Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
At first glance, A392243 comes from a simple rule: add the positive integers in order, changing the sign whenever the running index crosses into a new square-number block. This creates a sequence with a surprisingly rich internal structure. The question at the center of this paper is equally simple: when can one of these signed sums be a perfect sixth power? An unexpected answer leads directly to the Fibonacci numbers. The paper proves that every Fibonacci number from the nontrivial range generates a corresponding sixth-power value in A392243, producing an infinite family hidden inside the sequence. It then develops an exact factor-pair description of every possible sixth-power occurrence. This separates the familiar values forced by square indices from the newly identified Fibonacci family and turns the remaining search into a precise problem in Diophantine arithmetic. The paper also proves that only finitely many solutions can arise when one factor coordinate is fixed. Complete computation through bases 2≤m≤50,0002\le m\le50{,}000 finds no non-Fibonacci examples outside the forced family. Further results explain why several natural modular and first-level descent methods cannot, by themselves, settle the full problem. The evidence strongly supports Fibonacci exhaustion for m≥2m\ge2, but a universal proof remains open. Version 2.0 corrects the conjecture’s small-base boundary, since m=1m=1 gives both ∣a(1)∣=1|a(1)|=1 and ∣a(2)∣=1|a(2)|=1. It also repairs an overflowing artifact table, corrects the bibliography numbering, and adds verified publication details. The accompanying archive includes the manuscript, an AI-readable edition, reproducibility scripts, frozen computational results, licensing information, citation metadata, and SHA-256 checksums.
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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