Admissibility-First Reasoning: A Trilogy on Human-AI Scientific Collaboration

This archive contains a trilogy of methodological papers addressing a missing layer in scientific and AI-assisted reasoning: explicit admissibility.

Across many scientific domains, intelligence, data volume, and computational power have increased dramatically, yet foundational understanding has advanced more slowly. This work argues that the limiting factor has not been intelligence, but permission. Admissibility—the set of operations, representations, and assumptions allowed during reasoning—is typically inherited implicitly through models, conventions, and institutional practice rather than declared explicitly. In mixed human–AI reasoning, this implicitness produces apparent incoherence, misattributed AI failure, and stalled discovery.

Paper I introduces Admissibility-First Reasoning as a general framework in which permission is declared prior to inference and rigor is defined by stability under translation across reasoning agents. Paper II operationalizes this framework through Gamma Forensics, a conservative and falsifiable pipeline for reconstructing shared latent structure from heterogeneous, noisy data without assuming physical mechanisms or ontologies. Paper III diagnoses why AI has not accelerated foundational discovery, showing how model-first admissibility constrains both human and artificial reasoning and reframing AI failure as diagnostic information rather than defect.

This collection does not propose new physical theories, assert causal mechanisms, or grant authority to AI outputs. Its contribution is methodological. It provides a transparent, auditable approach to governing admissibility so that failure becomes informative, safety aligns with rigor, and discovery is limited only by evidence rather than by invisible permission structures.

If you cite this work, please reference this archive as a methodological trilogy on admissibility-first reasoning.