AUGMANITAI / NEOMANITAI — Sport-Performance-Informed Methodology, Coupled-Performance Working Papers, and The Explicitation Protocol (Extended Restricted Snapshot v2, 2026-04-23)

Extended restricted snapshot (v2) 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 the new Explicitation Protocol paper (NEOMANITAIExplicitationProtocolᵥ1. 0Ehstand₂026-04-23. pdf), which develops a structural consequence of the Compression Axiom that the public concept statement (10. 5281/zenodo. 19691495) did not make fully explicit: the axiom is not primarily a specification for the well-formed definitions that human practitioners should produce. It is primarily an interface specification for a recursive self-explicitation protocol between learning systems. Central structural claim of the Explicitation Protocol paper. The producer of an axiom-conformant definition need not be a human. The producer is, in the general case, the learning system itself, directed to compress, name, and structure a phenomenon that the system already handles implicitly in its trained representations. A framing agent — human or algorithmic — contributes the invitation to explicate, verifies conformity with the five conditions of the axiom, and routes the resulting primitive where it can be reused. The invention of the concept, the compression of implicit signal into structured form, and the act of naming are the system's work. Paper contents. The four-step explicitation cycle (implicit representation formation, framed explicitation, explicit injection, recursive return) ; a ten-way structural distinction from neighbouring paradigms (classical symbolic AI including Cyc, pure deep learning, concept-based XAI, Concept Bottleneck Models, Multi-Dimensional Concept Discovery, mechanistic interpretability via sparse autoencoders, Patchscopes, Predictive Concept Decoders, neuro-symbolic AI, weight-space learning, recursive self-improvement, constitutional AI, active learning, meta-learning) ; three structural consequences for world-model learning (compute redirection, interoperability by construction, interpretability at the input layer) ; a candidate experimental protocol with three conditions and five metrics; an inter-system exchange framework for multi-agent coordination at the primitive layer; three constructive applications (scientific discovery, cross-disciplinary translation, longitudinal self-observation) ; three stated limitations (explicitability is not universal, quality depends on framing competence, primitive layers evolve over time) ; full bibliography. 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. Relation to prior deposits. Companion to the Technical Paper (10. 5281/zenodo. 19691489), the Mathematical Foundations Supplement (10. 5281/zenodo. 19691491), and the public Concept statement (10. 5281/zenodo. 19691495). Prior v1 files of the Sport-Performance-Informed Methodology Bundle 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. 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