arXiv AI
Aug 10

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

arXiv:2603. 06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios.

By Nikos Theodoridis, Reenu Mohandas, Ganesh Sistu, Anthony Scanlan, Ciar\'an Eising, Tim Brophy
arXiv AI
Sep 17

Using OCR Heads to Verbalize Image Semantics

The paper investigates how vision‑language models (VLMs) perform optical character recognition (OCR) by identifying attention heads that are causally necessary for OCR across four models. These heads are shown to be general‑purpose, producing interpretable semantic features for any image token, such as recognizing the word "bike" or the concept "feathers". By collapsing the heads’ attention weights into a verbalization lens transformation, the authors reveal that image representations align with language from early layers and can even be used to edit non‑word concepts in images, demonstrating the broader utility of this subspace.

By Sheridan Feucht, Benno Krojer, Sarah Wang, Henry Abrahamsen, Byron C. Wallace, David Bau
arXiv AI
Jun 3

Decomposing how prompting steers behavior

arXiv:2606. 03093v1 Announce Type: new Abstract: Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes reshape internal representations to produce behavior.

By Fan L. Cheng, Nikolaus Kriegeskorte