Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 3 citations· 1 references
Abstract
Version note (v1.0.1, 2026-09-07). Typesetting correction only: a stray punctuation mark in the Core Epistemic Registration block is removed. Text, equations, ledger and references are unchanged from v1.0. What this is. A theory/perspective manuscript (Final Journal Manuscript v1.0, 7 September 2026; submission-ready, not peer reviewed) on the economics of expertise under generative AI: why candidate abundance shifts scarcity from generating knowledge-like output to validating it; why knowledge is not expertise; expertise as a context-bound human state (three ideal-type states, not a deterministic ladder); a formal economics of candidate abundance and validation scarcity (candidate sets, validation capacity, backlog, steady state, AI-induced epistemic scarcity shift, validated throughput and value, bottleneck revaluation); and why practitioners become epistemically central. It introduces a three-part Core Epistemic Registration for projects — core respondent / experience-based expert, interactional expert (or None), and the AI model(s) used with their roles — with the non-collapse rule that these three never merge. Equation provenance. Existing equations attributed to the Human–AI Readout Programme are taken from Toledo, the programme's equation library (concept DOI 10.5281/zenodo.22537318), and cited by lineage; every new definition, derivation, architecture and hypothesis is labelled as such in the paper's own Appendix A ledger (30 numbered equations) and is registered in Toledo as a coded reading with this record as its origin. Status. K0 theory manuscript with an explicit claim-boundary ledger; hypotheses H1–H9 carry falsifiers. Checked under the glosa methodology before deposit (assessment in the programme's research journal). The paper's own Core Epistemic Registration names the AI models used and their roles, by the author's decision; no AI system is an author or contributor.
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