TileMix: Tile-Centric Mixed-Precision Attention for LLM Inference Acceleration
arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce
arXiv:2604.05887v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is
arXiv:2608.27875v1 Announce Type: new Abstract: Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit
arXiv:2607.20475v1 Announce Type: new Abstract: Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for
arXiv:2605.04215v1 Announce Type: cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token
arXiv:2605.15250v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank
arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large
arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and
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