… We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single model, identifying capability loss in different ways: mixed RLVR suffers from inter-capability divergence cost, while the pipeline of first training experts and then performing OPD , tho… …
… Moreover, lightweight offline LoRA finetuning on only one camera-annotated video further improves this capability and generalizes to unseen videos, improving camera adherence, visual quality, and motion dynamics without test-time optimization or target-video adaptation. …
… However, this offline paradigm introduces a Supervision-Perception Mismatch: the student model is forced to align with fine-grained details beyond its perceptual capacity, learning to guess rather than to see. …
… AI-generated summary Recent progress in multimodal large language models MLLMs has brought AI capabilities from static offline data processing to real-time streaming interaction , yet they still remain far from human-level multimodal interaction. …
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… It contains 2,061 examples across three levels: active visual evidence seeking , controlled offline interleaved multimodal search, and open-web interleaved multimodal search. …
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… By combining benchmark review , in-the-wild stress tests , and expert-constrained case studies , this roadmap offers a capability-centered lens for understanding, evaluating, and advancing the next generation of intelligent visual generation systems. …