Skip to content
#explainable ai Open access

The Observational Incompleteness Framework

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

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, including 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 and formalization (verification/, papers/oi_lattice_code/) — Lean/Mathlib theorem developments, independent executable probes and countercontrols, a coverage ledger and proof-status census, lattice Monte Carlo sources, run drivers, analysis scripts, and deterministic test suites. The verification record distinguishes executable evidence from kernel-checked results and tracks graded proof status. Among the formally tracked milestones is the K2 two-branch Hamiltonian reconstruction theorem: under its stated hypotheses, transition-probability dynamics determine the Hamiltonian up to diagonal rephasing, energy origin, and a global antiunitary branch; the named non-kernel step is the cited integer turnpike classification of Bekir and Golomb. 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).

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

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 · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

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. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

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. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

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. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

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. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested 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 · 39 citations · ⚡2

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.