most citedInference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

3 citations · 4 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2025

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.LG2025

Weight Spectra Induced Efficient Model Adaptation

Chongjie Si, Xuankun Yang, Muqing Liu +5

Large-scale foundation models have demonstrated remarkable versatility across a wide range of downstream tasks. However, fully fine-tuning these models incurs prohibitive computati…

cs.CV2025

Unified Autoregressive Visual Generation and Understanding with Continuous Tokens

Lijie Fan, Luming Tang, Siyang Qin +11

We present UniFluid, a unified autoregressive framework for joint visual generation and understanding leveraging continuous visual tokens. Our unified autoregressive architecture p…

cs.RO2025

SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models

Jiawei Zhang, Xuan Yang, Taiqi Wang +3

Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances…

cs.CV20251 cited

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

Guoqing Ma, Haoyang Huang, Kun Yan +112

We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression…

cs.CV20253 cited

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Nanye Ma, Shangyuan Tong, Haolin Jia +8

Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…