Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Particle physics theoretical and experimental studies
Abstract
This manuscript presents a revised account of the assumptions, calculations, and unresolved derivations underlying selected predictions of Fundamental Information Theory (FIT). It examines three central hypotheses: normal neutrino mass ordering with a massless lightest state, a leptonic CP phase within 270°–290°, and a present cold-dark-matter-to-baryon density ratio of 16/3. The neutrino mass sum is presented as a conditional consequence of the mass hypothesis and measured mass-squared differences, rather than an independent prediction. The discussion distinguishes information-theoretic motivation, phenomenological assumptions, established mathematical consequences, and empirical evidence. It explains the missing steps required for a complete physical derivation and corrects earlier claims concerning the fine-structure constant, the electroweak scale, and a proposed minimum distinguishability threshold. This document accompanies “FIT: Testable Predictions.” Together, they supersede the six earlier FIT manuscripts identified in the text as the current formulation of the selected hypotheses. Earlier versions remain historical records. This revision does not constitute retrospective preregistration or claim a complete, experimentally confirmed derivation of the Standard Model. Prepared by Franciscus Gerardus Adrianus van den Berg, independent researcher, with AI assistance in analysis and editing. The author is responsible for the content. This manuscript has not undergone external peer review. Version 1.0 — 12 October 2026.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Microsoft Research Blog· microsoft.comOct 22, 2026
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.