Tootfinder

A Survey of Vibe Coding with Large Language Models
Yuyao Ge, Lingrui Mei, Zenghao Duan, Tianhao Li, Yujia Zheng, Yiwei Wang, Lexin Wang, Jiayu Yao, Tianyu Liu, Yujun Cai, Baolong Bi, Fangda Guo, Jiafeng Guo, Shenghua Liu, Xueqi Cheng
https://arxiv.org/abs/2510.12399

A Survey of Vibe Coding with Large Language Models
The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology termed "Vibe Coding" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension. Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored, with empirical evidence revealing unexpected productivity losse…

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
Yuqi Bai, Tianyu Huang, Kun Sun, Yuting Chen
https://arxiv.org/abs/2510.11734

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
This research focuses on using large language models (LLMs) to simulate social experiments, exploring their ability to emulate human personality in virtual persona role-playing. The research develops an end-to-end evaluation framework, including individual-level analysis of stability and identifiability, as well as population-level analysis called progressive personality curves to examine the veracity and consistency of LLMs in simulating human personality. Methodologically, this research propo…

FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption
Justus Viga, Penelope Mueck, Alexander L\"oser, Torben Weis
https://arxiv.org/abs/2510.08217 ht…

FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption
In the shipping industry, fuel consumption and emissions are critical factors due to their significant impact on economic efficiency and environmental sustainability. Accurate prediction of ship fuel consumption is essential for further optimization of maritime operations. However, heterogeneous methodologies and limited high-quality datasets hinder direct comparison of modeling approaches. This paper makes three key contributions: (1) we introduce and release a new dataset (https://huggingface…

AIM 2025 Challenge on Real-World RAW Image Denoising
Feiran Li, Jiacheng Li, Marcos V. Conde, Beril Besbinar, Vlad Hosu, Daisuke Iso, Radu Timofte
https://arxiv.org/abs/2510.06601

AIM 2025 Challenge on Real-World RAW Image Denoising
We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is built upon a newly established evaluation benchmark featuring challenging low-light noisy images captured in the wild using five different DSLR cameras. Participants are tasked with developing novel noise synthesis pipelines, network architectures, and training methodologies to achieve high performance across different …

Variational Inference for Count Response Semiparametric Regression: A Convex Solution
Virginia Murru, Matt P. Wand
https://arxiv.org/abs/2510.12356 https://

Variational Inference for Count Response Semiparametric Regression: A Convex Solution
We develop a version of variational inference for Bayesian count response regression-type models that possesses attractive attributes such as convexity and closed form updates. The convex solution aspect entails numerically stable fitting algorithms, whilst the closed form aspect makes the methodology fast and easy to implement. The essence of the approach is the use of Pólya-Gamma augmentation of a Negative Binomial likelihood, a finite-valued prior on the shape parameter and the structured m…

Generalized Distributions of Host Dispersion Measures in the Fast Radio Burst Cosmology
Jing-Yi Jia, Da-Chun Qiang, Lin-Yu Li, Hao Wei
https://arxiv.org/abs/2510.09463 https://

Generalized Distributions of Host Dispersion Measures in the Fast Radio Burst Cosmology
As is well known, Hubble tension is one of the most serious challenges in cosmology to date. So, it is of interest to measure the Hubble constant by using some new probes independent of cosmic microwave background (CMB) and type Ia supernovae (SNIa). One of the promising probes is the fast radio bursts (FRBs), which could be useful in cosmology. In the literature, the methodology proposed by Macquart {\it et al.} has been widely used, in which both $\rm DM_{IGM}$ and $\rm DM_{host}$ are describ…

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Leonhard H\"olscher, Oliver Ahrend, Lukas Karch, Carlotta L'Estocq, Marc Marfany Andreu, Tobias Stollenwerk, Frank K. Wilhelm, Julia Kowalski
https://arxiv.org/abs/2510.07280

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Topology optimization is a key methodology in engineering design for finding efficient and robust structures. Due to the enormous size of the design space, evaluating all possible configurations is typically infeasible. In this work, we present an end-to-end, fault-tolerant quantum algorithm for topology optimization that operates on the exponential Hilbert space representing the design space. We demonstrate the algorithm on the two-dimensional Messerschmitt-Bölkow-Blohm (MBB) beam problem. By…

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Hongxing Li, Dingming Li, Zixuan Wang, Yuchen Yan, Hang Wu, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang
https://arxiv.org/abs/2510.08531

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We identify that this limitation stems from a critical gap: existing methods attempt to learn spatial reasoning directly without establishing the hierarchical foundations of perception and understanding. To address this challenge, we present a comprehensive methodology for building spatial intelligence progressively. We in…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- General Bayesian L2 calibration of mathematical models
Antony M. Overstall, James M. McGree