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cs.CV2026
MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs
Chaoqian Mu, Wenhao Wu, Zichen Liang +4
Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large language models (MLLMs). Existing b…
cs.CV2025
Revisiting the Data Sampling in Multimodal Post-training from a Difficulty-Distinguish View
Jianyu Qi, Ding Zou, Wenrui Yan +5
Recent advances in Multimodal Large Language Models (MLLMs) have spurred significant progress in Chain-of-Thought (CoT) reasoning. Building on the success of Deepseek-R1, researche…