In this work we take a step towards understanding and mitigating the vision-language performance gap, but there's still more to explore!

This was an awesome collaboration w\ Yossi Gandelsman, @boknilev.bsky.social, led by Yaniv Nikankin 🤩

Paper and code: technion-cs-nlp.github.io/vlm-circuits...

By simply patching visual data tokens from later layers back into earlier ones, we improve of 4.6% on average - closing a third of the gap!

4. Zooming on data positions, we show that visual representations gradually align with their textual analogs across model layers (also shown by
@zhaofeng_wu
et al.). We hypothesize this may happen too late in the model to process the information, and fix it with back-patching.

3. Data sub-circuits, however, are modality-specific; Swapping them significantly degrades performance. This is critical - this highlights that the differences in data processing are a key factor in the performance gap.

2. Structure is only half the story: different circuits can still implement similar logic. We swap sub-circuits between modalities to measure cross-modal faithfulness.
Turns out, query and generation sub-circuits are functionally equivalent, retaining faithfulness when swapped!

1. Circuits for the same task are mostly structurally disjoint, with an average of only 18% components shared between modalities!
The overlap is extremely low in data and query positions, and moderate in the generation (last) position only.

We identify circuits (task-specific computational sub-graphs composed of attention heads and MLP neurons) used by VLMs to solve both variants.
What did we find? >>

Consider object counting: we can ask a VLM “how many books are there?” given either an image or a sequence of words. Like Kaduri et al., we consider three types of positions within the input - data (image or word sequence), query ("how many..."), and generation (last token).

Thank you! Added to my reading list ☺️

Should work now!

SAEs have sparked a debate over their utility; we hope to add another perspective. Would love to hear your thoughts!

Paper: arxiv.org/abs/2505.20063
Code: github.com/technion-cs-...

Huge thanks to ‪@boknilev.bsky.social‬, ‪@amuuueller.bsky.social‬, it’s been great working on this project with you!

These findings have practical implications: after filtering out features with low output scores, we see 2-3x improvements for steering with SAEs, making them competitive with supervised methods on AxBench, a recent steering benchmark ( Wu and ‪@aryaman.io‬ et al.)

We show that high scores rarely co-occur, and emerge at different layers: features in earlier layers primarily detect input patterns, while features in later layers are more likely to drive the model’s outputs, consistent with prior analyses of LLM neuron functionality.