pith:BDTNG3CD
Learning to Theorize the World from Observation
A neural model learns to induce executable explanatory programs from raw observations alone.
arxiv:2605.03413 v2 · 2026-05-05 · cs.LG · cs.AI
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\usepackage{pith}
\pithnumber{BDTNG3CDRJOILHOFHO52SEY6TD}
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Record completeness
Claims
Experiments show that this formulation enables explanation-driven generalization, allowing observations to be understood in terms of the programs that generate them.
That raw non-textual observations alone suffice for a neural model to induce meaningful, compositional, and executable explanatory programs without additional structure or supervision.
NEO induces compositional latent programs as world theories from observations and executes them to enable explanation-driven generalization.
Receipt and verification
| First computed | 2026-06-05T01:15:25.087041Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
08e6d36c438a5c859dc53bbba9131e98fe4cf129d13c766b993c138172088bba
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BDTNG3CDRJOILHOFHO52SEY6TD \
| 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: 08e6d36c438a5c859dc53bbba9131e98fe4cf129d13c766b993c138172088bba
Canonical record JSON
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"cross_cats_sorted": [
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],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-05-05T06:39:12Z",
"title_canon_sha256": "132131a4d994f0875a9875cd6a37f4d52fb88e5991aa57f31e637fd239682738"
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"source": {
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"kind": "arxiv",
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