43 citations · 101 across the 10 of their papers we have counts for
10 papers
Generative UI: LLMs are Effective UI Generators
Yaniv Leviathan, Dani Valevski, Matan Kalman +10
AI models excel at creating content, but typically render it with static, predefined interfaces. Specifically, the output of LLMs is often a markdown "wall of text". Generative UI…
Prompt Repetition Improves Non-Reasoning LLMs
Yaniv Leviathan, Matan Kalman, Yossi Matias
When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens o…
Selective Attention Improves Transformer
Yaniv Leviathan, Matan Kalman, Yossi Matias
Unneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces…
Diffusion Models Are Real-Time Game Engines
Dani Valevski, Yaniv Leviathan, Moab Arar +1
We present GameNGen, the first game engine powered entirely by a neural model that also enables real-time interaction with a complex environment over long trajectories at high qual…
Block Verification Accelerates Speculative Decoding
Ziteng Sun, Uri Mendlovic, Yaniv Leviathan +4
Speculative decoding is an effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then veri…
A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation
Eyal Segalis, Dani Valevski, Danny Lumen +2
Text-to-image diffusion models achieved a remarkable leap in capabilities over the last few years, enabling high-quality and diverse synthesis of images from a textual prompt. Howe…
Face0: Instantaneously Conditioning a Text-to-Image Model on a Face
Dani Valevski, Danny Wasserman, Yossi Matias +1
We present Face0, a novel way to instantaneously condition a text-to-image generation model on a face, in sample time, without any optimization procedures such as fine-tuning or in…
Dreamix: Video Diffusion Models are General Video Editors
Eyal Molad, Eliahu Horwitz, Dani Valevski +5
Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very…
Fast Inference from Transformers via Speculative Decoding
Yaniv Leviathan, Matan Kalman, Yossi Matias
Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model. In this work we introduce speculative decoding - an algor…
UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image
Dani Valevski, Matan Kalman, Eyal Molad +3
Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, simil…