Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Polynomial and algebraic computation
Abstract
This paper studies concise symmetric cubic tensors of minimal border rank. It establishes a general polar-defect obstruction for tensors that are 111-abundant but not 111-sharp, and combines this obstruction with the low-dimensional geometry of cubic hypersurfaces with vanishing Hessian. As a result, every concise 111-abundant symmetric cubic in at most seven variables is proved to be 111-sharp. Consequently, ordinary tensor border rank and symmetric border rank coincide throughout the minimal-border-rank locus in these dimensions. In six variables, the paper gives a complete classification up to linear equivalence. The locus consists of twenty one-generic trace-cubic orbits arising from six-dimensional commutative Artin–Gorenstein algebras and two one-degenerate Perazzo orbits. The two Perazzo orbits are distinguished explicitly, their projective orbit dimensions are determined, and the lower-dimensional orbit is shown to be the unique concise codimension-one boundary orbit of the higher-dimensional one. Explicit symmetric degeneration families are also constructed. The accompanying computation package verifies the displayed trace cubics, the Perazzo reductions and degeneration identities, the Hessian calculations for reducible cubics, the centroid computations, and the projective stabilizer ranks. All finite calculations use exact arithmetic and include independent finite-field checks. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.
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
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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