| --- |
| pretty_name: Cross-Form Learning Consequence |
| tags: |
| - machine-learning |
| - online-learning |
| - plasticity |
| - reproducibility |
| - synthetic-data |
| --- |
| |
| # Cross-Form Learning Consequence |
|
|
| A small controlled experiment asking whether a useful consequence of learning can survive after the **task-specific predictive representation that produced it is destroyed**. |
|
|
| The task family is deliberately transparent: 16-dimensional synthetic binary classification with four active coordinates and manual NumPy logistic SGD. |
|
|
| ## Experiment |
|
|
| 1. Learn a task-specific predictor `R`. |
| 2. Derive a lower-bandwidth four-coordinate structural outline `S` from `|R|`. |
| 3. Destroy `R` and reset the future predictor to zero. |
| 4. Use `S` only to bias the **directional allocation** of future gradient updates. |
| 5. Compare against neutral, shuffled-history, wrong-family, random-outline, oracle, no-history, and direct-preservation controls. |
|
|
| Structured updates are rescaled every step so their L2 norm matches the ordinary gradient norm. The intervention therefore changes allocation/direction rather than step magnitude. |
|
|
| ## Result |
|
|
| Across **1,024 paired episodes**: |
|
|
| - the destroyed predictor returned to **0.500000** accuracy on every episode; |
| - the structural outline recovered the hidden support exactly on **98.83%** of episodes; |
| - transformed-minus-neutral normalized accuracy-AUC was **+0.080524**; |
| - oracle-minus-neutral was **+0.081000**; |
| - the fixed transform recovered **99.42%** of oracle structural headroom across seed means. |
|
|
| This is an **instrument-level existence result**, not a learned memory or architecture result. The transformation was fixed by the experimenter. |
|
|
| ## Run |
|
|
| ```bash |
| python code/run_experiment.py --self-test |
| ``` |
|
|
| Full exposed cohort: |
|
|
| ```bash |
| python code/run_experiment.py --output-root ./runs |
| ``` |
|
|
| ## Repository contents |
|
|
| - `code/run_experiment.py` — public-release copy of the frozen NumPy implementation |
| - `protocol/FROZEN_PROTOCOL_PUBLIC_RELEASE.md` — complete protocol with public naming |
| - `results/aggregate_summary.json` |
| - `results/per_seed_summary.csv` |
| - `results/episode_summary.csv` |
| - `results/assertion_log.txt` |
| - `results/development_result.md` |
| - `notebooks/RUN_IN_COLAB.ipynb` |
| - `docs/SOURCE_CUSTODY.md` |
|
|
| ## Scope |
|
|
| The result applies to this deliberately constructed synthetic instrument. It does not establish general continual-learning performance, learned persistence-form selection, architecture independence, or superiority to standard transfer/meta-learning methods. |
|
|
