AGI Meta-Learning Report

Executive Summary

This report demonstrates verifiable meta-learning capabilities through knowledge transfer between neural network specialists trained on different dimensional problems. The experiment tests whether knowledge from one specialist can improve performance in another, demonstrating a key AGI capability.

Specialist Performance

Baseline accuracy of all 10 trained specialists across different dimensional problems:

Knowledge Transfer Results

Detailed results of all 45 transfer attempts between specialists:

Transfer Performance Analysis

Breakdown of transfer outcomes by category:

AGI Concepts Demonstrated

Verification & Reproducibility

This demonstration is fully verifiable and reproducible:

• Fixed Seed: 657454018 ensures identical random initialization
• Consistent Baseline: All specialists trained with same methodology
• Evidence Saved: Complete results saved to 'meta_learning_evidence_zenodo_version.json'
• Quantitative Metrics: All improvements measured against established baselines