Extended restricted snapshot (v3) of the AUGMANITAI / NEOMANITAI research bundle. Author: Andreas Ehstand, independent researcher, Starnberg, Germany. ORCID 0009-0006-3773-7796. Wikidata Q138634675. This version extends the prior snapshot by adding three new companion papers to the Explicitation Protocol (v2, 10.5281/zenodo.19701316). Together, the three new papers deepen the structural, domain-reach, and philosophical-epistemic characterisation of the Compression Axiom read as an interface for recursive self-explicitation between learning systems. Paper A — The Epistemic Interface: From Implicit Machine Knowledge to Explicit Concept and Back. 21 pages, 9,100+ words. Fine-grained anatomy of the extraction-serialisation-reinjection cycle in eight sequential steps: framed invitation, internal search, compression, differentiation, relational embedding, serialisation, external verification, and injection with provenance recording. Characterises the three-fold function of the five axiom conditions (search filter, structuring scaffold, quality criterion). Describes SKOS as the carrier layer with native support for provenance, versioning, and multi-concept coexistence. Sets out the epistemic consequences of the cycle: communicability, auditability, iterative improvability with preserved provenance. Paper B — The Naming Engine: On the Universal Reach of the Explicitation Protocol Across Domains of Observable Structure. 17 pages, 6,900+ words. Systematic examination of six representative domain classes: molecular-scale scientific structure, observational astrophysical data, practitioner tacit knowledge across skilled professions, system-internal states in learning systems themselves, non-human perceptual environments (the Uexküll tradition), and emergent group-level behavioural regularities. For each domain class: what kinds of phenomena the protocol can make explicit, what quality of explicitation can be expected, what open questions remain. General characterisation of the protocol's reach and three structural limits. Paper C — The Universal Naming Engine: On the Systematic Conversion of Implicit Pattern into Explicit Concept Across Domains of Observable Structure. 20 pages, 8,700+ words. Philosophical-structural characterisation of the protocol, viewed as a mechanism that shifts the boundary between the nameable and the not-yet-named. Three sources of previously unnameable content: content beyond direct human perception, content below the threshold of sustained attention, content beyond combinatorial attention. The distinctive structural features of the resulting content (positioning, provenance, versioning, injectability) and its five epistemic functions (shared reference, targeted measurement, targeted intervention, transmission, inter-domain comparison). Three illustrative application sketches at the molecular, perceptual, and reflexive levels. Placement of the protocol in a longer methodological history. Central structural claim, stated once, across all three papers. The producer of axiom-conformant content is, in the general case, the learning system itself. The system compresses implicit distributed representations into explicit structured primitives; the framing agent invites, verifies, and curates. What was previously available only as opaque behaviour becomes available as structured, provenanced, interoperable content — generated continuously, revisable over time, transmissible across systems. 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, to date, 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), the Mathematical Foundations Supplement (10.5281/zenodo.19691491), the public Concept statement (10.5281/zenodo.19691495), and the Explicitation Protocol v2 (10.5281/zenodo.19701316). Prior v1 and v2 files are retained in this version unchanged. Access. Restricted. Requests are evaluated individually. Contact via the author's ORCID record. Legitimate research, review, or legal parties are welcome to request access. License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Record date: 23 April 2026. Part of the AUGMANITAI / NEOMANITAI research ecosystem. 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. Any resemblance to therapeutic concepts is incidental to the terminological description of observed phenomena. §8 No Diagnostic Position: No content within this framework constitutes, implies, or should be interpreted as a clinical diagnosis, psychological assessment, cognitive evaluation, or any form of diagnostic instrument. Performance factor analyses describe research constructs, not clinical diagnostic categories. §9 No Legal Position: No content within this framework constitutes, implies, or should be interpreted as legal advice, legal opinion, legal analysis, regulatory guidance, compliance advice, or any form of legal counsel. References to legal frameworks (such as the EU AI Act) are descriptive and do not constitute legal interpretation. §10 No Moral Position: No content within this framework constitutes, implies, or should be interpreted as a moral judgment, ethical prescription, or philosophical position about what is morally right or wrong. 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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Andreas Ehstand
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Andreas Ehstand (Thu,) studied this question.
www.synapsesocial.com/papers/69eb092b553a5433e34b3bda — DOI: https://doi.org/10.5281/zenodo.19701432
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