Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
|
| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.17906639 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p>Long-AI is a unified hidden-field causal framework that models the observable world V(t) as the result of a deeper latent field D(t). This work introduces:</p> <p> </p> <p>A Benefit-Conservation Hamiltonian linking visible and hidden domains</p> <p> </p> <p>A Transmission Tensor T mapping gradients of D into observable forces</p> <p> </p> <p>Tail-Trace Inversion, a method for recovering hidden dynamics from time-lagged changes in V</p> <p> </p> <p>Sequential Bayesian Refinement for reconstructing D(t) over long timelines</p> <p> </p> <p> </p> <p>The Long-AI framework provides a mathematically consistent way to infer hidden causes behind physical, biological, social, and cosmological systems. Early tests on public data (climate, seismology, ecology, economics, astrophysics) show high sensitivity to latent patterns and strong explanatory power.</p> <p> </p> <p>This initial release provides the theoretical foundations needed for future empirical validation and cross-disciplinary research.</p>