Pith Number
pith:BOFFCX3N
pith:2016:BOFFCX3NJ2FJKCPEOE2KTOW7KQ
not attested
not anchored
not stored
refs pending
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
arxiv:1607.00215 v3 · 2016-07-01 · stat.ML · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BOFFCX3NJ2FJKCPEOE2KTOW7KQ}
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.
Cited by
Receipt and verification
| First computed | 2026-05-18T00:42:32.742040Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0b8a515f6d4e8a9509e47134a9badf54162df1d716d0635d439266a90daf4a96
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BOFFCX3NJ2FJKCPEOE2KTOW7KQ \
| 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: 0b8a515f6d4e8a9509e47134a9badf54162df1d716d0635d439266a90daf4a96
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "af5fb3fd692ad05c50076d745f646abcece506c4dcfd4aa90ac2e2e410ade0fc",
"cross_cats_sorted": [
"cs.AI",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.ML",
"submitted_at": "2016-07-01T11:58:28Z",
"title_canon_sha256": "72a8e3d460cca5919721760020a62f3f01e1119ce938359a9ebbe70080411613"
},
"schema_version": "1.0",
"source": {
"id": "1607.00215",
"kind": "arxiv",
"version": 3
}
}