Heuristic Layering: Structuring AI Systems Beyond End-to-End Models
Rogério Figurelli · Preprints.org · 2025
Heuristic layering is introduced as a conceptual and architectural alternative to end-to-end modeling in artificial intelligence. Instead of treating intelligent systems as monolithic pipelines optimized solely through data-driven processes, this paradigm organizes AI into explicit, modular layers — each governed by transparent heuristics tailored to distinct cognitive or computational functions. The approach offers a pathway to increased interpretability, flexibility, robustness, and adaptability, addressing core limitations of end-to-end systems such as global brittleness and lack of targeted auditability. By mapping clear interfaces between layers and enabling local updates without retraining the entire system, heuristic layering supports both incremental innovation and systematic error isolation. This article frames the theoretical foundations of heuristic layering, draws parallels with modular design in software engineering and cognitive science, and discusses scenarios in which layered architectures may surpass traditional end-to-end models in transparency, maintainability, and domain transferability. The conclusion outlines future research avenues for the taxonomy, formal patterns, and practical deployment of heuristic-layered AI systems.