The Observational Incompleteness Framework is an AI-based research programme deriving quantum-mechanical structure from the premise that observation is a proper subsystem of a deterministic whole. The archive contains three kinds of content: Technical papers (papers/) — the authoritative technical content, the core papers Main (central theorem and emergent quantum mechanics), Substratum (substratum construction and reconstruction theorem), Structure (structural realism), SM (Standard Model derivation) and GR (gravitational sector), together with the focused presentation Juno (neutrino-sector prediction), the methodology paper Physics Modulo Gauge, and the companion documents Explainer, Complexity, Medicine and Bioinformatics. Sources and built PDFs are both included. Book manuscript (book/) — The Incompleteness of Observation: A Unified Framework from Quantum Mechanics to Computational Biology, a working draft addressed to a general technical readership. The papers, not the book, are the primary reference for framework-internal derivations. Verification code (papers/oi_lattice_code/) — lattice Monte Carlo sources, run drivers, analysis scripts, and the deterministic test suites behind claims made in the papers. Licensing. This deposit is mixed content under a single Zenodo license field. The manuscripts are licensed CC-BY-4.0, which is the label shown here; the source code is licensed MIT, per the LICENSE file at the archive root. The Licensing section of README.md is the authoritative statement of scope. Version-specific DOIs are minted for each release; the concept DOI resolves to the latest version. Work reproducing a specific claim should cite the version DOI of the release that carries it. Discussion and feedback are welcome via the linked GitHub repository (Discussions).
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
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
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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