4 papers
Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
Serwan Jassim, Victoria Bosch, Daniel Anthes +2
Decoding sensory-evoked functional magnetic resonance imaging (fMRI) data is a central analytical tool in cognitive neuroscience. Here we ask whether self-supervised learning (SSL)…
Pixels to Principles: Probing Intuitive Physics Understanding in Multimodal Language Models
Mohamad Ballout, Serwan Jassim, Elia Bruni
This paper presents a systematic evaluation of state-of-the-art multimodal large language models (MLLMs) on intuitive physics tasks using the GRASP and IntPhys 2 datasets. We asses…
iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs
Julius Mayer, Mohamad Ballout, Serwan Jassim +2
Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment. To help overcome these limitations, we introduce iVISPAR, an interactive multimodal…
GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models
Serwan Jassim, Mario Holubar, Annika Richter +3
This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs). This…