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cs.LG2026
KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization
Lexington Whalen, Yuki Ito, Ryo Sakamoto
We develop a second-order theory of quantization noise in matrix multiplication in which the quantization format is characterized by the variance it assigns to each element. The co…
cs.CL2026
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
cs.CL2026
Teaching Diffusion to Speculate Left-to-Right
Lexington Whalen, Yuki Ito, Ryo Sakamoto
Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inhere…