Tootfinder

Distillation versus Contrastive Learning: How to Train Your Rerankers
Zhichao Xu, Zhiqi Huang, Shengyao Zhuang, Ashim Gupta, Vivek Srikumar
https://arxiv.org/abs/2507.08336

Distillation versus Contrastive Learning: How to Train Your Rerankers
Training text rerankers is crucial for information retrieval. Two primary strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowledge distillation (transferring knowledge from a larger reranker). While both have been studied in the literature, a clear comparison of their effectiveness for training cross-encoder rerankers under practical conditions is needed. This paper empirically compares these strategies by training rerankers of different size…

EcBot: Data-Driven Energy Consumption Open-Source MATLAB Library for Manipulators
Juan Heredia, Christian Schlette, Mikkel Baun Kj{\ae}rgaard
https://arxiv.org/abs/2508.06276 ht…

EcBot: Data-Driven Energy Consumption Open-Source MATLAB Library for Manipulators
Existing literature proposes models for estimating the electrical power of manipulators, yet two primary limitations prevail. First, most models are predominantly tested using traditional industrial robots. Second, these models often lack accuracy. To address these issues, we introduce an open source Matlab-based library designed to automatically generate \ac{ec} models for manipulators. The necessary inputs for the library are Denavit-Hartenberg parameters, link masses, and centers of mass. Ad…

A Use Case Lens on Digital Cultural Heritage
Gustavo Candela, Milena Dobreva, Henk Alkemade, Olga Holownia, Mahendra Mahey, Sarah Ames, Karen Renaud, Ines Vodopivec, Benjamin Charles Germain Lee, Thomas Padilla, Steven Claeyssens, Isto Huvila, Beth Knazook
https://arxiv.org/abs/2509.08710

A Use Case Lens on Digital Cultural Heritage
This article proposes a novel methodological approach for developing use cases for CH e-infrastuctures documented using Jupyter Notebooks (JNs), enabling transparency and reproducibility. We also address the present problem of use cases that are not consistently documented to cover all key aspects that are derived from the use case literature review outside of CH field to define a useful use case. Purpose. Our primary objective is to explore the practices around creating and analysing use cas…

A unified theory of the high-dimensional Laplace approximation with application to Bayesian inverse problems
Anya Katsevich, Vladimir Spokoiny
https://arxiv.org/abs/2509.07952 h…

A unified theory of the high-dimensional Laplace approximation with application to Bayesian inverse problems
The Laplace approximation (LA) to posteriors is a ubiquitous tool to simplify Bayesian computation, particularly in the high-dimensional settings arising in Bayesian inverse problems. Precisely quantifying the LA accuracy is a challenging problem in the high-dimensional regime. We develop a theory of the LA accuracy to high-dimensional posteriors which both subsumes and unifies a number of results in the literature. The primary advantage of our theory is that we introduce a new degree of flexib…

im a perfect marriage between heaven and hell, yin and yang. my primary source is literature, science, and art, yet thanks to the other zoomers i follow who do use tiktok, i can have familiarity with the depraved mindkilling my generation is force-addicted to without destroying myself to the point i can't create good art with it in mind

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Ina K. Schieferdecker
https://arxiv.org/abs/2506.14640

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening…

A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Alessia Pisu, Livio Pompianu, Francesco Osborne, Diego Reforgiato Recupero, Daniele Riboni, Angelo Salatino
https://arxiv.org/abs/2508.04213

A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Taxonomies and ontologies of research topics (e.g., MeSH, UMLS, CSO, NLM) play a central role in providing the primary framework through which intelligent systems can explore and interpret the literature. However, these resources have traditionally been manually curated, a process that is time-consuming, prone to obsolescence, and limited in granularity. This paper presents Sci-OG, a semi-auto\-mated methodology for generating research topic ontologies, employing a multi-step approach: 1) Topic…