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20242026
most citedWhat matters when building vision-language models?

18 citations · 28 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

Théophane Vallaeys, Jakob Verbeek, Matthieu Cord

Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing data dimension and redundancy. Mos…

cs.LG2025

Learning to Steer: Input-dependent Steering for Multimodal LLMs

Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +3

Steering has emerged as a practical approach to enable post-hoc guidance of LLMs towards enforcing a specific behavior. However, it remains largely underexplored for multimodal LLM…

cs.LG2025★ 1 cited

SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Mustafa Shukor, Dana Aubakirova, Francesco Capuano +11

Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than t…

cs.CV2025

Scaling Laws for Native Multimodal Models

Mustafa Shukor, Enrico Fini, Victor Guilherme Turrisi da Costa +3

Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately…

cs.AI2025★ 2 cited

Analyzing Finetuning Representation Shift for Multimodal LLMs Steering

Pegah Khayatan, Mustafa Shukor, Jayneel Parekh +2

Multimodal LLMs (MLLMs) have reached remarkable levels of proficiency in understanding multimodal inputs. However, understanding and interpreting the behavior of such complex model…