This work introduces SQUARE-Bench, a comprehensive benchmark that systematically evaluates LMM capabilities as evaluators of AI-generated images across four aspects: Semantics, Quality, Authenticity, and Responsibility, and conducts a proof-of-concept study of LMM-guided iterative editing.
Yu Zhao, Jia-Rui Wang, Hui-Yu Duan et al.· 0 citations
TREND-10K is introduced, a next-generation comprehensive VQA dataset consisting of the trend-driven part and the static part, containing $10,000$ videos across a wide spectrum of content types, and presents a robust framework for advancing VQA, addressing challenges caused by the temporal evolution of user perceptual h...
Zi-Heng Jia, Zi-Cheng Zhang, Jun-Qi Zhang et al.· 0 citations
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
Lei Bai, Jiaqi Cao, Chiyu Chen et al.· 3 citations
This work presents Memory Decoder at Scale, scaling memory models up to 6.9B parameters and pretraining them on 300B tokens, demonstrating that independently scaling pretrained memory offers a more parameter efficient path to improving language model performance.
Ru-Bin Wei, Jiaqi Cao, Jiarui Wang et al.· arXiv.org· 1 citation
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