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Nanjing University
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VLMs can achieve substantial improvements in reasoning performance using only unlabeled data through a novel self-reflective training framework inspired by human cognition.
Current interactive world models fall short, with none passing the rigorous tests of WorldRoamBench designed to assess long-horizon stability across action, vision, physics, and memory.
Current image editing models struggle with physics-based reasoning, as revealed by the new PhyEditBench benchmark.
SSR not only resolves destructive parameter collisions in LoRA merging but also guarantees mathematical optimality, setting a new standard for efficiency in diffusion model training.
GIM-World achieves superior long-horizon visual consistency by integrating geometry-aware implicit memory, outperforming traditional memory systems.
