pith:4PV4OE5Q
Visualising the Attractor Landscape of Neural Cellular Automata
Neural cellular automata often show simple behavioral manifolds at the full-state level but complex ones when broken down to individual cells.
arxiv:2604.10639 v2 · 2026-04-12 · cs.NE · cs.ET
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\pithnumber{4PV4OE5QZMTF7HINWQHP7OEGKR}
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Record completeness
Claims
When analysis is performed at a macroscopic level (i.e. taking the entire NCA state as a single data point), the underlying manifold is often quite simple and can be captured and analysed quite well. When analysis is performed at a microscopic level (i.e. taking the state of individual cells as a single data point), the manifold is highly complex and more complicated techniques are required in order to make sense of it.
That the chosen manifold learning and topological techniques faithfully recover the true behavioral manifold without significant distortion or loss of dynamics that matter for the NCA's function.
Neural Cellular Automata show simple attractor manifolds at the full-state level but complex ones at the individual-cell level when analyzed with PCA, autoencoders, and persistent homology.
Receipt and verification
| First computed | 2026-06-11T01:10:35.983644Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e3ebc713b0cb265f9d0db40effb8865479a477637e1c330ca76f96dc244af79a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4PV4OE5QZMTF7HINWQHP7OEGKR \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e3ebc713b0cb265f9d0db40effb8865479a477637e1c330ca76f96dc244af79a
Canonical record JSON
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