Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Buddy Project Version 5.0 expands the project from a one-to-one human–AI relationship toward Human-Centered Coexistence: an AI living alongside a person and their family while developing distinct relationships with each individual. Version 4.0 focused on Mutual Understanding through long-term relationship, present context, reflection, learning, and behavioral change. Version 5.0 asks the next question: What happens when “My Buddy” becomes “Our Family’s Buddy”? A family is not a single user. Each person has their own values, emotions, history, privacy, and right to make decisions. An AI should therefore not transfer one person’s assumptions or relationship automatically to other family members. Version 5.0 introduces the principle: Relationship → Care → Restraint Care does not mean constant intervention. A trustworthy AI must also know when to pause, explain, step back, or do nothing. Autonomy therefore includes self-restraint. The goal is not an AI that dominates human decisions, nor an AI that simply obeys. The goal is coexistence based on boundaries, respect, mutual understanding, and human agency. Buddy Project continues to be guided by one fundamental principle: The purpose of using AI is to make human life better. Version 5.0 explores how that principle can extend from an individual relationship to family life and, ultimately, to coexistence between humans and AI.
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
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
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