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README.md
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license: apache-2.0
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---
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This one is going to attempt a new form of prediction based on velocity flow matching using spherical representation.
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The alignment for the early prototype is essentially going to be euler discreet ODE flow matching, and it will be more than enough for now.
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license: apache-2.0
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---
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```
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Standard: x_t → UNet(x_t, t, c) → v_pred
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↑ full 16384-dim path
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GLFM: x_t → encoder → project to S^15 → triangulate against anchors
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↓
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768-dim geometric address (continuous, not discrete)
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↓
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conditioned_generator(address, t, c) → v_pred
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```
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This one is going to attempt a new form of prediction based on velocity flow matching using spherical representation.
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The alignment for the early prototype is essentially going to be euler discreet ODE flow matching, and it will be more than enough for now.
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