extended Shannon entropy with a learning observer. Here'sā¦
0 reactions
extended Shannon entropy with a learning observer. Here's what I built.
Classical Shannon entropy H(X) is observer-agnostic. It doesn't model what happens when an observer learns over time.
I added exactly that:
**H_lambda(X,t) = H(X | M_t)**
As the observer's model M_t improves, residual uncertainty drops. The system tracks this in real time.
The result is Aether ā a local analysis and reconstruction framework that combines:
- Observer-relative residual uncertainty
- Structural invariants (symmetry, periodicity, Fourier)
- Bayesian + graph state layers
- Reconstruction conditions (snapshot + residual)
- Local governance and security
During development, the evolutionary subsystem (AELAB) identified Ļ as a recurring structural anchor in raw binary files. This is documented honestly in the whitepaper ā as an observed phenomenon, not a proven theorem.
Full system + whitepaper (source-available):
https://github.com/stillsilent22-spec/Aether-
Looking for serious feedback from people working in information theory, complexity or observer-dependent systems.
#technology
Classical Shannon entropy H(X) is observer-agnostic. It doesn't model what happens when an observer learns over time.
I added exactly that:
**H_lambda(X,t) = H(X | M_t)**
As the observer's model M_t improves, residual uncertainty drops. The system tracks this in real time.
The result is Aether ā a local analysis and reconstruction framework that combines:
- Observer-relative residual uncertainty
- Structural invariants (symmetry, periodicity, Fourier)
- Bayesian + graph state layers
- Reconstruction conditions (snapshot + residual)
- Local governance and security
During development, the evolutionary subsystem (AELAB) identified Ļ as a recurring structural anchor in raw binary files. This is documented honestly in the whitepaper ā as an observed phenomenon, not a proven theorem.
Full system + whitepaper (source-available):
https://github.com/stillsilent22-spec/Aether-
Looking for serious feedback from people working in information theory, complexity or observer-dependent systems.
#technology
| 4,000 mlx | |
| 8 | |
| 0 |