GeoFlow update — two training runs on the pentachoron geometric prior (4.8M params modulating frozen SD1.5).
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GeoFlow update — two training runs on the pentachoron geometric prior (4.8M params modulating frozen SD1.5).
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10k ImageNet run fixed fragmented anatomy and spatial coherence in 7 minutes. 
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10k ImageNet run fixed fragmented anatomy and spatial coherence in 7 minutes. 
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50k object-relations run taught actual compositional reasoning — "red cup on top of blue book" goes from a floating cup to correctly placed on the book.
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50k object-relations run taught actual compositional reasoning — "red cup on top of blue book" goes from a floating cup to correctly placed on the book.
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Most interesting finding: learning happens in two phases. Object association locks first (~500 steps), spatial arrangement crystallizes after. You can watch it happen — "three candles in a triangle on a wooden tray" starts as candles side by side, then reorganizes into proper triangular formation. The tray itself rendered as a pentagon. Five vertices in, five sides out. The simplex is thinking in its own geometry.
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Most interesting finding: learning happens in two phases. Object association locks first (~500 steps), spatial arrangement crystallizes after. You can watch it happen — "three candles in a triangle on a wooden tray" starts as candles side by side, then reorganizes into proper triangular formation. The tray itself rendered as a pentagon. Five vertices in, five sides out. The simplex is thinking in its own geometry.
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Loss sits around 0.4 the entire time yet composition steadily improves. The prior nudges conditioning, it doesn't overwrite it.
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Loss sits around 0.4 the entire time yet composition steadily improves. The prior nudges conditioning, it doesn't overwrite it.
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Weights: 
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Weights: 
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https://huggingface.co/AbstractPhil/sd15-geoflow-object-association
AbstractPhil/sd15-geoflow-object-association
model
resource
https://huggingface.co/AbstractPhil/sd15-geoflow-object-association
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Dataset: 
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Dataset: 
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https://huggingface.co/datasets/AbstractPhil/synthetic-object-relations
AbstractPhil/synthetic-object-relations
dataset
resource
https://huggingface.co/datasets/AbstractPhil/synthetic-object-relations
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Formulas: 
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Formulas: 
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https://huggingface.co/AbstractPhil/sd15-rectified-geometric-matching
AbstractPhil/sd15-rectified-geometric-matching
model
resource
https://huggingface.co/AbstractPhil/sd15-rectified-geometric-matching
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Next up — measuring the exact entropy decay inflection point across layers to enable branching the simplex into parallel paths with different anchor deviations. Geometric ensemble attention where the branches disagree on purpose.
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Next up — measuring the exact entropy decay inflection point across layers to enable branching the simplex into parallel paths with different anchor deviations. Geometric ensemble attention where the branches disagree on purpose.
GeoFlow update — two training runs on the pentachoron geometric prior (4.8M params modulating frozen SD1.5).

10k ImageNet run fixed fragmented anatomy and spatial coherence in 7 minutes. 

50k object-relations run taught actual compositional reasoning — "red cup on top of blue book" goes from a floating cup to correctly placed on the book.

Most interesting finding: learning happens in two phases. Object association locks first (~500 steps), spatial arrangement crystallizes after. You can watch it happen — "three candles in a triangle on a wooden tray" starts as candles side by side, then reorganizes into proper triangular formation. The tray itself rendered as a pentagon. Five vertices in, five sides out. The simplex is thinking in its own geometry.

Loss sits around 0.4 the entire time yet composition steadily improves. The prior nudges conditioning, it doesn't overwrite it.

Weights: 
https://huggingface.co/AbstractPhil/sd15-geoflow-object-association
Dataset: 
https://huggingface.co/datasets/AbstractPhil/synthetic-object-relations
Formulas: 
https://huggingface.co/AbstractPhil/sd15-rectified-geometric-matching

Next up — measuring the exact entropy decay inflection point across layers to enable branching the simplex into parallel paths with different anchor deviations. Geometric ensemble attention where the branches disagree on purpose.
🚀
John6666