activity
20202026
most citedMarius: Learning Massive Graph Embeddings on a Single Machine

10 citations · 36 across the 23 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.AI2026

Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration

Simeng Sun, Roger Waleffe

When training Mixture-of-Experts (MoE) language models with expert parallelism, all-to-all token dispatch and combine collectives can consume a substantial fraction of end-to-end t…

cs.CL2026

Nemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context

Fitsum Reda, John Kamalu, Roger Waleffe +3

Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However, existing approaches use a si…

cs.CV2026

Cosmos 3: Omnimodal World Models for Physical AI

NVIDIA, :, Aditi +293

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…

cs.CL2026

Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

NVIDIA, :, Aaron Blakeman +572

We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…

cs.LG2026

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

NVIDIA, :, Amala Sanjay Deshmukh +204

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…

cs.LG2026

Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

NVIDIA, :, Aakshita Chandiramani +544

We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…

cs.LG2026

LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts

Venmugil Elango, Nidhi Bhatia, Roger Waleffe +13

Mixture of Experts (MoEs) have become a central component of many state-of-the-art open-source and proprietary large language models. Despite their widespread adoption, it remains…