Pith Number
pith:LDXFJZPY
pith:2025:LDXFJZPY2X6E3T23UBNPX44TRM
not attested
not anchored
not stored
refs pending
APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
arxiv:2508.10732 v1 · 2025-08-14 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{LDXFJZPY2X6E3T23UBNPX44TRM}
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 | 2026-07-05T11:54:01.569217Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
58ee54e5f8d5fc4dcf5ba05afbf3938b03982acd7defc8cf0b7a60708df2ae27
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LDXFJZPY2X6E3T23UBNPX44TRM \
| 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: 58ee54e5f8d5fc4dcf5ba05afbf3938b03982acd7defc8cf0b7a60708df2ae27
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "a61df787ff51d7380870c9e93f10e1b1a6de6b661d396eeef12211e29d7d1014",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2025-08-14T15:12:50Z",
"title_canon_sha256": "7a896bd82d09429cda8b7869a1d133c7dea45a38215a56c65a0e32a3f6da8bd9"
},
"schema_version": "1.0",
"source": {
"id": "2508.10732",
"kind": "arxiv",
"version": 1
}
}