pith:4YDNK5Z5
Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence
SurpMark detects LLM-generated text by comparing the transition patterns of discretized token surprisals to fixed human and machine reference matrices using generalized Jensen-Shannon divergence.
arxiv:2510.07500 v3 · 2025-10-08 · cs.LG · cs.IT · math.IT
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Record completeness
Claims
Empirically, across multiple datasets, source models, and scenarios, SurpMark consistently matches or surpasses baselines, demonstrating strong robustness across domains and generators.
That fixed reference transition matrices built once from existing human and machine corpora remain discriminative even when the test text comes from unseen domains, generators, or when the proxy LM differs substantially from the unknown source model.
SurpMark detects machine-generated text by estimating state-transition matrices from discretized surprisals and scoring them with generalized Jensen-Shannon divergence to human versus machine references.
Formal links
Receipt and verification
| First computed | 2026-06-23T03:13:48.677481Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e606d5773d9ca897b174a6be72d1cfebd75c90018a827838b69103e966895d72
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4YDNK5Z5TSUJPMLUU27HFUOP5P \
| 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: e606d5773d9ca897b174a6be72d1cfebd75c90018a827838b69103e966895d72
Canonical record JSON
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