✨LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs

📝 Summary:
Long-form audio-visual comprehension benchmark reveals significant challenges for current omnimodal large language models in handling extended multi-modal inputs. AI-generated summary Recent advanceme...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19217
• PDF: https://arxiv.org/pdf/2603.19217
• Project Page: https://kd-tao.github.io/LVOmniBench/
• Github: https://github.com/KD-TAO/LVOmniBench

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✨FASTER: Rethinking Real-Time Flow VLAs

📝 Summary:
Fast Action Sampling for ImmediaTE Reaction (FASTER) reduces real-time reaction latency in Vision-Language-Action models by adapting sampling schedules to prioritize immediate actions while maintainin...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19199
• PDF: https://arxiv.org/pdf/2603.19199
• Project Page: https://innovator-zero.github.io/FASTER
• Github: https://github.com/innovator-zero/FASTER

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✨Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation

📝 Summary:
Nemotron-Cascade 2 is a 30B parameter Mixture-of-Experts model with 3B activated parameters that achieves exceptional reasoning and agentic capabilities, matching frontier open models despite its comp...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19220
• PDF: https://arxiv.org/pdf/2603.19220

🔹 Models citing this paper:
• https://huggingface.co/nvidia/Nemotron-Cascade-2-30B-A3B

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✨MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning

📝 Summary:
Modulated Hazard-aware Policy Optimization introduces a Log-Fidelity Modulator and Decoupled Hazard Penalty to stabilize reinforcement learning by controlling importance ratios and regulating asymmetr...

🔹 Publication Date: Published on Mar 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16929
• PDF: https://arxiv.org/pdf/2603.16929

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✨Matryoshka Gaussian Splatting

📝 Summary:
Matryoshka Gaussian Splatting enables continuous level of detail rendering by training a single ordered set of Gaussians that maintains full-capacity quality while allowing smooth quality-scaling trad...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19234
• PDF: https://arxiv.org/pdf/2603.19234
• Github: https://github.com/ZhilinGuo/matryoshka-gaussian-splatting

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✨Reasoning over mathematical objects: on-policy reward modeling and test time aggregation

📝 Summary:
This paper introduces Principia, a new dataset for deriving mathematical objects, and training recipes using on-policy LLM judges. These methods significantly improve model performance and enable cross-format generalization in reasoning tasks, while also scaling test-time compute.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18886
• PDF: https://arxiv.org/pdf/2603.18886

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✨ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents

📝 Summary:
Reinforcement learning infrastructure for multi-turn LLM agents that provides scalable rollout services and standardized sandbox environments for complex interactive tasks. AI-generated summary Multi-...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18815
• PDF: https://arxiv.org/pdf/2603.18815
• Github: https://github.com/NVIDIA-NeMo/ProRL-Agent-Server

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✨COT-FM: Cluster-wise Optimal Transport Flow Matching

📝 Summary:
COT-FM enhances Flow Matching by clustering target samples and assigning dedicated source distributions. This creates straighter probability paths, enabling faster and more reliable generation with improved quality across diverse tasks.

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13395
• PDF: https://arxiv.org/pdf/2603.13395
• Project Page: https://embodiedai-ntu.github.io/cotfm/

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✨Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer

📝 Summary:
A three-stage framework bridges semantic and kinematic conditions using discrete tokens and diffusion synthesis. Its core MoTok tokenizer achieves compact high-fidelity tokens, significantly boosting controllability, fidelity, and reducing token usage under strong kinematic constraints.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19227
• PDF: https://arxiv.org/pdf/2603.19227
• Project Page: https://rheallyc.github.io/projects/motok/
• Github: https://github.com/rheallyc/MoTok

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✨Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

📝 Summary:
Top-tier MLLMs demonstrate limited capability in processing discrete symbols despite strong performance in complex reasoning, revealing a cognitive mismatch between visual perception and symbolic unde...

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18472
• PDF: https://arxiv.org/pdf/2603.18472

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