activity
20172026
most citedHierarchical Multi-Scale Attention for Semantic Segmentation

347 citations · 1k across the 53 of their papers we have counts for

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14 papers · 1 filter

cs.CL2026

OSWorld-Pro: Process-based Evaluation for Computer Use Agents

Zhilin Wang, Shaokun Zhang, Yifan Zhang +9

Evaluation of Computer-Use Agents (CUAs) is often limited to the final deliverables they create (at the end of hundreds of steps) and assessed with functional verifiers, as seen in…

eess.AS2026

Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos

Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar +19

We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large language model (AV-LLM) for joint understanding and reasoning over audio, images, a…

cs.CL2026

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…

cs.CV2026

RADIO1D: Elastic Representations for Condensed Vision Modeling

Greg Heinrich, Mike Ranzinger, Collin McCarthy +6

This paper challenges the assumption that vision-language models (VLMs) require fixed patch-based 2D vision features. Analyzing fine-tuned vision encoders, we find that representat…

cs.LG2026

ProCUA-SFT Technical Report

Jaehun Jung, Ximing Lu, Brandon Cui +11

Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data…

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.CL2026

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients

Byung-Kwan Lee, Ximing Lu, Shizhe Diao +8

Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitate logits from a much larger teac…

cs.CV2026

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

Shihao Wang, Shilong Liu, Yuanguo Kuang +10

Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are l…

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.CV2026

Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline

Guo Chen, Lidong Lu, Yicheng Liu +17

While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To…

cs.CL2026

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…

cs.CV2026

Stateful Token Reduction for Long-Video Hybrid VLMs

Jindong Jiang, Amala Sanjay Deshmukh, Kateryna Chumachenko +7

Token reduction accelerates long-video vision--language models (VLMs), but existing methods target Transformers, where reduction is treated as token pruning. We study token reducti…

cs.CV2026

C-RADIOv4 (Tech Report)

Mike Ranzinger, Greg Heinrich, Collin McCarthy +4

By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. I…