Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
AdaptVision: Efficient vision-language models via adaptive visual acquisition.arXiv preprint arXiv:2512.03794, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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AVA-VLM reduces visual-token usage by 69% while improving PPE-violation F1 by 13 points over direct-QA baselines by training a VLM to adaptively crop high-resolution local regions from a downsampled global image.
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Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
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AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring
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