Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Artificial intelligence changes what organizations can do; human leadership must decide what should be done, what remains accountable, and what must never be surrendered to machines. This working paper presents FILE: The Five Intelligences of Leadership Evolution as a human-centered management theory and diagnostic framework for responsible leadership and decision-making in the age of artificial intelligence and robotics. FILE argues that the most important human skills for the AI era and the future of work require the integrated exercise of five meta-intelligences: Augmented Intelligence, Emotional Intelligence, Cultural Intelligence, Political Intelligence, and Adaptive Intelligence. The paper defines the FILE formula, maps the framework onto the human hand, presents the 70 foundational nested intelligences, the 128-intelligence Teaching and Diagnostic Framework, and the 163-intelligence Extended Taxonomy, explains five emergent outcomes, and develops RC: The Relational Commons as the shared human ecosystem that leadership must protect, cultivate, and renew. It also clarifies boundaries among adjacent intelligences, offers a diagnostic application to AI-supported employee performance evaluation, and states limitations and future research directions. Its purpose is to establish a public, citable, and timestamped record of FILE as a transdisciplinary theory of human leadership in the age of AI and robots.
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
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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