KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.
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ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
RTPrune introduces a reading-twice inspired two-stage pruning technique for DeepSeek-OCR that retains 84.25% tokens while delivering 99.47% accuracy and 1.23x faster prefill on OmniDocBench.
A new model unifies per-pixel and word tokens in a generative language model with per-pixel embeddings, color folding, and unsupervised image pretraining, reporting good performance on small models with limited data.
citing papers explorer
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Geometry Guided Self-Consistency for Physical AI
KeyStone improves task success rates in diffusion-based physical AI models by up to 13.3% by sampling K trajectories in parallel, clustering them in action space, and returning the medoid of the largest cluster.
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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference
RTPrune introduces a reading-twice inspired two-stage pruning technique for DeepSeek-OCR that retains 84.25% tokens while delivering 99.47% accuracy and 1.23x faster prefill on OmniDocBench.
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Unified Pix Token And Word Token Generative Language Model
A new model unifies per-pixel and word tokens in a generative language model with per-pixel embeddings, color folding, and unsupervised image pretraining, reporting good performance on small models with limited data.