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20202026
most citedNVIDIA Nemotron 3: Efficient and Open Intelligence

1 citations · 1 across the 13 of their papers we have counts for

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cs.CL2025★ 1 cited

NVIDIA Nemotron 3: Efficient and Open Intelligence

NVIDIA, :, Aaron Blakeman +356

We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a…

cs.CL2025

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

NVIDIA, :, Aaron Blakeman +311

We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 t…

cs.AI2025

WeMusic-Agent: Efficient Conversational Music Recommendation via Knowledge Internalization and Agentic Boundary Learning

Wendong Bi, Yirong Mao, Xianglong Liu +4

Personalized music recommendation in conversational scenarios usually requires a deep understanding of user preferences and nuanced musical context, yet existing methods often stru…

cs.CV2025

: A "Spot the Difference" Challenge for Large Multimodal Models

Kewei Wei, Bocheng Hu, Jie Cao +13

Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend…

cs.DC2025

MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training

Lu Zhao, Rong Shi, Shaoqing Zhang +21

The training of large-scale Mixture of Experts (MoE) models faces a critical memory bottleneck due to severe load imbalance caused by dynamic token routing. This imbalance leads to…

cs.CV2025

Frame-Difference Guided Dynamic Region Perception for CLIP Adaptation in Text-Video Retrieval

Jiaao Yu, Mingjie Han, Tao Gong +2

With the rapid growth of video data, text-video retrieval technology has become increasingly important in numerous application scenarios such as recommendation and search. Early te…

cs.CV2025

Noise is an Efficient Learner for Zero-Shot Vision-Language Models

Raza Imam, Asif Hanif, Jian Zhang +3

Recently, test-time adaptation has garnered attention as a method for tuning models without labeled data. The conventional modus operandi for adapting pre-trained vision-language m…