Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:06:28.441029Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2507.09209.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:06:28.441029Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
58 of 58 outbound references displayed
External citation measurements
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Pubmedclip: How much does clip benefit visual question answering in the medical domain? In Findings of the As- sociation for Computational Linguistics, 2023
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models LoRA: Low-Rank Adaptation of Large Language Models
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Self-supervised vision-language pretraining for Medical visual question answering
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Evaluating object hallucination in large vision-language models
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Divide and conquer: Isolating normal-abnormal attributes in knowledge graph-enhanced radiology report generation
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Slake: A semantically-labeled knowledge- enhanced dataset for medical visual question answering
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Biogpt: Generative pre- trained transformer for biomedical text generation and min- ing
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Friedrich
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Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
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