Reproducible macroscopic dynamics in a closed-loop human-AI learning system

arXiv:2608.30946 · cs.LG, nlin.AO · Submitted 2026-08-31 · Read on arXiv

cs.LG, nlin.AO

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/riiid/ednet

Terminology

• Alignment — 50× in this paper · explained in 1 episode
• Core — 37× in this paper · explained in 2 episodes
• Calibration — 14× in this paper · explained in 7 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Post-Selection — 6× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Parsimony — 3× in this paper · explained in 2 episodes
• Self-consistency — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Intrinsic Dimension — 2× in this paper · explained in 2 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Memory Decay — 1× in this paper · explained in 3 episodes
• Metastability — 1× in this paper · explained in 3 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Structural Control — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• There Will Be a Scientific Theory of Deep Learning
• Layer Normalization
• Gaussian Error Linear Units (GELUs)

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