Tootfinder

Jerry Jones Reveals What He Has Already Offered Micah Parsons https://heavy.com/sports/nfl/dallas-cowboys/jerry-jones-most-guaranteed-micah-parsons/?adt_ei=[email]

CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs
Jinghao Zhang, Sihang Jiang, Shiwei Guo, Shisong Chen, Yanghua Xiao, Hongwei Feng, Jiaqing Liang, Minggui HE, Shimin Tao, Hongxia Ma
https://arxiv.org/abs/2509.16188

CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs
As large language models (LLMs) are increasingly deployed in diverse cultural environments, evaluating their cultural understanding capability has become essential for ensuring trustworthy and culturally aligned applications. However, most existing benchmarks lack comprehensiveness and are challenging to scale and adapt across different cultural contexts, because their frameworks often lack guidance from well-established cultural theories and tend to rely on expert-driven manual annotations. To…

Adaptive Fast-and-Slow Visual Program Reasoning for Long-Form VideoQA
Chenglin Li, Feng Han, FengTao, Ruilin Li, Qianglong Chen, Jingqi Tong, Yin Zhang, Jiaqi Wang
https://arxiv.org/abs/2509.17743

Adaptive Fast-and-Slow Visual Program Reasoning for Long-Form VideoQA
Large language models (LLMs) have shown promise in generating program workflows for visual tasks. However, previous approaches often rely on closed-source models, lack systematic reasoning, and struggle with long-form video question answering (videoQA). To address these challenges, we introduce the FS-VisPR framework, an adaptive visual program reasoning approach that balances fast reasoning for simple queries with slow reasoning for difficult ones. First, we design efficient visual modules (e.…

Task-Oriented Gaussian Optimization for Non-Gaussian Resources in Continuous-Variable Quantum Computation
Boxuan Jing, Feng-Xiao Sun, Qiongyi He
https://arxiv.org/abs/2509.15747

Task-Oriented Gaussian Optimization for Non-Gaussian Resources in Continuous-Variable Quantum Computation
In continuous-variable systems, non-Gaussian resources are essential for achieving universal quantum computation that lies beyond classical simulation. Among the candidate states, the cubic phase state stands out as the simplest form of single-mode non-Gaussian resource, yet its experimental preparation still remains a great challenge. Although a variety of approximate schemes have been proposed to simulate the cubic phase state, they often fall short when deployed in concrete quantum tasks. In…

CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C Compilation Repair
Weixuan Sun, Jucai Zhai, Dengfeng Liu, Xin Zhang, Xiaojun Wu, Qiaobo Hao, AIMgroup, Yang Fang, Jiuyang Tang
https://arxiv.org/abs/2509.15690

CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C++ Compilation Repair
The automated repair of C++ compilation errors presents a significant challenge, the resolution of which is critical for developer productivity. Progress in this domain is constrained by two primary factors: the scarcity of large-scale, high-fidelity datasets and the limitations of conventional supervised methods, which often fail to generate semantically correct patches.This paper addresses these gaps by introducing a comprehensive framework with three core contributions. First, we present CCr…

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Jin Ye, Jingran Wang, Fengchao Xiong, Jingzhou Chen, Yuntao Qian
https://arxiv.org/abs/2508.15553 …

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Hyperspectral images (HSIs) play a crucial role in remote sensing but are often degraded by complex noise patterns. Ensuring the physical property of the denoised HSIs is vital for robust HSI denoising, giving the rise of deep unfolding-based methods. However, these methods map the optimization of a physical model to a learnable network with a predefined depth, which lacks convergence guarantees. In contrast, Deep Equilibrium (DEQ) models treat the hidden layers of deep networks as the solution…

A misaligned protostellar disk fed by gas streamers in a barred spiral-like massive dense core
Xiaofeng Mai, Tie Liu, Xunchuan Liu, Bo Zhang, Paul F. Goldsmith, Neal J. Evans II, Qizhou Zhang, Kee-Tae Kim, Dongting Yang, Mika Juvela, Fengwei Xu, Wenyu Jiao, Hongli Liu, Patricio Sanhueza, Guido Garay, Xi Chen, Shengli Qin, Jakobus M. Vorster, Anandmayee Tej, Zhiyuan Ren, Sami Dib, Shanghuo Li, Qiuyi Luo, Jihye Hwang, Prasanta Gorai, Ariful Hoque, Yichen Zhang, Jeong-Eun Lee, Siju Zhang,…

A misaligned protostellar disk fed by gas streamers in a barred spiral-like massive dense core
High-mass stars, born in massive dense cores (MDCs), profoundly impact the cosmic ecosystem through feedback processes and metal enrichment, yet little is known about how MDCs assemble and transfer mass across scales to form high-mass young stellar objects (HMYSOs). Using multi-scale (40-2500 au) observations of an MDC hosting an HMYSO, we identify a coherent dynamical structure analogous to barred spiral galaxies: three 20,000 au spiral arms feed a 7,500 au central bar, which channels gas to a…

A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination
Chengjin Wang, Yanmin Zhou, Zhipeng Wang, Zheng Yan, Feng Luan, Shuo Jiang, Runjie Shen, Hongrui Sang, Bin He
https://arxiv.org/abs/2509.16963

A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination
Humans and animals can make real-time adjustments to movements by imagining their action outcomes to prevent unanticipated or even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception-action loops to handle physical interaction-induced uncertainties in perception and system modeling within complex systems. Inspired by the action-awareness capability of animal intelligence, this study proposes an imaginati…

Micah Parsons' not-so-cryptic message to Jerry Jones channels Edgar Allen Poe https://www.si.com/nfl/cowboys/news/micah-parsons-not-so-cryptic-message-dallas-cowboys-jerry-jones-channels-edgar-allen-poe

Distribution-Aligned Decoding for Efficient LLM Task Adaptation
Senkang Hu, Xudong Han, Jinqi Jiang, Yihang Tao, Zihan Fang, Sam Tak Wu Kwong, Yuguang Fang
https://arxiv.org/abs/2509.15888

Distribution-Aligned Decoding for Efficient LLM Task Adaptation
Adapting billion-parameter language models to a downstream task is still costly, even with parameter-efficient fine-tuning (PEFT). We re-cast task adaptation as output-distribution alignment: the objective is to steer the output distribution toward the task distribution directly during decoding rather than indirectly through weight updates. Building on this view, we introduce Steering Vector Decoding (SVD), a lightweight, PEFT-compatible, and theoretically grounded method. We start with a short…