58 citations · 120 across the 59 of their papers we have counts for
63 papers
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair
Elad Sharony, Ido Greenberg, Jialin Song +3
Large language model (LLM) code-repair systems fix faulty programs by repeatedly generating and testing revisions. Each revision is costly, making the choice of which repair to pur…
Look Where You Say You're Looking: Self-Grounded Attention for Visual Reasoning
Uri Berger, Gal Chechik, Gal Dalal
We introduce Self-Saliency, a method for training Vision-Language Models (VLMs) to increase the alignment between their visual attention and the image regions mentioned in their re…
All modalities are equal, but video is more equal: Closing the Cross-Attention Gap in Joint Video Generation
Ohad Rahamim, Dvir Samuel, Idan Schwartz +1
Video is a rich representation of a physical event, capturing appearance, geometry, motion, and temporal evolution. Other modalities, such as 3D body motion or audio, encode narrow…
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4
Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mi…
Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
Yoad Tewel, Yuval Atzmon, Gal Chechik +1
Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits sca…
Fast 4D Mesh Generation by Spatio-Temporal Attention Chains
Dvir Samuel, Yuval Atzmon, Gal Chechik +1
4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and dif…
Compositional Video Generation via Inference-Time Guidance
Ariel Shaulov, Eitan Shaar, Amit Edenzon +2
Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attribut…
Simulating clinical interventions with a generative multimodal model of human physiology
Guy Lutsker, Gal Sapir, Jordi Merino +7
Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer…
More Test-Time Compute Can Hurt: Overestimation Bias in LLM Beam Search
Gal Dalal, Assaf Hallak, Gal Chechik +1
Wider beam search should improve LLM reasoning, but when should you stop widening? Prior work on beam width selection has focused on inference efficiency \citep{qin2025dsbd, freita…
Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces
Eitan Shaar, Ariel Shaulov, Yalcin Tur +2
Adversarial attacks are a central tool for probing the robustness of modern vision models, yet most methods optimize perturbations directly in pixel space under or $\…
Spanning the Visual Analogy Space with a Weight Basis of LoRAs
Hila Manor, Rinon Gal, Haggai Maron +2
Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words.…
Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
Dvir Samuel, Issar Tzachor, Matan Levy +3
Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their…
LR-DWM: Efficient Watermarking for Diffusion Language Models
Ofek Raban, Ethan Fetaya, Gal Chechik
Watermarking (WM) is a critical mechanism for detecting and attributing AI-generated content. Current WM methods for Large Language Models (LLMs) are predominantly tailored for aut…
Expert Merging in Sparse Mixture of Experts with Nash Bargaining
Dung V. Nguyen, Anh T. Nguyen, Minh H. Nguyen +6
Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a prin…
Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image
Sapir Esther Yiflach, Yuval Atzmon, Gal Chechik
Text-to-image diffusion models can generate stunning visuals, yet they often fail at tasks children find trivial--like placing a dog to the right of a teddy bear rather than to the…
Per-Query Visual Concept Learning
Ori Malca, Dvir Samuel, Gal Chechik
Visual concept learning, also known as Text-to-image personalization, is the process of teaching new concepts to a pretrained model. This has numerous applications from product pla…
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
Anum Afzal, Florian Matthes, Gal Chechik +1
We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representat…
Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning
Zhengyi Luo, Chen Tessler, Toru Lin +8
Human behavior is fundamentally shaped by visual perception -- our ability to interact with the world depends on actively gathering relevant information and adapting our movements…
Policy Optimized Text-to-Image Pipeline Design
Uri Gadot, Rinon Gal, Yftah Ziser +2
Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even…
MaskedManipulator: Versatile Whole-Body Manipulation
Chen Tessler, Yifeng Jiang, Erwin Coumans +3
We tackle the challenges of synthesizing versatile, physically simulated human motions for full-body object manipulation. Unlike prior methods that are focused on detailed motion t…
Efficient GNN Training Through Structure-Aware Randomized Mini-Batching
Vignesh Balaji, Christos Kozyrakis, Gal Chechik +1
Graph Neural Networks (GNNs) enable learning on realworld graphs and mini-batch training has emerged as the de facto standard for training GNNs because it can scale to very large g…
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms
Ido Greenberg, Piotr Sielski, Hugo Linsenmaier +5
Vehicle Routing Problems (VRP) are an extension of the Traveling Salesperson Problem and are a fundamental NP-hard challenge in combinatorial optimization. Solving VRP in real-time…
Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds
Eitan Shaar, Ariel Shaulov, Gal Chechik +1
In the domain of audio-visual event perception, which focuses on the temporal localization and classification of events across distinct modalities (audio and visual), existing appr…
TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
Dana Cohen-Bar, Daniel Cohen-Or, Gal Chechik +1
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-…