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Introduces Future-L1, an interleaved latent visual reasoning framework that improves video event prediction by maintaining visual semantics in latent space. Achieves state-of-the-art results on FutureBench and TwiFF-Bench benchmarks.
KVarN is a calibration-free KV-cache quantizer that uses Hadamard rotation and dual-scaling variance normalization to reduce error accumulation during autoregressive decoding in large language models, achieving state-of-the-art 2-bit precision on reasoning benchmarks.
NVIDIA released Nemotron-Labs-Diffusion, a family of diffusion language models that generate multiple tokens in parallel, enabling faster inference and better GPU utilization, with sizes from 3B to 14B including vision-language variants.
This paper introduces BitLM, a language model that uses bitwise continuous diffusion to generate multiple tokens in parallel, aiming to overcome the sequential bottleneck of traditional autoregressive generation while preserving causal structure.