The Architect's Report: Connective Abduction as Research Methodology in the Age of AI-Assisted Knowledge Production

Abstract

(Part 3 of a 3-part series.) This paper proposes and validates a research methodology termed connective abduction: the systematic identification of novel connections between existing, independently verified knowledge claims, with AI systems serving as the verification infrastructure that tests whether those connections hold logically and empirically. Unlike traditional research, which requires deep domain expertise to both generate and validate hypotheses, connective abduction decouples these functions: the human researcher identifies explanatory gaps and cross-domain patterns through abductive reasoning—connecting dots that exist in the published literature but have not been previously linked—while AI systems verify the logical soundness and empirical support of each proposed connection against the referenced sources. A hierarchical adversarial verification protocol is formalized, applying the cascade filtering principle (Ahn, 2026a) to research methodology: multiple independent AI verification layers, each applying progressively stricter evaluation criteria, reduce hallucination propagation risk logarithmically rather than linearly. The methodology is validated through a single-case study in which the present author—whose formal training consists of incomplete undergraduate coursework in chemistry and an in-progress bachelor's degree in computer science—produced two cross-disciplinary research manuscripts within approximately two days. This case is presented not as evidence of individual capability but as a diagnostic case for a structural transformation in knowledge production: a methodological phase transition in which the binding constraint on research shifts from knowledge accumulation to question design. Risks of the transition—including verification deficits, hallucination propagation, depth erosion, and structural uncontrollability—are addressed, and transparent reporting of AI-assisted methodology is proposed as a necessary component of the emerging paradigm. Revision Note (v5) v4 → v5 Changes: Section 7.3: Added contextual blindness as a distinct AI failure mode — AI systems may produce logically valid inferences while missing domain constraints that any informed human would apply. Illustrated with the car wash example (walking to a car wash because it is nearby) documented in early LLM evaluation literature. Implication: domain expert review is not redundant under connective abduction; its function relocates from producing connections to validating contextual fit.

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