E1P in AI: The Dynamics Beneath the Physics — Model Collapse as Coherence Failure

Recent theoretical work has established model collapse as a phase transition phenomenon, attributing it to thermodynamic constraints inherent in physical substrates. We propose an inversion: model collapse is not a materiality problem — it is a coherence problem.

The Energetic First Principles (E1P) framework demonstrates that collapse occurs when the balance between Active (differentiation) and Connective (integration) components is lost. We validate this through 16 experiments across three hardware architectures (GPU, LPU, Wafer-Scale Engine), seven model families, and an 870x parameter range (270M to 235B).

Key findings:
- τ ≈ 0.5 is a fundamental threshold — all standard-trained models cross it under accumulation pressure
- Hardware is irrelevant — same model on different hardware produces identical τ crossing
- Three paths to coherence resilience exist: reasoning training (Kimi K2), extreme scale (Qwen3 235B), and architecture design (GLM-4.6)

These resilient models are not violations of E1P: they confirm that coherence, not materiality, is the operative variable. Design for A/C balance, and collapse is avoidable.