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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…
SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
Niccolo Avogaro, Nayanika Debnath, Li Mi +6
Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for vision-language models (VLMs). Unstruc…
Composition-Grounded Data Synthesis for Visual Reasoning
Xinyi Gu, Jiayuan Mao, Zhang-Wei Hong +5
Pretrained multi-modal large language models (MLLMs) demonstrate strong performance on diverse multimodal tasks, but remain limited in reasoning capabilities for domains where anno…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…