Ahead of Rosh Hashanah, we're publishing the TLV AI 50: a list of the 50 most influential Israeli AI researchers and founders. Their ideas shape how modern models think, see, and reason, and their companies turn those ideas into the chips, companies, and products the world runs on. Full list: eu1.hubs.ly/H0y9XRZ0 30 researchers, 20 founders, 7 papers. This is not a ranking, but rather a representation of the most impactful Israelis in the AI research realm. Here’s a few trends that stood out: •⁠  ⁠Israeli neolabs are finally emerging at the model layer •⁠  ⁠AI security: a new cohort of AI natives is defining how systems get hardened and verified - Israel is firmly at the frontier •⁠  ⁠Vision: some of the most cited labs anywhere, with startups now harnessing that talent for physical AI Also featured are 7 noteworthy papers from up-and-coming Israeli researchers, below the list.

🥳 Excited to share UniProbe: A learnable, token-level hallucination detection for large VLMs 🔎 UniProbe looks inside a VLM’s internals to identify hallucinated response tokens and mitigate them before they reach the user. 📉 55% fewer hallucinations ⚡ Just 1.06× latency

Incredible AI parkour simulation from @NVIDIAAI. It learned to be a pro from looking at just 30 seconds of footage! ▶️Full video: piped.video/8B05cy3UuSE

דברים זזים במטה הלאומי לבינה מלאכותית. זה לא קצב ממשלתי. זה קצב אחר. אתמול פרסם המטה הלאומי לבינה מלאכותית יחד עם החשב הכללי שלושה RFIs חשובים מאוד שמוציאים לדרך את שלושה פרויקטים גדולים של התכנית הלאומית לבינה מלאכותית: 1. הקמת מחשב קוונטי לאומי 2. ⁠הקמת עיר התנסויות ל physical AI 3. ⁠מודל שפה לאומי כחול לבן בדגש לסייבר. 1. פיזילנד: hazira.gpa.gov.il/subdomain/… 2. מודל נולד: hazira.gpa.gov.il/subdomain/… 3. mr.gov.il/ilgstorefront/he/p…

1/ Text-to-video models generate beautiful videos, but they still struggle to follow complex prompts. Relations like left/right, towards/away, on top of, behind, or even multi-stage instructions are often generated incorrectly. In our new paper, accepted to SIGGRAPH Asia 2026, we introduce CVG, an inference-time guidance method that improves compositional video generation without retraining or architectural changes.

Long due. Next: add small submission fees growing with number of papers

ICLR 2027 has authorship quotas: - No more than 20 submissions per author - No more than 1 submission where no author has a paper previously accepted to a major ML conference

🚀 Excited to share our new paper: “Fast Autoregressive Video Diffusion & World Models with Temporal Cache Compression & Sparse Attention.” We address attention bottlenecks in auto-regressive video diffusion, enabling ×5–×10 speedup and constant memory over long rollouts.

I'm going to present Fast-AR at #ICML26 tomorrow (Tuesday) @ 2pm - poster #1204! Come and say hi 👋 If you're around this week, feel free to DM me. Project page: dvirsamuel.github.io/fast-au… Details below ⬇️

Physical AI Day is coming to #SIGGRAPH2026. 🤩 Learn how NVIDIA research is advancing physical AI for robotics, autonomous vehicles, and industrial digital twins. Don't miss sessions including: 👉 OpenUSD for AI Agents, Model-Driven Workflows, and 3D Graphics Pipelines 👉 Building SimReady 3D Worlds for Physical AI 👉 Career Growth in Physical AI: Bringing 3D Graphics Skills into Robotics and Digital Twins Learn more: nvda.ws/4eJxeR0

We've released an update to ProtoMotions! nvlabs.github.io/ProtoMotion… Most importantly, this release was built with Yifeng Jiang, @YiShi_333 , @erwincoumans and @xbpeng4 , whose work shaped everything from the core methods to the final code release. 1/5

If you're in the area, don't miss this event! I'll be there to chat and show interactive demos of our recent work.

🎉 Excited to introduce TRON, a relighting framework for 3D captures. 💡TRON pairs a neural renderer with 3D Gaussian reconstructions, achieving realistic quality, with 3D, material, & lighting control at interactive frame rates. arxiv.org/abs/2606.11314 research.nvidia.com/labs/sil…

🎉 Happy to share our latest work: Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching (accepted to #ICML2026)! TL;DR: We train image and video editing models without any paired data. No ground-truth edit data, no external reward models.

Thank you for explaining our work so wonderfully well!

It's amazing to see how far ProtoMotions has come since it's first release. If you are looking for a feature-rich and scalable framework that can train controllers on massive datasets, then checkout ProtoMotions!

At @nvidia, we built ProtoMotions to help us, and researchers world-wide, innovate quickly without compromising on applicability. We're proud to announce ProtoMotions3 -- our biggest release yet! 🧵👇

🎉 I am excited to present our new paper! Our paper improves personalization of text-to-image models, by adding one special cleaning step on top of existing personalized models. With just a single gradient update (~4 seconds on an NVIDIA H100 GPU) and a single image of the target concept, our method improves both text alignment and image alignment. For example, it improves LoRA by (+7% / +14%). This is achieved by adding new loss terms and taking into account the prompt and seed. This work was done together with @dvir_samuel and @GalChechik. 🌐 Paper page: per-query-visual-concept-lea… 📄 arXiv paper: arxiv.org/abs/2508.09045 More details in the comments below.

What if video editing took seconds instead of hours? OmnimatteZero – by @dvir_samuel Prof. @GalChechik from @Bar_ilan, HUJI & OriginAI – Removes dynamic objects with their shadows and reflections, separates layers, and reinserts them in real time. 👉 arxiv.org/abs/2503.18033

How do generative models represent the notion of "an object"? In early 20th century, Gestalt psychologist studied this question for human perception. This paper now looks into related mechanisms in text-to-video models.