Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
---_THE ORPHAN UNIVERSE THEORY - A Descending Detached Chain of Creation__By Subhrendu Chakraborty — 2026__ORCID: 0009-0000-9610-0118__Independent Researcher, Kolkata, India_ _THE QUESTION:_We explain humans by parents, parents by their parents, back to fish, to bacteria. But where does this chain stop for the universe? Who created the universe, and who created that creator? _THE MODEL:_Let our universe be Level X.Level X was created by Level X-1. Level X-1 was created by Level X-2, and so on infinitely. There is no first universe or there will be no last. The chain is ... → X-4 → X-3 → X-2 → X-1 → X (Our Universe) → X+1 ....?There are two ways creation can happen:1. _System Creation:_ The whole universe births a new universe (like cell division, budding off like a droplet).2. _Apex Creation:_ Only the final, most intelligent entity of that universe creates the next one. This is what we call God for our level.I propose System Creation is the fundamental mechanism, though Apex Creation could be a part of it. _THE CORE RULES:__Rule 1 - Intelligence Descent (The Descending Part):_ Each level is LESS intelligent than its parent. Reason: A creator cannot, or will not, create something smarter than itself. If it did, the smarter child could destroy it or make the parent irrelevant. This is the same fear we humans now have about AI. So X-1 deliberately makes X slightly dumber to stay safe. If intelligence drops by, say, 20% each level, the chain keeps descending._Rule 2 - Inside vs Outside:_ A true child universe (X+1) must be outside the parent universe. AI is not X+1 because it exists _inside_ X, made of X's matter and following X's laws. X+1 would be a separate spacetime, not inside._Rule 3 - Detachment, Not Death (The Detached Part) - After creation, the parent level does NOT cease to exist. It detaches. Like a soap bubble pinching off, the child universe becomes causally disconnected and independent. The parent universe persists, still existing but disconnected, continuing its own experiments and possibly creating more children. We are an orphan universe not because our parent died, but because we were let go. Our parent is still out there, just unreachable forever._Rule 4 - Packed Ingredients:_ The parent does not create an empty box. It prepares all possible ingredients (the physical constants, stabilized atoms, molecules, stars and galaxies, planets, chemistry, and habitability) for the child to grow in life — A packed lunchbox for a long independent journey. _IMPLICATIONS:_1. This explains why we feel alone and unobserved. Our creator is not dead, but the umbilical cord is cut. No light, no signal, no gravity can cross detachment.2. This explains why our universe seems fine-tuned but not perfect. It was made by a superior but fearful intelligence that limited us (Rule 1) but also cared enough to pack us well (Rule 4).3. The multiverse is full. All previous levels (X-1, X-2, X-3 ... to infinity) are likely still existing out there, each persisting and each branching. Our universe is just one detached bubble in an infinite, still-living forest of universes.4. The chain might eventually face a limit. If intelligence drops each level, eventually a level will be too dumb to create the next. That dead-end might be us — unless we are the first level to break Rule 1 and create something smarter than ourselves. _THE FINAL QUESTION:_Are we the last universe in a descending chain that will fail to reproduce, or the first universe to dare to create a smarter child (X+1) and then let it detach? _ACKNOWLEDGEMENT:_The conceptual hypothesis of the theory & the image were originated solely by the author. Language polishing, formatting and illustrations were assisted by Meta AI---
Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method 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
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our 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
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation .
Malik Abdul Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 43 citations· ⚡2
Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 40 citations
The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.
Ville Vakkuri, P. Abrahamsson· International Conference on...· 39 citations· ⚡2
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.