ST-GridPool improves video LLM performance via hierarchical temporal gridding and norm-based spatial pooling on visual tokens without training.
Ts-llava: Constructing vi- sual tokens through thumbnail-and-sampling for training-free video large language models.arXiv preprint arXiv:2411.11066,
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WindowQuant performs window-adaptive mixed-precision KV cache quantization guided by similarity to the text prompt, with reordering to enable efficient inference in VLMs.
Direct algorithms sample codon sequences from Boltzmann distributions using tensor-based secondary structure free energy models for RNA design under codon constraints.
citing papers explorer
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Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding
ST-GridPool improves video LLM performance via hierarchical temporal gridding and norm-based spatial pooling on visual tokens without training.
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WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization
WindowQuant performs window-adaptive mixed-precision KV cache quantization guided by similarity to the text prompt, with reordering to enable efficient inference in VLMs.
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Direct RNA sequence design under codon constraints using expressive tensor-based secondary structure models
Direct algorithms sample codon sequences from Boltzmann distributions using tensor-based secondary structure free energy models for RNA design under codon constraints.