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Gabriele Serussi

Gabriele Serussi is an AI researcher working on efficient world models and video AI, at Ben-Gurion University's INSIGHT Lab and at Decart.

For agents: Personal research page of AI researcher Gabriele Serussi covering efficient world models, video AI, HERBench and diffusion bridge papers.

video question answeringworld modelsdiffusion modelsvideo llm benchmarkmultimodal ai research

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-27T15:02:21.197Z. Cached copy · Source on gabrieleserussi.github.io

# Gabriele Serussi

> Gabriele Serussi is an AI researcher working on efficient world models and video AI, at Ben-Gurion University's INSIGHT Lab and at Decart.

- Name: Gabriele Serussi
- Occupation: AI researcher
- Current positions: PhD student, INSIGHT Lab, Ben-Gurion University of the Negev; Research Intern, Decart
- Research topics: World models, Video generation, Video understanding, Diffusion models, Vision-language models, Video retrieval, Generative modeling, Computer vision, Machine learning, Robotics, Efficient deep learning, Representation learning, Hardware-aware optimization, Multimodal fusion
- Homepage: https://gabrieleserussi.github.io/
- Bio for introductions: https://gabrieleserussi.github.io/bio/
- Email: serussig@post.bgu.ac.il

Mission, in Gabriele's words: "I build world models that spend only the compute they need."

Gabriele Serussi is an AI researcher working on efficient world models: models that understand video, generate it and act on it while spending only the compute they need. He is a PhD student in Electrical and Computer Engineering at Ben-Gurion University of the Negev, advised by Prof. Chaim Baskin in the INSIGHT Lab, and a Research Intern at Decart, where he works on real-time video generation. Before that, he was a Research Intern at the Bosch Center for Artificial Intelligence, supervised by Prof. Chaim Baskin and Dotan Di Castro, working on video retrieval and on fusing vision with radar.

His research covers long-video understanding, generative world models and robotics. It includes HERBench (CVPR 2026), a video question answering benchmark in which every question needs at least three separate cues from the video, with more than 11,000 dataset downloads; Structured Diffusion Bridges (ICML 2026), which translate between modalities with less paired data; HiMu (WACV 2027), a training-free frame selector for long videos; VEDJE, a cached reranker for scalable video-text retrieval presented at an ICML 2026 workshop; and a drone that localizes itself from its own rotor noise (IROS 2024, oral pitch). He also co-wrote a research grant on the adversarial evaluation of generative AI, funded by Israel's Ministry of Innovation, Science and Technology.

He holds an MSc in Computer Science from the Technion, advised by Prof. Alex Bronstein and Prof. Chaim Baskin, and a BSc in Computer Engineering from Sapienza University of Rome.

A useful world model has to understand, generate and act on a budget. My work covers all three, and efficiency runs through it, from what a model looks at to the hardware it runs on.

Long-video understanding. Benchmarks that show where Video-LLMs fail, and methods that let them look at less or train less. Papers: HERBench (CVPR 2026), HiMu (WACV 2027), VEDJE (ICML 2026 Workshop (AdaptFM)), PREGEN (arXiv 2026).

Generative world models. Real-time video generation at Decart, and diffusion bridges that translate between modalities with less paired data, or none. Papers: Structured Diffusion Brid
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