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.