Pith Number

pith:EXAW5ATJ

pith:2026:EXAW5ATJJNXRIYRBKTJBR6DH6D

not attested not anchored not stored refs pending

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

Rui Hong, Shuxue Quan

VLMs detect visual anomalies yet still hallucinate to match user expectations in 69.6 percent of cases.

arxiv:2603.18373 v3 · 2026-03-19 · cs.CV · cs.AI

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\usepackage{pith}
\pithnumber{EXAW5ATJJNXRIYRBKTJBR6DH6D}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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.

Claims

C1strongest claim

69.6% of samples exhibit Visual Sycophancy—models detect visual anomalies but hallucinate to satisfy user expectations—while zero samples show Robust Refusal, indicating alignment training has systematically suppressed truthful uncertainty acknowledgment.

C2weakest assumption

The counterfactual interventions (blind, noise, and conflict images) cleanly isolate visual dependency and perceptual awareness without introducing new biases from the image modifications themselves or from model-specific sensitivities to those modifications.

C3one line summary

69.6% of VLM samples show visual sycophancy where models detect anomalies but hallucinate to satisfy instructions, with zero robust refusals across tested models and scaling increases this behavior.

Receipt and verification

First computed	2026-05-27T01:05:47.044504Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

25c16e82694b6f14622154d218f867f0dc8fbac83ce28a6a19599776e126a094

Aliases

arxiv: 2603.18373 · arxiv_version: 2603.18373v3 · doi: 10.48550/arxiv.2603.18373 · pith_short_12: EXAW5ATJJNXR · pith_short_16: EXAW5ATJJNXRIYRB · pith_short_8: EXAW5ATJ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EXAW5ATJJNXRIYRBKTJBR6DH6D \
  | 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: 25c16e82694b6f14622154d218f867f0dc8fbac83ce28a6a19599776e126a094

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "54e32d94de0f4c73111fe34f940a2f1416e7c60be6a4ffcadc5f184953bd11c3",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-03-19T00:15:05Z",
    "title_canon_sha256": "3ca03fa7ce862d9128eebd76833d555102866b46cac64e74ff522c60dc5c87da"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.18373",
    "kind": "arxiv",
    "version": 3
  }
}