# 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 Bridges (ICML 2026). Robotics. Drones that shape their own rotor noise to localize themselves, and a growing interest in world models for robots. Papers: Rotor-noise localization (IROS 2024). In progress: Autoregressive video generation. Generating long videos one chunk at a time while keeping them consistent. In progress: Diffusion distillation. Distilling diffusion models into faster generators. In progress: Evidence aggregation in Transformers. Helping Transformers combine many pieces of evidence, the failure HERBench (https://gabrieleserussi.github.io/research/herbench/) exposed in Video-LLMs. In progress: Disaggregated inference. Serving large models efficiently across different kinds of hardware. Newest direction: memory for world models, deciding what a generator or a robot should keep when it cannot keep everything. ## Research interests - 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 ## Experience and education - Research Intern, real-time video generation, Decart (2026 to present) - Research Intern, video retrieval and multimodal fusion of vision and radar, supervised by Prof. Chaim Baskin and Dotan Di Castro, Bosch Center for Artificial Intelligence (2024 to 2026) - PhD, Electrical and Computer Engineering, INSIGHT Lab, advised by Prof. Chaim Baskin, Ben-Gurion University of the Negev (2024 to present) - MSc, Computer Science, advised by Prof. Alex Bronstein and Prof. Chaim Baskin, Technion, Israel Institute of Technology (2021 to 2024) - BSc, Computer Engineering, Sapienza University of Rome (2018 to 2021) ## Grants and awards - Adversarial Evaluation and Hardening of Generative AI Trustworthiness, Ministry of Innovation, Science and Technology (Israel) (2026 to 2029). Research grant from Israel's Ministry of Innovation, Science and Technology, won in a competitive national call with Prof. Chaim Baskin as PI, to build methods and an open-source toolkit that find and fix the failures of generative models. I helped shape the research plan and aims, wrote major sections of the proposal and now work on the project. ## Selected research - [HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering](https://gabrieleserussi.github.io/research/herbench/): CVPR 2026, co-first author. HERBench forces video models to aggregate evidence: each of its 26,806 questions needs at least three separate cues from distinct parts of the video, and 13 state-of-the-art Video-LLMs reach only 31 to 42% (random is 20%). - [Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges](https://gabrieleserussi.github.io/research/sdb/): ICML 2026. Structured Diffusion Bridges reach near fully paired quality in modality translation with half the paired data, 25.2 PSNR against 25.6 for a fully paired baseline on zero-shot super-resolution, and still learn with no pairs at all. - [HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering](https://gabrieleserussi.github.io/research/himu/): WACV 2027. With 16 frames chosen by HiMu, a training-free frame selector for long-video question answering, Qwen3-VL-8B scores 73.2% on Video-MME, above its 71.7% with 64 uniformly sampled frames. - [Active propulsion noise shaping for multi-rotor aircraft localization](https://gabrieleserussi.github.io/research/ears/): IROS 2024, oral pitch, co-first author. Also presented at ICML 2024 Workshop (AI4Science). Learning a drone's rotor phases together with a neural localizer lets it localize itself from its own propulsion noise, more than 2.7 times better in simulation than the best handcrafted modulation. - [VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval](https://gabrieleserussi.github.io/research/vedje/): ICML 2026 Workshop (AdaptFM), co-first author. VEDJE matches a 7.6B-parameter multimodal LLM retriever on MSR-VTT text-to-video retrieval (59.8 against 59.7 R@1) with about 157M online parameters, roughly 48 times fewer, by encoding each video once into a compact cache read by a 33M-parameter reranker. - [PREGEN: Uncovering Latent Thoughts in Composed Video Retrieval](https://gabrieleserussi.github.io/research/pregen/): arXiv 2026, co-first author. PREGEN reaches 99.73 Recall@1 on WebVid-CoVR, 27.23 points above the previous best, by reading a frozen VLM's hidden states with a lightweight encoder for composed video retrieval. - [All publications](https://gabrieleserussi.github.io/publications/) ## Elsewhere - [Google Scholar](https://scholar.google.com/citations?user=GJ19YUEAAAAJ&hl=en) - [GitHub](https://github.com/GabrieleSerussi) - [X](https://x.com/GSerussi) - [LinkedIn](https://www.linkedin.com/in/gabriele-serussi) - [ORCID](https://orcid.org/0009-0001-4280-4918) - [OpenReview](https://openreview.net/profile?id=~Gabriele_Serussi1) - [DBLP](https://dblp.org/pid/371/3989.html) - [Semantic Scholar](https://www.semanticscholar.org/author/2288184194) - [Hugging Face](https://huggingface.co/GSerussi) - [alphaXiv](https://www.alphaxiv.org/@gabriele-serussi) ## Optional - [Full profile with every paper and its BibTeX](https://gabrieleserussi.github.io/llms-full.txt)