README.md

metadata

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

python code/run_experiment.py --self-test

Full exposed cohort:

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.