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pith:GQ3K7COZ

pith:2026:GQ3K7COZB6FCUBKXM57QUKKDGQ
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GEASS: Gated Evidence-Adaptive Selective Caption Trust for Vision-Language Models

Jiashen Ding, Shuoyang Zhang, Zeshang Li

GEASS lets vision-language models decide per query how much of a self-generated caption to trust, cutting hallucinations.

arxiv:2605.01733 v2 · 2026-05-03 · cs.CV · cs.AI

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4 Citations open
5 Replications open
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Claims

C1strongest claim

Experiments on POPE and HallusionBench across four VLMs show that GEASS consistently improves over vanilla inference and contrastive decoding, with only two extra forward passes per query.

C2weakest assumption

That the combination of clean-path confidence and entropy reduction reliably identifies when and how much caption content is useful without discarding beneficial information or introducing new selection bias on a per-query basis.

C3one line summary

GEASS selectively gates and weights self-generated captions using confidence and entropy to reduce object hallucinations in VLMs, outperforming vanilla inference and contrastive decoding on POPE and HallusionBench.

Receipt and verification
First computed 2026-05-20T01:05:15.137695Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

3436af89d90f8a2a0557677f0a2943343fd1ef2d654eac0a4622ecb755f33a4a

Aliases

arxiv: 2605.01733 · arxiv_version: 2605.01733v2 · doi: 10.48550/arxiv.2605.01733 · pith_short_12: GQ3K7COZB6FC · pith_short_16: GQ3K7COZB6FCUBKX · pith_short_8: GQ3K7COZ
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GQ3K7COZB6FCUBKXM57QUKKDGQ \
  | 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: 3436af89d90f8a2a0557677f0a2943343fd1ef2d654eac0a4622ecb755f33a4a
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-03T06:09:04Z",
    "title_canon_sha256": "3ff95be860c28c40322b7bf26b65e24c59f0bd7facc4dec79c1051cd5999e4c9"
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