Pith Number
pith:Y2QAIWOO
pith:2023:Y2QAIWOODXAB6VJSIUWQ7DLTT5
not attested
not anchored
not stored
refs pending
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
arxiv:2401.01369 v1 · 2023-12-27 · cs.IR · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{Y2QAIWOODXAB6VJSIUWQ7DLTT5}
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 | 2026-07-05T07:29:42.042112Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c6a00459ce1dc01f5532452d0f8d739f5eebbcfc3c3128f3647168836b8d082b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y2QAIWOODXAB6VJSIUWQ7DLTT5 \
| 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: c6a00459ce1dc01f5532452d0f8d739f5eebbcfc3c3128f3647168836b8d082b
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "f18e857559991573ef959ca9b6ba9eccf6e68b6778b8fcf56929cdee661a0618",
"cross_cats_sorted": [
"cs.AI",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.IR",
"submitted_at": "2023-12-27T12:40:19Z",
"title_canon_sha256": "4bad95b21d3f023d0d473bef11876231493c849deb0228f8bb571e660a765ddb"
},
"schema_version": "1.0",
"source": {
"id": "2401.01369",
"kind": "arxiv",
"version": 1
}
}