Pith Number
pith:GSMYRMQM
pith:2026:GSMYRMQME5COD27ZXVNIZWV4N7
not attested
not anchored
not stored
refs pending
Curvature-aware dynamic precision approach for physics-informed neural networks
arxiv:2606.04736 v1 · 2026-06-03 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{GSMYRMQME5COD27ZXVNIZWV4N7}
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.
Receipt and verification
| First computed | 2026-06-04T01:09:27.516114Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
349988b20c2744e1ebf9bd5a8cdabc6fd1a1501c3bb22fb931e0d92185cf9624
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GSMYRMQME5COD27ZXVNIZWV4N7 \
| 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: 349988b20c2744e1ebf9bd5a8cdabc6fd1a1501c3bb22fb931e0d92185cf9624
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "8a0834c18e722d9e1c93326f51cc5c9323a58c6a9d37c14d7073948de2c0db0d",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-06-03T11:19:53Z",
"title_canon_sha256": "0e5a58c4b9135ebdfad91c7644accc2fa6f2c7ef732ffe95234c0fa9d296405b"
},
"schema_version": "1.0",
"source": {
"id": "2606.04736",
"kind": "arxiv",
"version": 1
}
}