Pith Number
pith:GJNUVZFH
pith:2025:GJNUVZFHRHTD36NJYR2CBK2SQA
not attested
not anchored
not stored
refs pending
MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations
arxiv:2508.00760 v1 · 2025-08-01 · cs.CL · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{GJNUVZFHRHTD36NJYR2CBK2SQA}
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
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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 | 2026-07-05T11:47:02.816173Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
325b4ae4a789e63df9a9c47420ab5280186222cbe1b62fff299d16daa14a6493
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GJNUVZFHRHTD36NJYR2CBK2SQA \
| 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: 325b4ae4a789e63df9a9c47420ab5280186222cbe1b62fff299d16daa14a6493
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "0028456fc41a34a617f2e83d7549f6783d58c7e816dfd2ee53e5337e2413256e",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CL",
"submitted_at": "2025-08-01T16:34:57Z",
"title_canon_sha256": "5f6c20ae0d3d3a4592715807f46e28ad06bc78f3850c5793622924ffb37723c2"
},
"schema_version": "1.0",
"source": {
"id": "2508.00760",
"kind": "arxiv",
"version": 1
}
}