{"topic":"all","updatedAt":"2026-07-27T09:49:10+00:00","cached":true,"items":[{"title":"The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning","description":"The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatial Foundation Models (GeoFMs), which are artificial intelligence/machine learning (AI/ML) models pre-trained on massive geospatial datasets through varied methodologies. W","link":"https://arxiv.org/abs/2607.12177v1","date":"Jul 13, 2026","isoDate":"2026-07-13T21:50:50+00:00","source":"arXiv","authors":"Shelley Cazares","signal":"NEW RESEARCH"},{"title":"ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning","description":"Dynamic traffic variations in Open Radio Access Networks (O-RAN) lead to drift, which degrades the performance of Artificial Intelligence/Machine Learning (AI/ML) models. Traditional retraining approaches maintain forecasting accuracy but incur high computational cost and may lead to violations of Service Level Agreeme","link":"https://arxiv.org/abs/2607.08443v1","date":"Jul 9, 2026","isoDate":"2026-07-09T13:05:42+00:00","source":"arXiv","authors":"Ashit Kumar Subudhi, Bhargav Chirumamilla, Shubham Vaishnav","signal":"NEW RESEARCH"},{"title":"A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability","description":"With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to s","link":"https://arxiv.org/abs/2607.01676v1","date":"Jul 2, 2026","isoDate":"2026-07-02T04:05:16+00:00","source":"arXiv","authors":"Bijit Kumar Banerjee, Devabrat Sharma, R. I. Sujith","signal":"NEW RESEARCH"},{"title":"On the Use of Commit Messages for Corrective Software Maintenance: A Systematic Mapping Study","description":"Corrective maintenance is crucial to ensure the quality of software, thereby improving reliability and user experience. In a version control system (VCS), developers write commit messages to document their changes and support later maintenance. Still, to this day, no secondary study has mapped the research landscape of","link":"https://arxiv.org/abs/2604.16404v1","date":"Mar 31, 2026","isoDate":"2026-03-31T14:16:43+00:00","source":"arXiv","authors":"Syful Islam, Stefano Zacchiroli","signal":"NEW RESEARCH"},{"title":"CACTUSDB: Unlock Co-Optimization Opportunities for SQL and AI/ML Inferences","description":"There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in database systems, to avoid data denormalization and transfer, facilitate management, and alleviate privacy concerns. Co-optimization techniqu","link":"https://arxiv.org/abs/2602.23469v1","date":"Feb 26, 2026","isoDate":"2026-02-26T19:58:54+00:00","source":"arXiv","authors":"Lixi Zhou, Kanchan Chowdhury, Lulu Xie","signal":"NEW RESEARCH"},{"title":"Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level","description":"This study examines the distribution of Artificial Intelligence (AI) research across European NUTS-3 regions during the period 2015-2024. Using bibliometric data from Clarivate InCites and the Citation Topics classification system, we analyse two hierarchical thematic levels: Electrical Engineering, Electronics & Compu","link":"https://arxiv.org/abs/2602.15249v2","date":"Feb 16, 2026","isoDate":"2026-02-16T23:01:14+00:00","source":"arXiv","authors":"Victor Herrero-Solana, Carmen Gálvez","signal":"NEW RESEARCH"},{"title":"Reverse N-Wise Output-Oriented Testing for AI/ML and Quantum Computing Systems","description":"Artificial intelligence/machine learning (AI/ML) systems and emerging quantum computing software present unprecedented testing challenges characterized by high-dimensional/continuous input spaces, probabilistic/non-deterministic output distributions, behavioral correctness defined exclusively over observable prediction","link":"https://arxiv.org/abs/2602.14275v1","date":"Feb 15, 2026","isoDate":"2026-02-15T18:57:11+00:00","source":"arXiv","authors":"Lamine Rihani","signal":"NEW RESEARCH"},{"title":"A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace","description":"Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training ","link":"https://arxiv.org/abs/2601.03120v1","date":"Jan 6, 2026","isoDate":"2026-01-06T15:49:12+00:00","source":"arXiv","authors":"Adam Keane, Nick Pepper, Chris Burr","signal":"NEW RESEARCH"}]}