Tootfinder

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
Somtochukwu Azie, Yiping Meng
https://arxiv.org/abs/2509.04505

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
The integration of Artificial Intelligence (AI) into construction project management (CPM) is accelerating, with Large Language Models (LLMs) emerging as accessible decision-support tools. This study aims to critically evaluate the ethical viability and reliability of LLMs when applied to the ethically sensitive, high-risk decision-making contexts inherent in CPM. A mixed-methods research design was employed, involving the quantitative performance testing of two leading LLMs against twelve real…

Generating Inputs for Grammar Mining using Dynamic Symbolic Execution
Andreas Pointner (University of Applied Sciences Upper Austria, Austria), Josef Pichler (University of Applied Sciences Upper Austria, Austria), Herbert Pr\"ahofer (Johannes Kepler University Linz, Austria)
https://arxiv.org/abs/2508.03832

Generating Inputs for Grammar Mining using Dynamic Symbolic Execution
A vast number of software systems include components that parse and process structured input. In addition to programming languages, which are analyzed by compilers or interpreters, there are numerous components that process standardized or proprietary data formats of varying complexity. Even if such components were initially developed and tested based on a specification, such as a grammar, numerous modifications and adaptations over the course of software evolution can make it impossible to pre…

DIPSY: A new Disc Instability Population SYnthesis, II. The Populations of Companions Formed Through Disc Instability
O. Schib, C. Mordasini, A. Emsenhuber, R. Helled
https://arxiv.org/abs/2510.02437

DIPSY: A new Disc Instability Population SYnthesis, II. The Populations of Companions Formed Through Disc Instability
We applied the global end-to-end model described in Paper~I of this series to perform a population synthesis of companions formed via disc instability (DI). By using initial conditions compatible with both observations and hydrodynamical simulations, and by studying a large range of primary masses (0.05-5 Msol), we can provide quantitative predictions of the outcome of DI. In the baseline population, we find that ~10 % of the discs fragment, and about half of these end up with a surviving com…

Measuring Human Leadership Skills with Artificially Intelligent Agents
Ben Weidmann, Yixian Xu, David J. Deming
https://arxiv.org/abs/2508.02966 https://ar…

Measuring Human Leadership Skills with Artificially Intelligent Agents
We show that the ability to lead groups of humans is predicted by leadership skill with Artificially Intelligent agents. In a large pre-registered lab experiment, human leaders worked with AI agents to solve problems. Their performance on this 'AI leadership test' was strongly correlated with their causal impact on human teams, which we estimate by repeatedly randomly assigning leaders to groups of human followers and measuring team performance. Successful leaders of both humans and AI agents a…

A Study of Rule Omission in Raven's Progressive Matrices
Binze Li
https://arxiv.org/abs/2510.03127 https://arxiv.org/pdf/2510.03127

A Study of Rule Omission in Raven's Progressive Matrices
Analogical reasoning lies at the core of human cognition and remains a fundamental challenge for artificial intelligence. Raven's Progressive Matrices (RPM) serve as a widely used benchmark to assess abstract reasoning by requiring the inference of underlying structural rules. While many vision-based and language-based models have achieved success on RPM tasks, it remains unclear whether their performance reflects genuine reasoning ability or reliance on statistical shortcuts. This study invest…

Coverage and Rate Performance Analysis of Multi-RIS-Assisted Dual-Hop mmWave Networks
Yuwen Cao, Xiaowen Wu, Jiguang He, Tomoaki Ohtsuki, Tony Q. S. Quek
https://arxiv.org/abs/2509.00717

Coverage and Rate Performance Analysis of Multi-RIS-Assisted Dual-Hop mmWave Networks
Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation; ii) Even though the introduction of reconfigurable intelligent surfaces (RISs) can overcome the performance degradation c…

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Maijunxian Wang, Ran Ji
https://arxiv.org/abs/2509.02089 https://arxiv.org/pdf/2509…

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Artificial intelligence is often measured by the range of tasks it can perform. Yet wide ability without depth remains only an imitation. This paper proposes a Structural-Generative Ontology of Intelligence: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time. These three conditions -- generativity, coordination, and sustaining -- define the depth that underlies real intelligence. Current AI systems, however b…

Metamorphic Testing of Deep Code Models: A Systematic Literature Review
Ali Asgari, Milan de Koning, Pouria Derakhshanfar, Annibale Panichella
https://arxiv.org/abs/2507.22610 h…

Metamorphic Testing of Deep Code Models: A Systematic Literature Review
Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices. Despite these capabilities, robustness remains a critical…

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang
https://arxiv.org/abs/2509.22661

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
The next Point-of-interest (POI) recommendation is mainly based on sequential traffic information to predict the user's next boarding point location. This is a highly regarded and widely applied research task in the field of intelligent transportation, and there have been many research results to date. Traditional POI prediction models primarily rely on short-term traffic sequence information, often neglecting both long-term and short-term preference data, as well as crucial spatiotemporal cont…

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
Aadarsh Anantha Ramakrishnan, Shubham Agarwal, Selvanayagam S, Kunwar Singh
https://arxiv.org/abs/2510.01967

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals - making them unsuitable for secure and …