6 papers
NaviRAG: Towards Active Knowledge Navigation for Retrieval-Augmented Generation
Jihao Dai, Dingjun Wu, Yuxuan Chen +4
Retrieval-augmented generation (RAG) typically relies on a flat retrieval paradigm that maps queries directly to static, isolated text segments. This approach struggles with more c…
Procedural Knowledge at Scale Improves Reasoning
Di Wu, Devendra Singh Sachan, Wen-tau Yih +1
Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat each problem in isolation and do n…
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
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…
GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking
Yunqiang Wang, Hengyuan Na, Di Wu +2
Audio Large Language Models (ALLMs) enable spoken interaction but introduce new jailbreak vulnerabilities. Existing perturbation-based jailbreaks do not explicitly control which fr…
Are We Scaling the Right Thing? A System Perspective on Test-Time Scaling
Youpeng Zhao, Jinpeng LV, Di Wu +2
Test-time scaling (TTS) has recently emerged as a promising direction to exploit the hidden reasoning capabilities of pre-trained large language models (LLMs). However, existing sc…