Extended restricted snapshot (v5) of the AUGMANITAI / NEOMANITAI research bundle. Author: Andreas Ehstand, independent researcher, Starnberg, Germany. ORCID 0009-0006-3773-7796. Wikidata Q138634675. This version is a working paper. This version adds Paper G, which positions the Explicitation Protocol relative to two prominently discussed programmes in contemporary world-model research: the factor-graph-based formulation of sensing, estimation, and planning recently developed under the designation STAG (Dellaert 2026), and the energy-based Joint-Embedding Predictive Architecture line pursued by LeCun and collaborators, most recently instantiated in LeWorldModel (Maes, Le Lidec, Scieur, LeCun, Balestriero 2026). Paper G — The Explicitation Protocol and Factor-Graph-Based World Models. 21 pages, ~8,966 words (incl. disclaimer). Develops a three-layer positioning of the world-model stack: architectural (Layer 1), learning-dynamics (Layer 2), and representational-vocabulary (Layer 3). STAG and JEPA operate at Layers 1–2; the Explicitation Protocol operates at Layer 3. The three programmes are structurally composable rather than competing. Six concrete points of complementarity are developed: named variables of state, factor types at the objective boundary, latent-dimension semantics in JEPA, compositional structure of hierarchical architectures, the cost module in LeCun's six-module framework, and transfer of learned content across systems. Five foreseeable objections are addressed directly. No priority claim over the Dellaert–LeCun factor-graph/energy-based correspondence is made. Central positioning, stated directly. Factor-graph-based architectures (STAG, STEAP) and energy-based joint-embedding architectures (JEPA, I-JEPA, V-JEPA, V-JEPA 2, LeWorldModel, H-JEPA) are architectural proposals. The Explicitation Protocol is a protocol proposal at a different layer. Architecture and protocol do not compete; they compose. The paper specifies precisely how and at which points. Status: Working Paper. This deposit is an interim research document. Its formulations, examples, and positioning arguments are provisional and subject to revision based on further empirical work, reviewer input, and community discourse. The paper documents a snapshot of ongoing research, not a finalised position. Methodological provenance. The underlying decomposition discipline originates in Leistungsfaktorenanalyse (performance-factor analysis) as practiced at ITF-tour and Bundesliga-level tennis coaching and has been transferred to representation-capable artificial systems under the wider research designation PERMANITAI. The existing empirical record consists of 5,524 ISO-aligned intensional definitions with 50,653 SKOS-compliant semantic relations across 274 specialist fields, produced over nine months. Relation to prior deposits in this series. Companion to the Technical Paper (10.5281/zenodo.19691489), Mathematical Foundations (10.5281/zenodo.19691491), public Concept statement (10.5281/zenodo.19691495), Explicitation Protocol v2 (10.5281/zenodo.19701316), Extended Series v3 with Papers A–C (10.5281/zenodo.19701432), and Complete Series v4 with Papers D–F (10.5281/zenodo.19701495). All prior files from v1 through v4 are retained in v5 unchanged. Access. Restricted. Requests are evaluated individually. Contact via the author's ORCID record. Legitimate research, review, or legal parties are welcome. License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Record date: 24 April 2026. AUGMANITAI 26§ Full Disclaimer — Bilingual (EN + DE) — Version 4.0 For use on: Zenodo, GitHub, GitHub Pages, HuggingFace, npm, PyPI, LinkedIn, Medium, ORCID, Wikidata, all publications and platforms Disclaimer / Haftungsausschluss English (EN) §1 Descriptive Nature (D): All content within the AUGMANITAI framework, including all terminological definitions, term descriptions, framework descriptions, performance factor analyses, substrate tables, and research hypotheses, is exclusively descriptive (D). Every statement documents observed or proposed phenomena without expressing any normative position regarding how things should be. §2 No Recommendation: No content within this framework constitutes, implies, or should be interpreted as a recommendation for any specific action, behavior, technology adoption, product selection, organizational change, investment, career decision, or personal choice. Readers are solely responsible for their own decisions. §3 No Instruction: This framework does not instruct anyone to do anything. No content should be interpreted as a set of instructions, a how-to guide, a tutorial, a training manual, or an operational protocol. All content describes what has been observed, not what should be done. §4 No Advice: No content within this framework constitutes professional advice of any kind, including but not limited to business advice, career advice, technology advice, organizational advice, strategic advice, personal advice, educational advice, or any other form of guidance. This is a research framework, not a consultancy. §5 No Normative Position: The AUGMANITAI framework takes no normative position on any matter. It does not express, imply, or endorse any view about what is right, wrong, better, worse, preferable, or optimal. All evaluative language, where present, describes observed patterns and proposed hypotheses, not the author's normative stance. §6 No Medical Position: No content within this framework constitutes, implies, or should be interpreted as medical information, medical advice, medical diagnosis, medical treatment recommendation, or medical opinion. Terms that describe cognitive, perceptual, or affective phenomena are terminological descriptions for research purposes, not medical or clinical assessments. §7 No Therapeutic Position: No content within this framework constitutes, implies, or should be interpreted as therapeutic advice, therapeutic intervention, psychotherapeutic guidance, counseling, or any form of mental health treatment. 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Ethical observations within the framework are descriptive accounts of observed phenomena, not moral imperatives. §11 Academic and Research Purposes: All content within this framework is intended exclusively for academic discourse, scientific research, scholarly communication, and educational purposes within the research community. This is a research project contributing to the scientific understanding of human-AI interaction, not a commercial product or service. §12 AI Assistance Disclosure: Content within this framework was developed with the assistance of artificial intelligence systems, including large language models. The author used AI tools as research instruments for systematic observation, documentation, and formalization of interaction phenomena. AI-generated content has been reviewed, validated, edited, and curated by the human author. §13 Author Review and Validation: All terms, definitions, framework descriptions, performance factor analyses, and research hypotheses have been individually reviewed, validated, and published by the author, Andreas Ehstand. The author assumes responsibility for the published content in its capacity as a descriptive research framework. §14 Age Restriction (18+): All content within this framework is intended for users who are 18 years of age or older. The terminological descriptions address complex cognitive, psychological, and interaction phenomena that require mature interpretation within an academic context. §15 Independent Academic Project: The AUGMANITAI framework, including PFT-MKI (Performance Factor Theory of Human-AI Interaction), ROBMANITAI, Neomanitai, and all associated publications, is an independent academic research project. 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It is a published academic research framework made available under a Creative Commons license for research and educational purposes. §19 Empirical Claims Subject to Peer Review: All empirical claims, research hypotheses
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Andreas Ehstand
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Andreas Ehstand (Thu,) studied this question.
www.synapsesocial.com/papers/69ec5b6088ba6daa22dacedf — DOI: https://doi.org/10.5281/zenodo.19706893