FULPs in Artificial Intelligence: A Developmental Training Paradigm with Dual-Belief Representation Learning

This paper presents a proof-of-concept implementation of the Fundamental Universal Learning Patterns (FULPs) as a developmental training architecture for artificial systems. Grounded in the companion theoretical work, we instantiate the FULPs as a staged pipeline, the Emergent Development Horizon (EDH), in which a model begins from a null state, acquires a stable representation of absence, and builds structured beliefs through a novel dual-belief revision mechanism: the Assumption Resonance Engine (ARE). The pipeline is evaluated against a comparably sized LSTM across five test conditions, using both controlled synthetic data and a real-world water quality dataset. The central finding concerns structural generalization: across 30 independent runs, the FULPs model achieves a mean shifted-pattern accuracy of 0.585 versus 0.526 for the LSTM (t = 2.62, p = 0.011), while degrading from in-distribution performance by a mean of 0.281, compared to 0.386 for the baseline (t = -2.21, p = 0.031, Cohen's d = -0.58). This is not a claim of superiority. It demonstrates that developmental ordering produces a qualitatively distinct relationship between a model and its environment, one in which structural generalization is partially retained at the cost of peak in-distribution performance. Both successes and failures are documented.