AbstractPhil/grid-geometric-multishape
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AbstractPhil
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updated
a model
about 7 hours ago
AbstractPhil/grid-geometric-multishape
published
a model
about 7 hours ago
AbstractPhil/grid-geometric-multishape
posted
an
update
about 16 hours ago
The Rosetta Stone geometric vocabulary and the ramping up capacity.
What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
Organizations
updated
a
model
about 7 hours ago
published
a
model
about 7 hours ago
posted
an
update
about 16 hours ago
Post
150
The Rosetta Stone geometric vocabulary and the ramping up capacity.
What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
published
an
article
about 17 hours ago
Article
Reading the Geometry of Learned Representations: How Synthetic Primitives Became a Rosetta Stone for VAE Latent Spaces
updated
a
model
about 17 hours ago
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a
model
3 days ago
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a
model
6 days ago
published
a
model
7 days ago
liked
a
model
10 days ago
updated
a
model
12 days ago
published
a
model
12 days ago
updated
a
model
12 days ago
published
a
model
12 days ago
updated
a
model
12 days ago
published
a
model
12 days ago
updated
a
model
12 days ago
published
a
model
12 days ago
updated
a
model
12 days ago
posted
an
update
12 days ago
Post
264
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:
AbstractPhil/sd15-geoflow-object-association
Dataset:
AbstractPhil/synthetic-object-relations
Formulas:
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.
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:
AbstractPhil/sd15-geoflow-object-association
Dataset:
AbstractPhil/synthetic-object-relations
Formulas:
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.
updated
a
model
13 days ago