Pith Number
pith:4U5KDJHH
pith:2025:4U5KDJHHIJIV7LTCADVFPSEHEK
not attested
not anchored
not stored
refs pending
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
arxiv:2509.05086 v1 · 2025-09-05 · cs.CV · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{4U5KDJHHIJIV7LTCADVFPSEHEK}
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 | 2026-07-05T12:05:35.949822Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e53aa1a4e742515fae6200ea57c887228a755c37b4b6da1e1ae7ddf8bef23247
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4U5KDJHHIJIV7LTCADVFPSEHEK \
| 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: e53aa1a4e742515fae6200ea57c887228a755c37b4b6da1e1ae7ddf8bef23247
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "8c27a53d3ddcd2ae2d05f8e1b87d2fb6988a9bf14c6a596ab85972cd6a2df96e",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2025-09-05T13:25:33Z",
"title_canon_sha256": "3282031f7c8685ee1ae7c416ab9d090f1635f4f79ecf9e892a11e9653461d65a"
},
"schema_version": "1.0",
"source": {
"id": "2509.05086",
"kind": "arxiv",
"version": 1
}
}