Pith Number
pith:FMBQ4P35
pith:2024:FMBQ4P357AEBLLRE57YPWFRNGY
not attested
not anchored
not stored
refs pending
Can LLMs Serve As Time Series Anomaly Detectors?
arxiv:2408.03475 v1 · 2024-08-06 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{FMBQ4P357AEBLLRE57YPWFRNGY}
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
· sign in to
claim
4
Citations
5
Replications
✓
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 | 2026-07-05T08:52:59.083131Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2b030e3f7df80815ae24eff0fb162d361374fd63393900e39345d8e7b3fea75b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FMBQ4P357AEBLLRE57YPWFRNGY \
| 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: 2b030e3f7df80815ae24eff0fb162d361374fd63393900e39345d8e7b3fea75b
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "efb914ed868f0f0d4b24d91d9ac1becb44d099ce653577e29ba310a6c248acd4",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-08-06T23:14:39Z",
"title_canon_sha256": "ee909698ce28366a0e7bac53fc96846c0cc8a5691d967b63ebf29cd5cb790560"
},
"schema_version": "1.0",
"source": {
"id": "2408.03475",
"kind": "arxiv",
"version": 1
}
}