Tootfinder

Autonomous real-time control of turbulent dynamics
Junjie Zhang, Chengwei Xia, Xianyang Jiang, Isabella Fumarola, Georgios Rigas
https://arxiv.org/abs/2509.11002 https://…

Autonomous real-time control of turbulent dynamics
Mastering turbulence remains one of physics' most intractable challenges, with its chaotic, multi-scale dynamics driving energy dissipation across transport and energy systems. Here we report REACT (Reinforcement Learning for Environmental Adaptation and Control of Turbulence), a fully autonomous reinforcement learning framework that achieves real-time, adaptive, closed-loop turbulence control in real-world environments. Deployed on a road vehicle model equipped solely with onboard sensors and …

Crosslisted article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Welfare-Centric Clustering
Claire Jie Zhang, Seyed A. Esmaeili, Jamie Morgenstern

Data or Language Supervision: What Makes CLIP Better than DINO?
Yiming Liu, Yuhui Zhang, Dhruba Ghosh, Ludwig Schmidt, Serena Yeung-Levy
https://arxiv.org/abs/2510.11835 https:/…

Data or Language Supervision: What Makes CLIP Better than DINO?
CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs), but it remains unclear whether this advantage stems from CLIP's language supervision or its much larger training data. To disentangle these factors, we pre-train CLIP and DINO under controlled settings -- using the same architecture, dataset, and training configuration -- achieving similar ImageNet accuracy. Embedding analysis shows that CLIP captures high-level semantics (e.g., object catego…

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners
Xiaoxue Luo, Jinwei Huang, Runyan Yang, Yingying Gao, Junlan Feng, Chao Deng, Shilei Zhang
https://arxiv.org/abs/2509.09201

DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners
Universal audio codecs learn entangled representations across audio types, whereas some specific codecs offer decoupled representations but are limited to speech. Real-world audio, however, often contains mixed speech and background sounds, and downstream tasks require selective access to these components. Therefore, we rethink the audio codec as a universal disentangled representation learner to enable controllable feature selection across different audio tasks. To this end, we introduce DeCod…

Doping Evolution of Nodal Electron Dynamics in Trilayer Cuprate Superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10 \delta}$ Revealed by Laser-Based Angle-Resolved Photoemission Spectroscopy
Hao Chen, Jumin Shi, Xiangyu Luo, Yinghao Li, Yiwen Chen, Chaohui Yin, Yingjie Shu, Jiuxiang Zhang, Taimin Miao, Bo Liang, Wenpei Zhu, Neng Cai, Xiaolin Ren, Chengtian Lin, Shenjin Zhang, Zhimin Wang, Fengfeng Zhang, Feng Yang, Qinjun Peng, Zuyan Xu, Guodong Liu, Hanqing Mao, Xintong Li, Lin Zhao, X. J. …

Doping Evolution of Nodal Electron Dynamics in Trilayer Cuprate Superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ Revealed by Laser-Based Angle-Resolved Photoemission Spectroscopy
The doping evolution of the nodal electron dynamics in the trilayer cuprate superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ (Bi2223) is investigated using high-resolution laser-based angle-resolved photoemission spectroscopy (ARPES). Bi2223 single crystals with different doping levels are prepared by controlled annealing which cover the underdoped, optimally-doped and overdoped regions. The electronic phase diagram of Bi2223 is established which describes the T$_\mathrm{c}$ dependence on the…

VoltanaLLM: Feedback-Driven Frequency Control and State-Space Routing for Energy-Efficient LLM Serving
Jiahuan Yu (University of Illinois Urbana-Champaign), Aryan Taneja (University of Illinois Urbana-Champaign), Junfeng Lin (Tsinghua University), Minjia Zhang (University of Illinois Urbana-Champaign)
https://arxiv.org/abs/2509.04827

VoltanaLLM: Feedback-Driven Frequency Control and State-Space Routing for Energy-Efficient LLM Serving
Modern Large Language Model (LLM) serving systems increasingly support interactive applications, like real-time chat assistants, code generation tools, and agentic workflows. However, the soaring energy cost of LLM inference presents a growing challenge for sustainable and cost-effective deployment. This paper introduces VoltanaLLM, a system for SLO-aware, energy-efficient LLM serving, built from a control theory perspective. VoltanaLLM co-designs frequency scaling and request routing in emergi…

VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
Wang Jiayin, Wei Yanran, Jiang Lei, Guo Xiaoyu, Zheng Ayong, Zhao Weidong, Li Zhongkui
https://arxiv.org/abs/2508.18937

VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
Autonomous control of the laparoscope in robot-assisted Minimally Invasive Surgery (MIS) has received considerable research interest due to its potential to improve surgical safety. Despite progress in pixel-level Image-Based Visual Servoing (IBVS) control, the requirement of continuous visibility and the existence of complex disturbances, such as parameterization error, measurement noise, and uncertainties of payloads, could degrade the surgeon's visual experience and compromise procedural saf…

It Takes Two: Your GRPO Is Secretly DPO
Yihong Wu, Liheng Ma, Lei Ding, Muzhi Li, Xinyu Wang, Kejia Chen, Zhan Su, Zhanguang Zhang, Chenyang Huang, Yingxue Zhang, Mark Coates, Jian-Yun Nie
https://arxiv.org/abs/2510.00977

It Takes Two: Your GRPO Is Secretly DPO
Group Relative Policy Optimization (GRPO) is a prominent reinforcement learning algorithm for post-training Large Language Models (LLMs). It is commonly believed that GRPO necessitates a large group size to ensure stable training via precise statistical estimation, which incurs substantial computational overhead. In this work, we challenge this assumption by reframing GRPO as a form of contrastive learning, which reveals a fundamental connection to Direct Preference Optimization (DPO). Motivate…

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Xinxu Zhou, Jiaqi Bai, Zhenqi Sun, Fanxiang Zeng, Yue Liu
https://arxiv.org/abs/2509.13677

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Although significant progress has been made in many tasks within the field of Natural Language Processing (NLP), Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation. Additionally, in real scenario and online applications, cost considerations, scalability, domain knowledge learning and more precise control are required, presenting more challenge for CTG. This paper introduces a novel and scalable frame…

Reputation-based partition scheme for IoT security
Zhikui Chen, Muhammad Zeeshan Haider, Naiwen Luo, Shuo Yu, Xu Yuan, Yaochen Zhang, Tayyaba Noreen
https://arxiv.org/abs/2508.03981

Reputation-based partition scheme for IoT security
With the popularity of smart terminals, such as the Internet of Things, crowdsensing is an emerging data aggregation paradigm, which plays a pivotal role in data-driven applications. There are some key issues in the development of crowdsensing such as platform security and privacy protection. As the crowdsensing is usually managed by a centralized platform, centralized management will bring various security vulnerabilities and scalability issues. To solve these issues, an effective reputation-b…