Stages: rename titles, fix execution order, remove all numbered references

projects/passepartout/architecture/knowledge-layers/neurological-empirical.org

@@ -109,13 +109,13 @@ All three are handled by the same provenance store, the same gate predicates, an
 
 **Where this fits in the stage plan.**
 
-- **Stage 0-1**: The provenance store does not exist. Neural network models are loaded as black boxes with no systematic validity checking. This is current practice in computational science — the user is responsible for knowing whether a model applies to their problem.
+- **Development to Neurosymbolic Agent**: The provenance store does not exist. Neural network models are loaded as black boxes with no systematic validity checking. This is current practice in computational science — the user is responsible for knowing whether a model applies to their problem.
 
-- **Stage 2**: The provenance store begins operation. Initially it handles traditional symbolic-fitted models because they have clear provenance chains and validity envelopes. Neural network models require the distribution match infrastructure, which is a separate development track.
+- **Social Protocol**: The provenance store begins operation. Initially it handles traditional symbolic-fitted models because they have clear provenance chains and validity envelopes. Neural network models require the distribution match infrastructure, which is a separate development track.
 
-- **Stage 3**: The distribution match infrastructure is operational. The gate can check whether an input is within a neural network's training distribution. The provenance store holds training dataset descriptions, validation benchmarks, and distribution summary statistics for each supported neural network model.
+- **Lisp Machine**: The distribution match infrastructure is operational. The gate can check whether an input is within a neural network's training distribution. The provenance store holds training dataset descriptions, validation benchmarks, and distribution summary statistics for each supported neural network model.
 
-- **Stage 4+**: Neural network models are loaded into the same address space as the symbolic engine and the provenance store. The distribution match check runs at the level of the evaluation loop itself. The gate's validity check becomes a fast native predicate — no querying a separate data store, just reading a hash table and computing a distance in the same process.
+- **AI Inference onwards**: Neural network models are loaded into the same address space as the symbolic engine and the provenance store. The distribution match check runs at the level of the evaluation loop itself. The gate's validity check becomes a fast native predicate — no querying a separate data store, just reading a hash table and computing a distance in the same process.
 
 **The summary.**