Evidence
The reproducible SAE-1.4 validation package — read it here, or download the full code and data.
This is the artifact behind the validation status on the equation page: the reference implementation, the 5,000-trial structural harness, the synthetic-data generator, and the full empirical suite — including the openly-published null result. Everything is reproducible end to end with fixed seeds.
↓ Download SAE14_analysis (code + data, .zip)Includes sae14.py, test_structural.py, gen_data.py, analyze.py, the synthetic dataset, and the canonical spec.
Validation summary
Condensed from Awareness Engineering v0.7. The failed sub-claim is shown openly — structural passing is not the same as real-human validation.
| Test | Result |
|---|---|
| Structural invariants (5,000 trials) | all PASS |
| Out-of-sample fit (synthetic) | R² ≈ 0.896 (components) vs ≈ 0.156 baseline; 0.584 fixed-form |
| Directionality (cross-lag) | Blockers(t)→Awareness(t+1): β = −6.19, p ≈ 3e−11; reverse null |
| Falsification (shown openly) | “Purity(t)→fewer future Blockers” did NOT hold (β ≈ 0, p ≈ 0.91) |
| Fairness (matched subgroups) | high- vs low-constraint differ by only +2.70 / 100 |
README
SAE14_analysis/README.md# SAE-1.4 Validation Package A rigorous structural + empirical test of the Sanatan Awareness Equation v1.4, run against honestly-generated synthetic HOPE-style logs. ## Files - `sae14.py` — canonical SAE-1.4 reference implementation (all clamps, coherence gate, stage gate, overdrive metric, display transform) - `test_structural.py` — 5000-trial randomized invariant property tests - `gen_data.py` — synthetic data generator (200 users x 120 days = 24,000 rows) - `analyze.py` — full empirical suite (model comparison, stratification, causality, fairness) - `hope_synth.csv` — the generated dataset - `results_output.txt` — captured run output ## Critical methodological choice The ground-truth synthetic world uses a DIFFERENT functional form than SAE-1.4 (saturating tanh in purity, multiplicative blockers, smooth belief gate, diminishing shakti returns, heavy-tailed additive grace). SAE-1.4's additive bracket is only an APPROXIMATION of it. This avoids circularity: SAE-1.4 is not being tested against data it generated. It must compete to recover an independent causal structure out-of-sample. Crucially, the ground truth was built so that PHYSICAL CONSTRAINT does NOT reduce inner awareness (only an expression channel), to test the spec's fairness requirement. ## Findings 1. STRUCTURAL: all invariants PASS over 5000 trials (monotonicity in blockers/purity/ constraints, range bounds, stage gate). 2. PREDICTIVE: SAE-1.4 components model reaches R^2 = 0.896 out-of-sample (walk-forward, train day<80 / test day>=80) vs best baseline 0.156. SAE earns its keep on data it did not generate. 3. STRATIFICATION CONFIRMED: corr(Purity, Awareness) = +0.489 in positive-belief regime vs +0.403 pooled. Belief regime must be conditioned on, exactly as the spec warned. 4. DIRECTIONALITY: Blockers(t) -> Awareness(t+1) strong (beta=-6.19, p~3e-11); reverse null (p=0.93). Arrow points as the model claims. 5. FALSIFICATION (important): "Purity(t) reduces future Blockers(t+1)" did NOT hold (beta~0, p=0.91) — because that pathway was not built into the ground truth. This is the model being genuinely falsifiable. If real HOPE data shows the same null, that sub-claim must be dropped or rebuilt. 6. FAIRNESS: after matching on Purity+Belief, high- vs low-constraint users differ by only +2.70 on a 0-100 scale. Small residual leak (mental-constraint/blocker confound), within fair range, but flags the constraint split for careful design. ## Honest scope This validates that SAE-1.4 is STRUCTURALLY COHERENT, FALSIFIABLE, and BEHAVES CORRECTLY, and that it can recover non-trivial structure better than baselines. It does NOT validate that SAE-1.4 describes real consciousness — the data is synthetic. Real validation requires real (even anonymized) HOPE logs of >=60-120 days with a logged ObservedAwareness proxy (e.g., a daily 0-100 Witness-access/clarity/steadiness rating). ## Next step with real data Provide anonymized HOPE log columns matching the schema in `gen_data.py`. Then: compute SAE outputs, fit weights under the spec's definitional constraints, run walk-forward validation against baselines, rank blocker/purity dimensions by predictive leverage, and characterize the empirical grace (epsilon) distribution.
Captured run output
SAE14_analysis/results_output.txt====================================================================== B. MODEL COMPARISON (walk-forward: train day<80, test day>=80) ====================================================================== model MAE R2(oos) B0 constant 11.932 -0.001 B1 purity-only 10.129 0.156 B2 blockers-only 11.841 0.010 B3 wellness(phys+mental) 11.865 0.012 SAE-1.4 raw composite 7.831 0.584 SAE-1.4 components 4.191 0.896 SAE-1.4 + interactions 5.217 0.842 ====================================================================== C. BELIEF-REGIME STRATIFICATION (the key insight) ====================================================================== POOLED (all) corr(Purity,Aw)=+0.403 corr(Blockers,Aw)=-0.108 n=24000 POSITIVE belief corr(Purity,Aw)=+0.489 corr(Blockers,Aw)=-0.132 n=16473 NEGATIVE belief corr(Purity,Aw)=+0.244 corr(Blockers,Aw)=-0.078 n=7527 Note: pooling positive+negative regimes can flip/wash signs — must stratify. ====================================================================== D. CROSS-LAG / DIRECTIONALITY (does Blockers(t) lead Awareness(t+1)?) ====================================================================== Blockers(t) -> Awareness(t+1): beta=-6.189 (p=3.4e-11) Awareness(t) -> Blockers(t+1): beta=-0.000 (p=9.3e-01) Purity(t) -> Blockers(t+1): beta=+0.000 (p=9.1e-01) (expect negative: purity reduces future blockers) ====================================================================== E. FAIRNESS CHECK (does high constraint unfairly imply low awareness?) ====================================================================== High-constraint users, matched mean Awareness: 23.00 Low-constraint users, matched mean Awareness: 25.69 Difference (lo-hi) after matching on Purity+Belief: +2.70 Ground truth was built so inner awareness does NOT depend on physical constraint. If diff is small, SAE measurement layer is fair; if large, constraints leak into awareness. DONE. Structural invariant tests over 5000 trials blockers_monotone : PASS purity_monotone : PASS constraint_monotone : PASS stage_gate : PASS ranges : PASS OVERALL: PASS