Tootfinder

Previously on... Automating Code Review
Robert Heum\"uller, Frank Ortmeier
https://arxiv.org/abs/2508.18003 https://arxiv.org/pdf/2508.18003

Previously on... Automating Code Review
Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability in task definitions, datasets, and evaluation procedures. This study provides the first comprehensive analysis of MCR automation research, aiming to characterize the field's evolution, formalize lear…

Hybrid solver methods for ODEs: Machine-Learning combined with standard methods
J\"urgen Geiser
https://arxiv.org/abs/2508.13538 https://arxiv.org/pdf…

Hybrid solver methods for ODEs: Machine-Learning combined with standard methods
In this article, we consider combined standard and machine learning methods to solve ODEs and PDEs. We deal with the minimisation problems for the machine learning algorithms and standard discretization methods, which are related to Runge-Kutta methods and finite difference methods. We show, that we could solve the ODEs with additional ML methods, e.g., feedforward network, such that it will accelerate the solver process.

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang, Karl Henrik Johansson, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
https://arxiv.org/abs/2509.08942

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data generating sources, standard ML methods often learn spurious correlations that perform well on average but degrade performance for atypical or underrepresented groups. Prior work addresses this issue by optimizing the worst-group performance. However, these approaches typically assume that the underlying data distribution…

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Suman Das Gupta, Shamik Ghosh, Laltu Gazi, Shubham Dutta, Alexander Ledovskoy, Satyaki Bhattacharya, Shilpi Jain
https://arxiv.org/abs/2509.11291

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Calorimeters operating in high-radiation environments are susceptible to damage, leading to increased noise that can significantly degrade energy resolution. A common way to mitigate noise is to apply a higher energy threshold on the cells, typically set a few standard deviations above the noise level. However, this method risks discarding cells with genuine energy deposits, worsening the energy resolution. In this paper we explore various machine learning (ML) algorithms that can replace a rig…

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms
Alexandra E. Koloniari, Lazaros Lazaridis, Christos Paschalidis, Nikolaos Stergioulas
https://arxiv.org/abs/2509.05283 …

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms
The discovery of gravitational waves (GWs) from merging compact binaries has transformed modern astrophysics, driving innovation in detection methodologies. Whereas matched-filtering techniques have long been the standard, the growing volume of data from advanced observatories like LIGO, Virgo, and KAGRA has spurred interest in machine learning (ML) solutions for their scalability and computational efficiency. As next-generation detectors approach reality, the development of reliable and adapta…