Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain underexplored. A fundamental question arises: to what extent do LVLMs rely on visual input, and which image regions contribute to their responses? It is non-trivial to interpret the free-form generation of LVLMs due to their complicated visual architecture (e.g., multiple encoders and multi-resolution) and variable-length outputs. In this paper, we extend existing heatmap visualization methods (e.g., iGOS++) to support LVLMs for open-ended visual question answering. We propose a method to select visually relevant tokens that reflect the relevance between generated answers and input image. Furthermore, we conduct a comprehensive analysis of state-of-the-art LVLMs on benchmarks designed to require visual information to answer. Our findings offer several insights into LVLM behavior, including the relationship between focus region and answer correctness, differences in visual attention across architectures, and the impact of LLM scale on visual understanding. The code and data are available at this https URL. — Read More
Daily Archives: September 3, 2025
How To Become A Mechanistic Interpretability Researcher
Mechanistic interpretability (mech interp) is, in my incredibly biased opinion, one of the most exciting research areas out there. We have these incredibly complex AI models that we don’t understand, yet there are tantalizing signs of real structure inside them. Even partial understanding of this structure opens up a world of possibilities, yet is neglected by 99% of machine learning researchers. There’s so much to do!
think mech interp is an unusually easy field to learn about on your own: there’s a lot of educational materials, you don’t need too much compute, and there’s short feedback loops. But if you’re new, it can feel pretty intimidating to get started. This is my updated guide on how to skill up, get involved, reach the point where you can do actual research, and some advice on how to go from there to a career/academic role in the field.
This guide is deliberately highly opinionated. My goal is to convey a productive mindset and concrete steps that I think will work well, and give a sense of direction, rather than trying to give a fully broad overview or perfect advice. — Read More
Parallel AI Agents Are a Game Changer
I’ve been in this industry long enough to watch technologies come and go. I’ve seen the excitement around new frameworks, the promises of revolutionary tools, and the breathless predictions about what would “change everything.” Most of the time, these technologies turned out to be incremental improvements wrapped in marketing hyperbole.
But parallel agents? This is different. This is the first time I can say, without any exaggeration, that I’m witnessing technology that will fundamentally transform how we develop software. — Read More
Rick's Cafe AI 