Do LoRA Read Directions Encode Visual Concepts?

TLDR I compare the semantic coherence of read directions learned by standard, ReLU, and TopK LoRA adapters with random directions in CLIP’s residual stream. Clarity, a measure of semantic coherence, is concentrated at the positive and negative extremes of the activation distribution. Random directions can occasionally produce highly coherent examples, so a convincing activation grid alone does not show that a concept was learned. However, learned directions are more consistently coherent: 86% of standard-LoRA, 95% of ReLU-LoRA, and 63% of TopK-LoRA directions exceed the median of their matched random-direction baseline. TopK LoRA shows substantially greater variability. Its high-Clarity directions are almost exclusively rarely activated, although rare activation is not sufficient for high Clarity. Overall, learning increases semantic coherence. Especially for standard and ReLU LoRA, but coherence alone does not establish that a direction represents a distinct or functionally important concept. Introduction Low-rank adaptation (LoRA) is widely used to adapt foundation models because it introduces relatively few trainable parameters. Most work on LoRA focuses on two practical questions: How efficiently can a model be adapted, and how much does its downstream performance improve? A less studied question is what the learned adapter components represent. Meanwhile, mechanistic interpretability research often tries to decompose model activations into interpretable features, for ex…