Pith Number
pith:KB3IVXKM
pith:2025:KB3IVXKMB2HU27UX7H2B5SLQXK
not attested
not anchored
not stored
refs pending
Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
arxiv:2508.00884 v1 · 2025-07-25 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{KB3IVXKMB2HU27UX7H2B5SLQXK}
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 | 2026-07-05T11:47:29.214013Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
50768add4c0e8f4d7e97f9f41ec970ba9363b4a13bf82f9d912b0b4de1e503de
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/KB3IVXKMB2HU27UX7H2B5SLQXK \
| 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: 50768add4c0e8f4d7e97f9f41ec970ba9363b4a13bf82f9d912b0b4de1e503de
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1a0ac65d592dec0d545e8ee7a261419b35c1d3ff9cfff12b5718482cb2a8e8e4",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2025-07-25T08:08:22Z",
"title_canon_sha256": "abae8cd6dc1f52f15cad9fb680419b664f342a5275f9c7e299ed58e4d98ce1c7"
},
"schema_version": "1.0",
"source": {
"id": "2508.00884",
"kind": "arxiv",
"version": 1
}
}