1 citations · 1 across the 16 of their papers we have counts for
18 papers
VERA: Scaling Verifiable Environments for Agentic co-Evolution
Junqi Liu, Yongyang Pan, Zhuosong Jiang +14
Competent agents need precise and verifiable environments, such as sandboxes that are resumable at any stage and evolve from observable evidence. However, most long-horizon work ex…
BaT: Towards Self-Evolving Medical Research Agent with Stage Rubrics
Junqi Liu, Yufan He, Yexiao He +9
Long-horizon agents are beginning to automate complete workflows that produce code, reports, and research artifacts. Medical imaging workflows are multi-stage and data-sensitive, w…
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…
Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis
Shufan Li, Greg Heinrich, Hanrong Ye +4
We propose Nemotron-Labs-Diffusion-Image, a state-of-the-art masked discrete diffusion model (MDM) for high-resolution text-to-image synthesis. Compared with prior work on masked i…
Vesta: A Generalist Embodied Reasoning Model
Johan Bjorck, Zhiqi Li, Yunze Man +29
Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While specialist models excel at indiv…
Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
Yitong Jiang, Hongjun Wang, Collin McCarthy +15
Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining. Subquadratic al…
JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search
Dongyun Zou, Zhuoyang Zhang, Junyu Chen +8
We introduce JetViT, a novel family of hybrid-architecture Vision Transformer (ViT) models that match the accuracy of state-of-the-art full-attention vision foundation models while…
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…
Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing
Baifeng Shi, Stephanie Fu, Long Lian +10
Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in th…
Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception
Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian +15
We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is…
GSPN-2: Efficient Parallel Sequence Modeling
Hongjun Wang, Yitong Jiang, Collin McCarthy +12
Efficient vision transformer remains a bottleneck for high-resolution images and long-video related real-world applications. Generalized Spatial Propagation Network (GSPN) addresse…
ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
Hongjin Su, Shizhe Diao, Ximing Lu +13
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and comp…
Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models
Yonggan Fu, Xin Dong, Shizhe Diao +12
Efficient deployment of small language models (SLMs) is essential for numerous real-world applications with stringent latency constraints. While previous work on SLM design has pri…
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
Hanrong Ye, Chao-Han Huck Yang, Arushi Goel +29
Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to buil…
UALM: Unified Audio Language Model for Understanding, Generation and Reasoning
Jinchuan Tian, Sang-gil Lee, Zhifeng Kong +11
Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks…
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang +11
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-i…
Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations
Hung-Chun Hsu, Yuan-Ching Kuo, Chao-Han Huck Yang +6
The rapid evolution of e-commerce has exposed the limitations of traditional product retrieval systems in managing complex, multi-turn user interactions. Recent advances in multimo…
Scaling RL to Long Videos
Yukang Chen, Wei Huang, Baifeng Shi +11
We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of…