Steven De Keninck retweetledi
Solid mathematical ideas almost always outperform contrived engineering tricks.
For years deep learning has been dominated by increasingly complex architectural hacks: CNN blocks, attention layers, channel mixers, residual pathways, normalization stacks.
Every few years a new architecture is announced as if it were a revolution.
One of the most famous examples was Kaiming He and Residual Networks (ResNet). At the time he was paraded around the AI world like a celebrity because residual connections supposedly “solved” deep learning.
But these were largely engineering patches.
Now something much more interesting appeared.
A new architecture called CliffordNet returns to mathematics — specifically Clifford Algebra, developed in the 19th century by William Kingdon Clifford.
Instead of stacking arbitrary modules, the model is built around the geometric product
uv = u·v + u∧v
A single algebraic operation that simultaneously captures inner product structure and geometric interactions.
In other words: the math already contains the interaction mechanism.
No attention blocks.
No mixer layers.
No architectural spaghetti.
The result:
• 77.82% accuracy on CIFAR-100 with only 1.4M parameters
• roughly 8× fewer parameters than ResNet-18
And with strict O(N) complexity.
The paper even suggests that once geometric interactions are modeled correctly, feed-forward networks become largely redundant.
A good reminder for the AI community.
Engineering tricks can dominate for years.
But eventually mathematics shows up and deletes half the architecture.
Paper:
[arxiv.org/pdf/2601.06793…)
19th century geometry just walked into computer vision.
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