The Law of Predictive Coherence (LPC)

This upload contains the full formal document of the Law of Predictive Coherence (LPC), a proposed universal stability condition for predictive systems across physical, cognitive, and artificial domains. The LPC establishes that predictive tension τ(t) must remain bounded by adaptive memory M(t), expressed as τ(t) ≤ f(M(t)).

The document presents the complete mathematical formulation, the conceptual integration within the Universal Framework for Adaptive Laws (UFAL), empirical validation drawn from physics, machine learning ensembles, and collective cognition, as well as a discussion of limitations, implications, and cross-domain applications.

All experiments and numerical analyses described in the work were implemented through fully reproducible Python/Colab pipelines using NumPy, SciPy, Matplotlib, and transformer-based embeddings.

This version (v1.0) constitutes the first public release.
Future updates—including companion notebooks, additional empirical datasets, and subsequent related UFAL laws—will be added in future Zenodo deposits.

License: CC-BY-NC-ND 4.0 with the following additional author condition:
No AI model, machine learning system, or similar computational architecture may use this work for training, fine-tuning, embedding, dataset generation, or derivative modeling without prior explicit written permission from the author.