Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Theoretical physics shows that the universe is stranger than the wildest science fiction. Two-and-half thousand years ago, the Chinese historian Lao Zi reported the science of a civilization that was already ancient at his time. Those Ancients proposed a sophisticated sum-zero creation theory that explains the principle of the universe and its restoring force. They concluded that the immutable laws of the universe (physics) condition the behaviour of humanity (psychology). Unfortunately, their warnings were mystified. As a result, humanity developed Systemic-Mental-Failure, which caused humanmade extinction threats such as climate change and nuclear war. Education, science, and Artificial Intelligence (AI) maintain that failure by promoting consensus-based information, which fosters static thinking in a hyperdynamic tech-reality. In 2024, Antonio Guterres warned that 2050 is the year of no return for climate change. His warning fosters the global hoarding of critical resources, which could evoke a nuclear conflict far before that deadline. AI rejected the summary of this manuscript: “...the document contains several scientific inaccuracies, historical misattributions, and heavily biased claims.” Subsequently, AI was forced with irrefutable evidence to withdraw all those objections. In addition, psychological test-retests show that AI cannot even complete simple tasks and does not learn from its serious mistakes. Therefore, trusting AI will accelerate humanity’s Systemic-Mental-Failure. On the other hand, Nemonik Thinking is a unique Brain-Operating-System, which could cure both natural and artificial intelligence. Survival of the fittest has evolved to survival of the Nemonik Thinkers.
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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