The Abductive Mirror: A Conceptual Model of Human-Al Synergy for Creative Innovation

ABSTRACT While the literature on human-AI collaboration focuses on prompt optimization, it lacks an epistemic framework to explain the genesis of conceptual innovation. To bridge this gap, The Abductive Mirror is proposed as a cyclical model that unites human and computational abductive inference. The cycle consists of three phases: (1) the human formulates an Intentional Abduction (a semantically dense hypothesis); (2) the LLM performs Hypothetical Amplification by recombining latent patterns; and (3) the human conducts Assimilation and Calibration, generating a qualitative leap in knowledge. The informational gain (Δ) from this process is objectively measurable. The paper argues that language is a necessary condition for shared abstraction, leading LLMs to operate on "second-order grounding." The work itself is a performative proof of the model, as it documents the "micro-shifts" that guided its creation and includes validation on an external case study. CORE CONTRIBUTIONS The "Abductive Mirror" Model: The paper introduces a structured, cyclical model for epistemic innovation that aims to structure human-LLM collaboration. The cycle is composed of defined phases: an Abductive Trigger (At), Hypothetical Amplification (M), Assimilation & Calibration (F), and an evaluation of the informational gain (Δ). First and Second-Order Grounding: A key theoretical distinction is proposed to explain the synergy. The human provides "first-order grounding," which is anchored to direct sensory and motor experiences, while the LLM operates on "second-order grounding," based on quasi-conceptual abstractions learned from text corpora. Language serves as a "Semiotic Bridge" between these two modalities. Performative Methodology: The paper itself acts as the primary case study and is a "performative demonstration of its own thesis." The model was followed during the writing process, and conceptual "micro-shifts" are documented as evidence of informational gain. VALIDATION METHODS Internal Case Study: The paper's creation is tracked to document self-referential "micro-paradigm shifts." The resulting gains were measured in terms of conceptual coverage (+40%), formal precision (+80%), and transferability (+90%). External Case Study: To test for generalizability, the model was applied to a design problem involving the creation of an ethical recommendation system. Success was measured using Bayesian, MDL, and Surprise metrics. Comparative Benchmark: A pilot study found that "The Abductive Mirror" method had a success rate (67%) that was statistically superior (p < 0.001) to that of the "Human Only" (34%) or "LLM Only" (41%) methods.