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February 12, 20260 citationsOpen Access

Solarium: A Structural Pre-Audit Protocol for Language Acquisition Scalability

YHYao-Hui Huang

Key Points

  • The aim is to establish a protocol that assesses a learner's system capacity for skill scaling and diagnose failure modes.
  • Defines a non-instructional audit protocol.
  • Examines client-provided artifacts like test outputs and writing samples.
  • Classifies outcomes as scalable/non-scalable and identifies bottleneck locations.
  • Outputs a structural verdict indicating whether the learner is pre-threshold or post-threshold.
  • Identifies irreversible plateau indicators.
  • Classifies failure modes for skill acquisition.

Abstract

Abstract Solarium defines a one-off, non-instructional audit protocol for diagnosing skill-scaling failures in human learning systems (e.g., language proficiency, exam performance, or professional skill acquisition). The protocol does not teach, coach, prescribe plans, or provide behavioral guidance. Its sole function is to identify whether a learner’s current system is structurally capable of scaling beyond a defined threshold, and to classify failure modes when it is not. Solarium outputs a structural verdict (e.g., “pre-threshold / post-threshold,” “scalable / non-scalable,” “bottleneck location,” “irreversible plateau indicators”) based on client-provided artifacts (test outputs, writing samples, transcripts, or documented learning workflows). The audit is point-in-time and informational: it makes no performance guarantees, has no update obligation, and establishes no ongoing advisory relationship. This Zenodo record serves as the canonical reference for the definition, scope boundaries, and output format of the Solarium Structural Pre-Audit Protocol (v1.0). Keywords: structural audit; learning systems; skill scaling; plateau diagnosis; non-instructional; point-in-time analysis; irreversibility; error taxonomy; English learning audit; TOEIC; IELTS

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Cite This Study

Yao-Hui Huang (2026) studied this question.

synapsesocial.com/papers/698d6e1a5be6419ac0d538edhttps://doi.org/10.5281/zenodo.18598498
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Also Consider

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  5. 5Silent evaluator artefacts in cross-vendor and multilingual clinical AI benchmarking: an existence-proof taxonomy of runtime and fixture-generation failure classes2026