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Dense pixel-level segmentation can be performed directly inside an autoregressive token space without external decoders by serializing binary change masks into grammar-constrained quadtrees.
CogVis redefines change detection by decoupling temporal perception from semantic categorization, achieving unprecedented efficiency and accuracy across multiple benchmarks.
DIVE achieves an impressive 88.9% reduction in visual tokens without sacrificing performance, redefining efficiency in vision-language models.
NebulaExp reveals that a meticulously curated dataset and innovative reinforcement learning strategies can boost LLM performance significantly, achieving up to 4.43 points improvement in instruction-following tasks with minimal data.
