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pith:WKM6CWEI

pith:2026:WKM6CWEIQE4HZERZV6R5QLVXNW

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Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models

Fei Luo, Jungong Han, Xinyu Liu, Yanbiao Ma, Yike Guo, Zhe Qian, Zhonghua Wang, Zhongxing Xu, Zhuohan Ouyang, Zongyuan Ge

Multimodal reasoning models hallucinate when they stop querying visual evidence at high-entropy decision points.

arxiv:2604.10219 v2 · 2026-04-11 · cs.AI

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Claims

C1strongest claim

We identify a concerning phenomenon termed the Reasoning Vision Truth Disconnect (RVTD): hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states. We attribute this vulnerability to a breakdown in visual semantic anchoring, localized within the network's intermediate layers; specifically, during these high uncertainty transitions, the model fails to query visual evidence, reverting instead to language priors.

C2weakest assumption

The assumption that dynamically incentivizing visual attention across critical intermediate layers upon detecting high entropy states will translate external debiasing interventions into an intrinsic capability for hallucination mitigation, and that this can be achieved via the proposed HVAR within GRPO and FRM without degrading overall reasoning performance.

C3one line summary

Multimodal reasoning models hallucinate at high-entropy cognitive bifurcation points due to loss of visual semantic anchoring, and the V-STAR training paradigm with HVAR rewards and FRM reflection mitigates this by reinforcing visual attention.

Cited by

1 paper in Pith

LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning

Receipt and verification

First computed	2026-05-29T02:05:44.616875Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b299e1588881387c9239afa3d82eb76db2390788070d8e5af88f274c354c0eb5

Aliases

arxiv: 2604.10219 · arxiv_version: 2604.10219v2 · doi: 10.48550/arxiv.2604.10219 · pith_short_12: WKM6CWEIQE4H · pith_short_16: WKM6CWEIQE4HZERZ · pith_short_8: WKM6CWEI

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/WKM6CWEIQE4HZERZV6R5QLVXNW \
  | 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: b299e1588881387c9239afa3d82eb76db2390788070d8e5af88f274c354c0eb5

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-04-11T13:59:05Z",
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