While In-Context Learning (ICL) enables models to adapt from exemplars without parameter updates, multimodal ICL remains largely underexplored, particularly regarding video demonstrations in interactive environments. For multimodal agents, learning from videos presents unique challenges: they must translate in-context...
Zi-Qian Fan, Shi-Bo Xu, Jun-Jie Li et al.· 0 citations
This work presents WorldReward, a VLM-based pairwise preference reward model that unifies action-consistency and visual-quality evaluation for camera-conditioned world models, and introduces WorldReward-Bench, a human-annotated benchmark measuring reward-model agreement with human preferences across action consistency,...
Yi-Bin Wang, Ze-Han Wang, Junshu Tang et al.· 0 citations
This work proposes Hybrid-Policy Self-Distillation (HPSD), a novel self-distillation framework where a single TI2V model acts as both teacher and student under different conditions: the teacher operates in TI2V mode with a high-quality first frame and an enhanced prompt, while the student runs in the base T2V mode with...
Jia-Zi Bu, Peng-Yang Ling, Yu-Jie Zhou et al.· 2 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
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