# SOV33 OWEM — real model or wrapper? Verified in-window (2026-07-12)
_Today's goal: build SOV33 as a real OWEM (Open World Emergence Model), not a wrapper. This is the honest verdict._

## THE TEST: does SOV33 have its OWN weights that learn + don't forget?

### 1. World predictor — HAS OWN WEIGHTS, MEASURABLY LEARNS ✅ (small scale)
- `JEPAPredictor` (sov33_owem_world_model.py): 16-dim state → 32 hidden → 16-dim next-state prediction.
- OWN weights: W1/W2 matrices, self-initialized (He), updated by gradient step. NOT an API wrapper.
- MEASURED this window: on a learnable next-state task, mean loss 1.11 → 0.51 over 5 epochs = 54.6% reduction.
  => It genuinely learns a signal. This is the crux: SOV33 owns trainable weights here, and they improve.
- HONEST SCALE CAVEAT: 16→32→16 is a TOY predictor. Right architecture (JEPA direction), tiny scale.
  Gradient rule is a hand-coded approximation (W2-only, error×0.1), not full backprop. Proves
  "has own weights that learn" — NOT "competitive world model".

### 2. EWC continual learning — REAL STRUCTURE, PROXY FISHER ⚠️
- `EWCContinualLearner`: real methods (compute_fisher_from_grads, ewc_loss, should_allow_update, snapshot).
- HONEST CAVEAT: at line ~234 Fisher information is approximated from WEIGHT MAGNITUDE (proxy), not the
  true gradient-of-log-likelihood (Kirkpatrick 2017). The no-forgetting STRUCTURE is real; the Fisher
  ESTIMATE is a proxy. Do not claim "full EWC" — claim "EWC-structured consolidation with proxy Fisher".

### 3. No catastrophic forgetting — ARCHITECTURALLY GUARANTEED ✅
- The base open model is FROZEN. New capability = memory + adapters on top. A frozen base cannot
  catastrophically forget by construction (no weights to overwrite). This claim is sound independent of (2).

## VERDICT (honest)
SOV33 is MORE than a wrapper: it owns a trainable world-predictor (verified learning) + an EWC-structured
consolidation layer + a growth controller (6 invariants, 10 lineages measured) + governance gates. It is
NOT yet a competitive foundation model — the sovereign-owned weights are small/toy scale. The accurate
public claim: "a governed sovereign substrate with its OWN (small) learning world-model, growing by
accretion on frozen open weights" — NOT "new foundation model" / "AGI" / "beats GPT".

## WHAT WOULD MAKE IT BIGGER (owner/GPU-gated, not code-gated)
- The overnight qwen3-0.6b-sov-compliance fine-tune (own weights at real LM scale) — needs confirming it
  landed in ~/.sovereign (Mac-side; sandbox cannot read that path).
- The Kaggle GSM8K capability grade — converts governance wins into a capability number.
- GPU for the 3 remaining experts (Defense/Intuition/Voice — data ready).

## HARDENING DONE THIS WINDOW
- sov33_owem_world_model.py: fixed the ~/.sovereign import-time write (SOV33_SIGIL_DIR env-override,
  fail-soft) so the real OWEM module imports + runs in-sandbox. Same fix pattern as the 51-component batch.

## SEPARATION-OF-CONCERNS — STRUCTURAL CLAIM ONLY (corrected 2026-07-12, NOT a live-swap proof)
Nick's framing: "the proof SOV33 works is that when we swap models in OWEM, the memory stays the same." The
right instinct — but the test I first ran (sov33_swap_persistence_proof.py) does NOT prove it. That test only
set os.environ['SOV33_OLLAMA_MODEL'] across 3 names and re-read a STATIC memory file; NO model was ever
invoked. Identical hashes were a tautology of construction (memory never rewritten between reads), not
evidence a real swap preserves the substrate. RETRACTED as a "proof".

WHAT IS ACTUALLY TRUE (structural, verifiable by code inspection):
- Memory lives in a FILE (SIGIL_DIR/sovereign_memory.jsonl); model choice is an ENV VAR (SOV33_OLLAMA_MODEL).
- They are architecturally decoupled — the memory store does not depend on which model is selected.
- This is a real STRUCTURAL property (separation of concerns), NOT a demonstrated live-swap invariance.

WHAT A REAL PROOF NEEDS (owner/endpoint-gated, not done):
- Actually invoke each lineage via a live inference call running a real sovereign turn that writes to
  memory/SIGIL as in production, THEN show invariants + prior episodes are unchanged while only the new
  model's answer differs. Requires reachable endpoints (Oracle/Ollama/Groq) — absent in sandbox.