Tootfinder

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Sun, Haotian Shi, Wei Ma, Jian Sun
https://arxiv.org/abs/2509.20102

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed trade-off between competing objectives such as adversariality and realism. This yields behavior-specific models that cannot be steered at inference time, lacking the efficiency and flexibility to generate tailored scenarios for diverse training and testing requirements. In view of this, we reframe the task of adversa…

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Sun, Haotian Shi, Wei Ma, Jian Sun
https://arxiv.org/abs/2509.20102

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed trade-off between competing objectives such as adversariality and realism. This yields behavior-specific models that cannot be steered at inference time, lacking the efficiency and flexibility to generate tailored scenarios for diverse training and testing requirements. In view of this, we reframe the task of adversa…

SDE-DET: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments
Yihao Hu, Pan Wang, Xiaodong Bai, Shijie Cai, Hang Wang, Huazhong Liu, Aiping Yang, Xiangxiang Li, Meiping Ding, Hongyan Liu, Jianguo Yao
https://arxiv.org/abs/2509.19990

SDE-DET: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments
Pomelo detection is an essential process for their localization, automated robotic harvesting, and maturity analysis. However, detecting Shatian pomelo in complex orchard environments poses significant challenges, including multi-scale issues, obstructions from trunks and leaves, small object detection, etc. To address these issues, this study constructs a custom dataset STP-AgriData and proposes the SDE-DET model for Shatian pomelo detection. SDE-DET first utilizes the Star Block to effectivel…

Harmonic balance-automatic differentiation method: an out-of-the-box and efficient solver for general nonlinear dynamics simulation
Yi Chen, Yuhong Jin, Rongzhou Lin, Yifan Jiang, Xutao Mei, Lei Houb, Yilong Wang, Ng Teng Yong, Anxin Guo
https://arxiv.org/abs/2508.07309

Harmonic balance-automatic differentiation method: an out-of-the-box and efficient solver for general nonlinear dynamics simulation
The Harmonic Balance-Alternating Frequency-Time domain (HB-AFT) method is extensively employed for dynamic response analysis of nonlinear systems. However, its application to high-dimensional complex systems is constrained by the manual derivation of Jacobian matrices during Newton-Raphson iterations, which become computationally intractable or error-prone for intricate nonlinearities. The Harmonic Balance-Automatic Differentiation (HB-AD) method is proposed to address this limitation, in which…

Task-Oriented Learning for Automatic EEG Denoising
Tian-Yu Xiang, Zheng Lei, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Mei-Jiang Gui, Hong-Yun Ou, Xin-Zheng Huang, Xin-Yi Fu, Zeng-Guang Hou
https://arxiv.org/abs/2509.14665

Task-Oriented Learning for Automatic EEG Denoising
Electroencephalography (EEG) denoising methods typically depend on manual intervention or clean reference signals. This work introduces a task-oriented learning framework for automatic EEG denoising that uses only task labels without clean reference signals. EEG recordings are first decomposed into components based on blind source separation (BSS) techniques. Then, a learning-based selector assigns a retention probability to each component, and the denoised signal is reconstructed as a probabil…

DeeP-TE: Data-enabled Predictive Traffic Engineering
Zhun Yin, Xiaotian Li, Lifan Mei, Yong Liu, Zhong-Ping Jiang
https://arxiv.org/abs/2508.14281 https://…

DeeP-TE: Data-enabled Predictive Traffic Engineering
Routing configurations of a network should constantly adapt to traffic variations to achieve good network performance. Adaptive routing faces two main challenges: 1) how to accurately measure/estimate time-varying traffic matrices? 2) how to control the network and application performance degradation caused by frequent route changes? In this paper, we develop a novel data-enabled predictive traffic engineering (DeeP-TE) algorithm that minimizes the network congestion by gracefully adapting rout…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
NextStep Team, Chunrui Han, Guopeng Li, Jingwei Wu, Quan Sun, Yan Cai, Yuang Peng, Zheng Ge, Deyu Zhou, Haomiao Tang, Hongyu Zhou, Kenkun Liu, Ailin Huang, Bin Wang, Changxin Miao, Deshan Sun, En Yu, Fukun Yin, Gang Yu, Hao Nie, Haoran Lv, Hanpeng Hu, Jia Wang, Jian Zhou, Jianjian Sun, Kaijun Tan, Kang An, Kangheng Lin, Liang Zhao, Mei Chen, Peng Xing, Rui Wang, Shiyu Liu, Shutao Xia, Tianhao You, Wei Ji…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. Next…

3D and 4D World Modeling: A Survey
Lingdong Kong, Wesley Yang, Jianbiao Mei, Youquan Liu, Ao Liang, Dekai Zhu, Dongyue Lu, Wei Yin, Xiaotao Hu, Mingkai Jia, Junyuan Deng, Kaiwen Zhang, Yang Wu, Tianyi Yan, Shenyuan Gao, Song Wang, Linfeng Li, Liang Pan, Yong Liu, Jianke Zhu, Wei Tsang Ooi, Steven C. H. Hoi, Ziwei Liu
https://arxiv.org/abs/…

3D and 4D World Modeling: A Survey
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphasizes generative methods for 2D image and video data, they overlook the rapidly growing body of work that leverages native 3D and 4D representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds for large-scale scene modeling. At the same time, the absence of a standardized definition and taxonomy for …

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Tian-Yu Xiang, Rui-Ze Ma, Nu-Fang Xiao, Zeng-Guang Hou
https://arxiv.org/abs/2508.10794

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has driven the adoption of self-supervised learning (SSL) methods such as masked image modeling (MIM) to leverage large-scale unlabeled data for learning transferable representations. Unfortunately, conventional MIM often fails to capture vascular anatomy because of the severe class imbalance between vessel and background…