The Creativity Conjecture: Innovation Mechanisms of Experience Training, Randomness, and Multi-Level Data Fusion

The “creative” outputs of artificial intelligence systems—from GPT composing poetry to AlphaFold predicting protein structures—raise a philosophical question: where does creativity come from? Is it simple reproduction of training data, or a chance product of stochastic algorithms? This paper proposes “The Creativity Conjecture”: the creative output of an AI system is neither pure reproduction of training data nor pure random generation, but an emergent phenomenon arising from “experience training” and “random perturbation” under a “multi-level data fusion” mechanism. The conjecture comprises three conditions: (1) empirical density—the system has internalized sufficient pattern structure from training data; (2) random perturbation—the system introduces randomness of appropriate intensity during generation; (3) fusion depth—the system possesses the capacity for cross-level, cross-modal data fusion. All three are necessary: randomness without experience is noise; experience without randomness is copying; experience plus randomness without fusion is collage. Only when all three fuse does “structured surprise”—i.e., creativity—emerge. This paper argues for the conjecture on three levels: structural analysis (the mechanism of each element), fusion mechanism (how the three couple to produce creative emergence), and experimental verifi- cation (the effects of randomness intensity and fusion depth on creative output). We further propose the concept of “weak creativity” to delineate the epistemological status of machine creativity—it transcends random generation and data reproduction, yet does not reach the level of human phenomenal creativity. This framework provides a structural foundation for understanding AI creativity and offers a new path for the philosophy of creativity, moving from “genius theory” to “structural theory.”