pith:M6T2WLTQ
EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors
EnsemHalDet improves hallucination detection in vision-language models by ensembling detectors from multiple internal states.
arxiv:2604.02784 v2 · 2026-04-03 · cs.CV · cs.CL
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\pithnumber{M6T2WLTQJO6K4LA6SP3IBXMGUC}
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Record completeness
Claims
EnsemHalDet consistently outperforms prior methods and single-detector models in terms of AUC across multiple VQA datasets and VLMs.
That the chosen internal representations (attention outputs and hidden states) supply sufficiently diverse and complementary hallucination signals that can be combined effectively by ensemble learning.
EnsemHalDet improves hallucination detection in VLMs by ensembling independent detectors on diverse internal states, yielding higher AUC than single-detector baselines on VQA datasets.
Receipt and verification
| First computed | 2026-05-20T01:05:12.453408Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
67a7ab2e704bbcae2c1e93f680dd86a09558056bd61b5ddb2dc381396fca99b3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/M6T2WLTQJO6K4LA6SP3IBXMGUC \
| 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: 67a7ab2e704bbcae2c1e93f680dd86a09558056bd61b5ddb2dc381396fca99b3
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by-sa/4.0/",
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"submitted_at": "2026-04-03T06:48:27Z",
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