28 citations · 90 across the 65 of their papers we have counts for
7 papers · 2 filters
IndicFDB: Benchmarking Full-Duplex Voice Agents across Indian Languages
Rajarshi Roy, Shobhit Banga, Jonathan Raiman +12
Full-duplex voice agents must handle pauses, take turns, backchannel, and respond to user interruptions in real time. Full-Duplex-Bench evaluates these behaviors, but its English-o…
Unified Audio Intelligence Without Regressing on Text Intelligence
Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim +17
Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-te…
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…
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…
Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
Zhuolin Yang, Zihan Liu, Yang Chen +14
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size,…
MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos
Arushi Goel, Sreyan Ghosh, Vatsal Agarwal +16
Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over…
On Data Engineering for Scaling LLM Terminal Capabilities
Renjie Pi, Grace Lam, Mohammad Shoeybi +3
Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed…