.LG2026
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
Akshay Kulkarni, Tsui-Wei Weng, Vivek Narayanaswamy +3
Sparse autoencoders (SAEs) promise a unified approach for mechanistic interpretability, concept discovery, and model steering in LLMs and LVLMs. However, realizing this potential r…
cs.CL2025
Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Concepts
Xinyuan Yan, Shusen Liu, Kowshik Thopalli +1
Sparse autoencoders (SAEs) have emerged as a powerful tool for uncovering interpretable features in large language models (LLMs) through the sparse directions they learn. However,…