activity
20182026
most citedWhite or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

24 citations · 38 across the 16 of their papers we have counts for

collaborators

16 papers

cs.AI2026

Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs

Akhiad Bercovich, Talor Abramovich, Daniel Afrimi +67

We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maximize server throughput under…

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 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.DC2026

Scalable Training of Mixture-of-Experts Models with Megatron Core

Zijie Yan, Hongxiao Bai, Xin Yao +42

Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total pa…

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…

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.CL2025

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

NVIDIA, :, Aarti Basant +214

We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…

cs.CL2025★ 1 cited

Llama-Nemotron: Efficient Reasoning Models

Akhiad Bercovich, Itay Levy, Izik Golan +132

We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an ope…

cs.CL2025

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

NVIDIA, :, Aaron Blakeman +198

As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…

cs.CL2024★ 1 cited

Upcycling Large Language Models into Mixture of Experts

Ethan He, Abhinav Khattar, Ryan Prenger +7

Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However,…

cs.CV2021

Analysis on Image Set Visual Question Answering

Abhinav Khattar, Aviral Joshi, Har Simrat Singh +2

We tackle the challenge of Visual Question Answering in multi-image setting for the ISVQA dataset. Traditional VQA tasks have focused on a single-image setting where the target ans…

cs.SI2019★ 1 cited

What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter

Indraneil Paul, Abhinav Khattar, Shaan Chopra +2

Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public int…

cs.SI2018★ 1 cited

Elites Tweet? Characterizing the Twitter Verified User Network

Indraneil Paul, Abhinav Khattar, Ponnurangam Kumaraguru +2

Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separate…

cs.SI2018★ 9 cited

Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach

Srishti Gupta, Abhinav Khattar, Arpit Gogia +2

Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks to spread spam campaigns by propagating phishing URLs, attaching malicious con…

cs.SI2018★ 24 cited

White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

Abhinav Khattar, Karan Dabas, Kshitij Gupta +2

The Blue Whale Challenge is a series of self-harm causing tasks that are propagated via online social media under the disguise of a "game." The list of tasks must be completed in a…