# Development result

**Descriptive outcome:** `FIXED_TRANSFORM_SATURATES_STRUCTURAL_HEADROOM`

This was an exposed instrument-development study with **8 seeds × 128 episodes = 1,024 paired episodes**.

## Key observations

- Mean pre-destruction task-E test accuracy: **0.895834**
- Post-reset task-E accuracy: **0.500000 on every episode**
- Exact structural-mask recovery: **98.83%**
- Transformed-minus-neutral normalized accuracy-AUC: **+0.080524**
- Oracle-minus-neutral normalized accuracy-AUC: **+0.081000**
- Mean transformed recovery of oracle structural headroom: **99.42%**
- Mean oracle-minus-transformed residual: **+0.000475**

The experiment used a deterministic, information-losing transform from a learned task-specific predictor to a four-coordinate structural outline. The predictor was then destroyed. The retained outline did not act as a forward predictor; it changed the directional allocation of future gradient updates while preserving each update's L2 norm.

## Interpretation

Within this deliberately constructed instrument, preservation of the exact original predictive representation was **not necessary** for a useful future learning consequence.

## Critical limitation

The transformation rule was supplied by the experimenter. This study does **not** show that a learned system can decide what to retain, how to transform it, or when a transformed state should persist. The fixed transform left negligible residual oracle headroom, so a learned transform was not justified in this environment.