collaborators

6 papers

cs.AI2026

Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems

Yarden Bakish, Amir Dudai, Roy Ganz +4

Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure at…

cs.AI2026

DREAM: Deep Research Evaluation with Agentic Metrics

Elad Ben Avraham, Changhao Li, Ron Dorfman +8

Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research…

cs.CV2024

DocVLM: Make Your VLM an Efficient Reader

Mor Shpigel Nacson, Aviad Aberdam, Roy Ganz +5

Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing. While typical visual tasks p…

cs.CV2024

TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models

Jonathan Fhima, Elad Ben Avraham, Oren Nuriel +4

Vision-Language (VL) models have garnered considerable research interest; however, they still face challenges in effectively handling text within images. To address this limitation…

cs.CV2024

Question Aware Vision Transformer for Multimodal Reasoning

Roy Ganz, Yair Kittenplon, Aviad Aberdam +4

Vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a…

cs.CL2024

GRAM: Global Reasoning for Multi-Page VQA

Tsachi Blau, Sharon Fogel, Roi Ronen +6

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading met…