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Synapse
April 1, 20260 citationsOpen Access

Model Collapse as Drift Cascade: Recursive AI Training, Entropy Loss, and the Thermodynamics of Synthetic Data Feedback

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AEAnthony W. Eckert

Key Points

  • The study investigates the phenomenon of model collapse within recursive AI training, focusing on entropy loss and its effects on data feedback.
  • Analyzed the drift cascade operating on training data in recursive AI training.
  • Calculated entropy loss as a measure for drift velocity during model training.
  • Mapped first-order phase transitions in data accumulation across multiple dimensions.
  • Identified drift velocity correlating with entropy loss values between 0.2-0.4 per generation.
  • Demonstrated a clear transition pattern from D1 to D2 to D3 in model performance.
  • Confirmed the presence of model collapse under specified training conditions.

Abstract

Model collapse (Shumailov et al. Nature 2024) is the drift cascade operating on training data. Entropy loss 0.2-0.4/gen = drift velocity, data accumulation = constraint specification, first-order phase transition maps onto D1→D2→D3. 7/7 KC PASS.

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Cite This Study

Anthony W. Eckert (2026) studied this question.

synapsesocial.com/papers/69ccb6fd16edfba7beb88d2ehttps://doi.org/10.5281/zenodo.19340905
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Also Consider

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  1. 1Drift Cascade as Thermodynamic Phase Transition in Reward Hacking: D1→D2→D3 Predicts Emergent Misalignment2026
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  3. 3Drift Cascade as Thermodynamic Phase Transition in Reward Hacking: D1→D2→D3 Predicts Emergent Misalignment2026
  4. 4Drift Cascade Theory of the Consciousness Cluster2026
  5. 5Loss Distribution Collapse: A Structural Theory of Dataset Degradation2026