Pith Number
pith:G64YHH3A
pith:2026:G64YHH3ASXLMNY2VD3P5WJPHZH
not attested
not anchored
not stored
refs pending
POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
arxiv:2605.18128 v1 · 2026-05-18 · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{G64YHH3ASXLMNY2VD3P5WJPHZH}
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 | 2026-05-20T00:05:17.402425Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
37b9839f6095d6c6e3551edfdb25e7c9edb472c0e6816960450e8df6d2a17f18
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/G64YHH3ASXLMNY2VD3P5WJPHZH \
| 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: 37b9839f6095d6c6e3551edfdb25e7c9edb472c0e6816960450e8df6d2a17f18
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "bf8d6ae6b29c5b7fdda392c16c09332ed4afb243370e03950b55449de48e1a32",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.AI",
"submitted_at": "2026-05-18T09:34:14Z",
"title_canon_sha256": "0f2b5d2cfd68eaf9fa437859831311ef3ae53db81f9799e4e2d142295a789401"
},
"schema_version": "1.0",
"source": {
"id": "2605.18128",
"kind": "arxiv",
"version": 1
}
}