3 citations · 4 across the 5 of their papers we have counts for
5 papers
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +24
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language…
Balanced Thinking: Improving Chain of Thought Training in Vision Language Models
Shaked Perek, Ben Wiesel, Avihu Dekel +2
Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all to…
ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +12
Chart-to-code reconstruction -- the task of recovering executable plotting scripts from chart images -- provides important insights into a model's ability to ground data visualizat…
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
Ido Levy, Ben Wiesel, Sami Marreed +4
Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprise…
From Grounding to Planning: Benchmarking Bottlenecks in Web Agents
Segev Shlomov, Ben wiesel, Aviad Sela +3
General web-based agents are increasingly essential for interacting with complex web environments, yet their performance in real-world web applications remains poor, yielding extre…