Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Space Science and Extraterrestrial Life
Abstract
This paper explores an idea I've been developing: that space is a web of quantum entanglement, and that the universe expands because the links in that web gradually weaken as new pieces of space are added.I set out two hypotheses. First, distance between points in space depends on how strongly they're entangled. Second, space grows by adding new nodes into existing links, after which each node rebalances its entanglement. I then show how these ideas fit with established physics, including black-hole entropy, Einstein's equations and the expansion history of the universe, and I'm clear about which parts follow firmly and which are assumptions.I also tested the idea. Simple computer simulations show that adding nodes at random makes space crumple, but growing it in time-ordered slices with rebalancing produces something close to real geometry in a simplified 2D test.I committed to one specific prediction: that empty regions of space (cosmic voids) should expand much faster than general relativity predicts. Real survey data show voids behave exactly as general relativity says, so that version of the idea is wrong, and I report this openly.What survives is the core idea that entanglement sets distance, which could in principle be tested in the lab through "entanglement harvesting", and an open question: whether this picture could explain the size of dark energy.I'm an independent researcher without formal physics training. I developed the maths and simulations with the help of an AI assistant (Claude, by Anthropic), and I'd welcome feedback from anyone in the field.
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.
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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.
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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.
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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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