Pith Number
pith:FXRZYHXA
pith:2025:FXRZYHXA4AN5CZ5WVO7WP7B73F
not attested
not anchored
not stored
refs pending
What is Adversarial Training for Diffusion Models?
arxiv:2505.21742 v1 · 2025-05-27 · cs.CV · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{FXRZYHXA4AN5CZ5WVO7WP7B73F}
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 | 2026-07-05T11:11:02.791691Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F \
| 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: 2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "d5d5ddd7c252e844a91ab9cc18d09e2ff8182a75a5f48e65a5756521264b4198",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2025-05-27T20:32:28Z",
"title_canon_sha256": "ce9b3694f74ee3a0dc0544b2afdae9a12751614e64186f7cd7ade673bffde4e0"
},
"schema_version": "1.0",
"source": {
"id": "2505.21742",
"kind": "arxiv",
"version": 1
}
}