Pith Number
pith:BNBDDWFJ
pith:2019:BNBDDWFJV2VFPAA7WXP7FBKEHP
not attested
not anchored
not stored
refs pending
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
arxiv:1902.09835 v1 · 2019-02-26 · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BNBDDWFJV2VFPAA7WXP7FBKEHP}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-05-17T23:52:35.227960Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0b4231d8a9aeaa57801fb5dff285443bf72e799cd1d6895f8ebfc7c7121774f8
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BNBDDWFJV2VFPAA7WXP7FBKEHP \
| 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: 0b4231d8a9aeaa57801fb5dff285443bf72e799cd1d6895f8ebfc7c7121774f8
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "9f5e33c56a52724d2f7d5783f610361a5ab57e67d4c17cf25ed99148b1b17310",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.AI",
"submitted_at": "2019-02-26T10:04:19Z",
"title_canon_sha256": "11e56c27b6597bdc79c3bce0512d052ee6b727faf5e07229bef351617f57cb3f"
},
"schema_version": "1.0",
"source": {
"id": "1902.09835",
"kind": "arxiv",
"version": 1
}
}