5 papers
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving
Kewei Zhang, Jin Wang, Sensen Gao +9
End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficient inference. Existing parad…
EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
Liang Lin, Chunxi Luo, Kaiwen Luo +9
Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing robustness methods primarily r…
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios
Yibo Zhang, Liang Lin, Kaiwen Luo +8
While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaus…
HearSay Benchmark: Do Audio LLMs Leak What They Hear?
Jin Wang, Liang Lin, Kaiwen Luo +8
While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications remain largely unexplored. This pa…