34 citations · 45 across the 5 of their papers we have counts for
5 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…
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…