UTL: Universal Law of Transition – Antifragile Regularization through Hazard and Latent Geometry
Why UTL?
Training neural networks is not a smooth process. Models sometimes “break” — they lose stability, overfit, or suddenly discover a new balance. UTL attempts to capture that transition moment mathematically.
But UTL also goes further: the same structural dynamics that describe transitions in AI training also appear in human experiences of transformation — most vividly in near-death experiences.
The Core Idea in One Image
UTL combines three key signals:
S(t): latent spread (the “surface” of representation)
D(t): latent depth (structural variability)
R_val(t): validation noise
Together they form the effective stimulus:
E∗(t)=S(t)⋅D(t)Rval(t)+ϵE^*(t) = \frac{S(t) \cdot D(t)}{R_{val}(t) + \epsilon}E∗(t)=Rval(t)+ϵS(t)⋅D(t)
From there, UTL defines:
Loss(t): the training error + an antifragile regularization term
h(t), Θ: the hazard function and cumulative stress threshold that signal phase transitions
Why Does This Matter?
For AI/ML: UTL acts as a new kind of dynamic regularizer, antifragile and geometry-aware.
For consciousness studies: UTL provides a mathematical language to map subjective reports of fragmentation vs. harmony in NDEs.
For interdisciplinary research: It creates a bridge where engineers, scientists, and philosophers can meet.
What’s Next?
UTL is released as a testable hypothesis:
Engineers can implement it in PyTorch or TensorFlow.
Researchers can compare it against datasets of NDE narratives.
Theorists can explore it as a candidate universal principle of phase transitions.
Conclusion
This is not a final answer — it’s an invitation to dialogue.
UTL is my contribution to the architecture of ideas — a step between typography, art, and science.
👉 Read the full preprint here: Zenodo
