Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent understanding of evolving world state. Building such a benchmark from real-world videos is challenging because the underlying world state is pa...
Kuan Zhang, Yu-Kun Chen, Zhi-Hao Yang et al.· 0 citations
Multimodal large language models can expose task-relevant evidence through generation, but producing useful evidence does not by itself determine how it enters a retrieval embedding. We study whether the semantic organization of that evidence can also specify where representations are read. To address this question, we...
Zi-Rong Chen, Fu-Da Ye, En-Jun Du et al.· 1 citation
This work forms data rewriting as a policy-learning problem and trains a lightweight LoRA rewriting policy with reinforcement learning that optimizes question-answering-style distributional alignment and semantic diversity under a hard task-consistency gate, producing verified supervision for downstream SFT.
Jia-Cheng Wang, Zhi-Jie Liu, Ping Jian et al.· 0 citations
EviRank is recast multimodal image re-ranking as a semantic constraint satisfaction problem and proposed, which parses any query - text-only, image-only, or composed - into a unified evidence package: typed criteria across six semantic slots, each labelled required, forbidden, or ignorable.
Enjun Du, Si-Yi Liu, Zi-Rong Chen et al.· 2 citations
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