Pith Number
pith:WJLGMHSS
pith:2024:WJLGMHSST2CNYPMMFJR7GL4BL5
not attested
not anchored
not stored
refs pending
Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
arxiv:2401.16788 v1 · 2024-01-30 · cs.CL · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{WJLGMHSST2CNYPMMFJR7GL4BL5}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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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.
Cited by
Receipt and verification
| First computed | 2026-07-05T07:39:00.121998Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b256661e529e84dc3d8c2a63f32f815f4bcc0b2373b4abf19ca5319f04798b58
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/WJLGMHSST2CNYPMMFJR7GL4BL5 \
| 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: b256661e529e84dc3d8c2a63f32f815f4bcc0b2373b4abf19ca5319f04798b58
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "94093d9e1653298d63cd6c50a9a0d03f97b3167754180f40314598bf7d61d346",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CL",
"submitted_at": "2024-01-30T07:03:32Z",
"title_canon_sha256": "9245dcb609f03a7b56c0bb6f2a6ab5216ed9323822dee923bac5d3817410b2e5"
},
"schema_version": "1.0",
"source": {
"id": "2401.16788",
"kind": "arxiv",
"version": 1
}
}