CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning

Researchers propose CURV, a curriculum learning framework that improves chart question answering by reformulating it as multi-step visual grounded reasoning, achieving up to 20.50% improvements over baselines and up to 12.30% on real-world benchmarks. The framework, introduced in arXiv:2608.02833v1, includes a three-level curriculum dataset (CCQA) with scalable synthetic generation across diverse chart types and reasoning patterns, and is generalizable to out-of-domain multimodal reasoning tasks with up to 10.20% gains.

arXiv:2608.02833v1 Announce Type: new Abstract: Chart question answering CQA requires multimodal large language models MLLMs to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning chains. While extrinsic chain-of-thought prompting and visual cues significantly improve performance, current MLLMs lack intrinsic visual grounded reasoning capabilities, leading to inaccurate perception and reasoning disconnected from visual evidence. To address these limitations, we propose CURV, a curriculum learning framework that develops intrinsic visual reasoning capabilities by reformulating CQA as multi-step visual grounded reasoning, where each step coordinates logical reasoning with dynamic visual grounding through spatial attention concentration. To assist model learning, we further introduce CCQA, a three-level curriculum dataset with scalable synthetic generation across diverse chart types and reasoning patterns. Our curriculum systematically progresses from basic single-operation reasoning to complex multi-chart compositional tasks. Experiments demonstrate that CURV achieves up to $\uparrow20.50\%$ improvements over baselines and is generalizable to real-world benchmarks up to $\uparrow12.30\%$ and out-of-domain multimodal reasoning tasks up to $\uparrow10.20\%$ , validating the effectiveness of internalizing visual reasoning with dynamic grounding for enhanced chart understanding capabilities. Code is available at: https://xhguo7.github.io/CURV/.