Skip to content
#explainable ai Open access

Scientific Computing Verification Stack

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management

Abstract

Documentation-only R3 addendum to Scientific Computing Verification Stack R2 (https://doi.org/10.5281/zenodo.22861401). The new PDF walks through four distinct questions in the finite R15 Maxwell sequence: measured source-problem refinement, the separately reported conforming R5 spectral comparator, failure of the historical hybrid pencil at β = 4, and the still-open physical-cluster equivalence of the revised β = 24 pencil. It derives the Schur reduction under an explicit invertibility assumption, computes the final-pair observed curl-error rate 0.973607357390671 from the reported mesh widths and errors, and explains why 323 negative finite modes at β = 4 cannot be erased by a local zero-negative-mode result for a changed operator. Local R7/R8 archive and bounded rerun checks are described with their limitations: the original R6 checkout was not authenticated and the R5 oracle was not rerun. This note does not claim a continuum theorem, spectral equivalence, physical Maxwell validation, or independent third-party review. This version deposits one original explanatory PDF only; it contains no solver code, source archive, or copied third-party figure. CC BY 4.0 applies only to that PDF. Earlier files and rights remain unchanged. Riccardo Giudici acts as an independent systems orchestrator and discloses computational and AI assistance; no institutional endorsement is claimed.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

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 · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

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. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

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. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

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. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

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. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Sep 30, 2026

This game-playing AI is the new champ at Stratego

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.