Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
About This Book This is the shortest way into the Productive Value–Productive Power (PV-PP) runtime. It explains what the PV-PP framework is and what the runtime does, recommends building with an AI assistant, shows how to install and verify the runtime, and then builds one small application, a battery-powered sensor, in five runnable steps. It is the first of three volumes. The PV-PP Framework & Programming Guide teaches the framework and application design at length, and the PV-PP Framework & Runtime Programming Reference states exactly what the runtime provides. Read this book first. You do not need the other two to finish it. Which runtime. Everything here describes PV-PP Runtime V2.1, frozen build v0.141, in the folder runtime-v2.1 of the repository. Section 3.2 shows how to prove that the copy you have is that build. The book stands alone. Everything you need to read is printed here: the tutorial programs and their output in Chapter 5, and the rules and prompts for AI assistants in Chapter 7. Every listing in Chapter 5 is reproduced verbatim from files that run against the unmodified, hash-verified runtime; every output shown is what those files print, and the six tests in Section 5.7 pass. Companion files. So that you do not have to retype anything, the repository’s getting-started folder holds exact copies of that material: the six tutorial files in getting-started/battery, to run, and Listings 7.1 to 7.4 in getting-started/ai-rules-and-prompts.txt, to paste into an assistant. Neither contains anything the book does not. Citations. A citation such as Guide 1.9 or Reference 3.1 names a section of one of the other two volumes, which the opening of Chapter 1 introduces.
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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