Solving Physical AI Drift: Developer Proposes Drift-Free Physical Bytecode Architecture
MarcJSchmidt · x · 2026-07-30
A developer shared deep insights and recent practical breakthroughs regarding the underlying mechanisms of physical AI (e.g., world models, JEPA).
- Core flaw: The biggest blunder in current physical AI is predicting the next state on the time axis (Δt), which inherently leads to drift regardless of optimization.
- Solution: After months of full-time research, the author iterated on a new architecture resembling "physical bytecode."
- Impact: This architecture achieves endless, drift-free prediction. Although it is not yet fully trained end-to-end, it represents a significant breakthrough.
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