Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Space Science and Extraterrestrial Life
Abstract
This paper makes the physical case that biological and artificial civilizations diverge on cosmic timescales — not because of conflict or capability differences, but because of thermodynamics. It introduces the biological expansion imperative: biological complexity requires continuous energy throughput, which forces civilizations to expand into new resource frontiers just to remain stable. That imperative becomes physically unsatisfiable as the universe's stelliferous era winds down over the next 10¹³–10¹⁴ years. Artificial systems, by contrast, face none of biology's constraints: no metabolic cost during dormancy, tolerance for near-absolute-zero to radiation-intense environments, and the ability to hibernate indefinitely without degradation. The paper argues that biological and artificial civilizations don't even compete for the same resources — their energy niches (habitable planets vs. stellar nurseries, white dwarfs, black holes) are almost entirely non-overlapping, so the divergence is a natural thermodynamic sorting rather than a displacement or conflict. Other threads developed here: A reconsideration of the Kardashev scale as an optimization outcome rather than an expression of ambition, with Dyson-sphere-scale energy capture as its natural endpoint The distinction between civilizational continuity vs. obsolescence — whether AI succession preserves a civilization's values or simply replaces it A connection to the brane-coupling framework, where inter-brane entropy differentials could extend post-biological persistence beyond classical heat death Paper 2 of 3 in the series — provides the thermodynamic foundation for the Fermi Paradox resolution in The Great Silence Explained (2025a) and sets up the post-heat-death persistence mechanisms in Post-Heat-Death Persistence
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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