Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
《三元稳态论(TSST)全球开源导读》中英混合开源文档,开源许可:CC BY‑NC‑SA 4.0,全套成果归档于 Zenodo 平台。 三元稳态论 (TSST) 是一套研究开放耗散复杂系统长期存续的跨学科元约束框架。提出三条互相独立的元公理:闭环完整性 FII、尺度同步 SSI、物理底线 BCI;构建生存不变量ℐ、协同‑冲突相变判据 Ψ 等可工程计算数理判据。 理论可在极限条件下退化为八大经典科学理论;提供人体生理、FPGA 芯片、过程控制、生成式 AI、关键基础设施、闭环脑机接口六大硬件实测实证案例。 文档完整收录中英摘要、关键词、符号速查、核心术语表、五卷专著与系列论文 DOI、著作权登记清单;清晰写明三大硬约束与使用禁区,区分理论推演与硬件实证边界。 本导读为综述文档,不替代原著严谨推导;面向全球科研人员、工程师、跨学科爱好者,欢迎开展复现、证伪实验与理论改进。 Global Open‑Source Guide to the Tri‑Steady‑State Theory (TSST), bilingual open‑source document. License: CC BY‑NC‑SA 4.0. The complete body of work is archived on Zenodo. Tri‑Steady‑State Theory (TSST) is an interdisciplinary meta‑constraint framework investigating long‑term survival of open dissipative complex systems. It puts forward three mutually independent meta‑axioms: Flow‑Information‑Integrity (FII), Scale‑Synchronization‑Interaction (SSI), and Boundary‑Constraint‑Invariant (BCI). It establishes engineering‑computable mathematical criteria including the survival invariant ℐ and the synergy‑conflict phase‑transition criterion Ψ. Under limit conditions, the theory degenerates into eight well‑established classical scientific theories. It presents six hardware‑validated empirical cases: human physiology monitoring, FPGA chips, process control, generative AI, critical infrastructure, and closed‑loop brain‑computer interfaces. This document contains bilingual abstract and keywords, symbol reference sheet, glossary of key terms, DOIs for the five‑volume monograph series and associated papers, as well as copyright registration lists. It explicitly states three hard constraints and usage restrictions, and draws clear distinctions between theoretical deduction and hardware‑based empirical evidence. As a survey guide, it cannot replace rigorous derivations in original monographs. Targeted at global researchers, engineers and interdisciplinary enthusiasts, reproduction, falsification experiments and theoretical improvements are welcome.
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
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026