Tootfinder

L-Mosaics and Bounded Join-Semilattices in Isabelle/HOL
Alessandro Linzi
https://arxiv.org/abs/2509.19854 https://arxiv.org/pdf/2509.19854

L-Mosaics and Bounded Join-Semilattices in Isabelle/HOL
We present a complete formalization in Isabelle/HOL of the object part of an equivalence between L-mosaics and bounded join-semilattices, employing an AI-assisted methodology that integrates large language models as reasoning assistants throughout the proof development process. The equivalence was originally established by Cangiotti, Linzi, and Talotti in their study of hypercompositional structures related to orthomodular lattices and quantum logic. Our formalization rigorously verifies the ma…

Responsible AI Technical Report
KT, :, Soonmin Bae, Wanjin Park, Jeongyeop Kim, Yunjin Park, Jungwon Yoon, Junhyung Moon, Myunggyo Oh, Wonhyuk Lee, Junseo Jang, Dongyoung Jung, Minwook Ju, Eunmi Kim, Sujin Kim, Youngchol Kim, Somin Lee, Wonyoung Lee, Minsung Noh, Hyoungjun Park, Eunyoung Shin
https://arxiv.org/abs/2509.20057

Responsible AI Technical Report
KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT'…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment
Julie Jung, Max Lu, Sina Chole Benker, Dogus Darici

Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persian
Ghazal Kalhor, Behnam Bahrak
https://arxiv.org/abs/2509.20168 https://arxi…

Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persian
Multilingual Large Language Models (LLMs) are increasingly used worldwide, making it essential to ensure they are free from gender bias to prevent representational harm. While prior studies have examined such biases in high-resource languages, low-resource languages remain understudied. In this paper, we propose a template-based probing methodology, validated against real-world data, to uncover gender stereotypes in LLMs. As part of this framework, we introduce the Domain-Specific Gender Skew I…

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 …

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…