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cs.CV2026
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Xuehang Guo, Pingyue Zhang, Ruiyi Zhang +6
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate…
cs.CV2025
EMCompress: Video-LLMs with Endomorphic Multimodal Compression
Zheyu Fan, Jiateng Liu, Yuji Zhang +4
Video-LLMs face a fundamental tension in long-video reasoning: static, sparse frame sampling either dilutes evidence across task-irrelevant segments at significant cost or misses f…