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ARXIV:2604.26419 · VISION-LANGUAGE MODELS · SUBMITTED 30 APR · 15:13 UTC · FRESHNESS STALE
ARXIV:2604.26419VISION-LANGUAGE MODELSSUBMITTED 30 APR · 15:13 UTCFRESHNESS STALEJunru Song · Yimeng Hu · Yijing Chen · Huining Li · Qian Li · Lizhen Cui · +1 at arXiv
Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains.
Opportunity summary
Pain Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge.
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results on the Visual-Idk dataset show our method improves the Truthful Rate from 57.9\% to 67.3\%. Code availability is flagged in the production record;…
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains.
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10.48550/arXiv.2604.26419Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains.
Abstract
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge. In this paper, we propose a systematic framework to enhance the refusal capability of VLMs when facing such unknown questions. We first curate a model-specific "Visual-Idk" (Visual-I don't know) dataset, leveraging multi-sample consistency probing to distinguish between known and unknown facts. We then align the model using supervised fine-tuning followed by preference-aware optimization (e.g., DPO, ORPO) to effectively delineate its knowledge boundaries. Results on the Visual-Idk dataset show our method improves the Truthful Rate from 57.9\% to 67.3\%. Additionally, internal probing also demonstrates that the model genuinely recognizes its boundaries instead of just memorizing refusal patterns. Our framework further generalizes to out-of-distribution medical and perceptual domains, providing a robust path toward more trustworthy and prudent visual assistants.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge.
METHOD
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowl...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results on the Visual-Idk dataset show our method improves the Truthful Rate from 57.9\% to 67.3\%. Code availability is flagged in the production record; the public repository link still needs proof alig...
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 16, "author": "Junru Song; Yimeng Hu; Yijing Chen; Huining Li; Qian Li; Lizhen Cui; Yuntao Du"
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Enhance large vision-language models to refuse queries beyond their knowledge, improving trustworthiness for specialized domains.
Segment
Vision-Language Models
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
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stale
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passport absent
stale
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Technical feasibility
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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