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cs.LG2026
Evo: Autoregressive-Diffusion Large Language Models with Evolving Balance
Junde Wu, Minhao Hu, Jiayuan Zhu +7
We introduce \textbf{Evo}, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative fr…
cs.LG2025
70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
Tianyi Zhang, Mohsen Hariri, Shaochen Zhong +4
Large-scale AI models, such as Large Language Models (LLMs) and Diffusion Models (DMs), have grown rapidly in size, creating significant challenges for efficient deployment on reso…
cs.LG2025
Quantize What Counts: More for Keys, Less for Values
Mohsen Hariri, Alan Luo, Weicong Chen +6
Large Language Models (LLMs) suffer inference-time memory bottlenecks dominated by the attention Key-Value (KV) cache, which scales with model size and context length. While KV-cac…