Mapping U.S. Federal AI Governance Against Sector Vulnerability
研究通过分析684份联邦AI治理文件，对比专家对各行业AI风险的评估，揭示了当前治理在覆盖面上存在的差距，特别是在金融和医疗等高风险领域。
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V-REX: Efficient Specialist VLM Training for Veterinary X-Rays
本文针对兽医X光片诊断问题，通过重新设计VLM训练流程，提出更高效的训练方法，不依赖大规模基础模型即实现优越性能。
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Self-Generated Text Recognition: Quality Heuristics, Cross-Task Transfer, and Downstream Bias in LLM Evaluation
研究探讨了自生成文本识别能力对AI安全的影响，通过不同实验设计选择评估多种模型，发现模型性能受评估格式、对话格式和任务领域影响，并提出需在关键AI应用中监控此能力。
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VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
Veterinary medical imaging suffers from severe scarcity of expert annotations. Method: This paper proposes a self-supervised learning framework leveraging multi-view X-ray images (e.g., ventrodorsal and lateral views) from the same clinical case, exploiting naturally occurring standardized anatomical correspondences to implicitly learn view-invariant representations and 3D spatial anatomy—without synthetic data augmentation. Contribution/Results: It pioneers the integration of clinical multi-view anatomical priors into medical self-supervised learning, establishing an anatomy-consistency-driven paradigm. The method synergistically combines the DINO architecture, multi-view knowledge distillation, cross-view feature alignment, and contrastive learning. Trained on 5 million canine radiographs, it achieves state-of-the-art performance across multiple downstream tasks, significantly enhancing anatomical understanding and generalization to synthetic or out-of-distribution data.
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Mapping U.S. Federal AI Governance Against Sector Vulnerability
研究通过分析684份联邦AI治理文件，对比专家对各行业AI风险的评估，揭示了当前治理在覆盖面上存在的差距，特别是在金融和医疗等高风险领域。
0 citationsRead paper
V-REX: Efficient Specialist VLM Training for Veterinary X-Rays
本文针对兽医X光片诊断问题，通过重新设计VLM训练流程，提出更高效的训练方法，不依赖大规模基础模型即实现优越性能。
0 citationsRead paper
Self-Generated Text Recognition: Quality Heuristics, Cross-Task Transfer, and Downstream Bias in LLM Evaluation
研究探讨了自生成文本识别能力对AI安全的影响，通过不同实验设计选择评估多种模型，发现模型性能受评估格式、对话格式和任务领域影响，并提出需在关键AI应用中监控此能力。
0 citationsRead paper
VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
Veterinary medical imaging suffers from severe scarcity of expert annotations. Method: This paper proposes a self-supervised learning framework leveraging multi-view X-ray images (e.g., ventrodorsal and lateral views) from the same clinical case, exploiting naturally occurring standardized anatomical correspondences to implicitly learn view-invariant representations and 3D spatial anatomy—without synthetic data augmentation. Contribution/Results: It pioneers the integration of clinical multi-view anatomical priors into medical self-supervised learning, establishing an anatomy-consistency-driven paradigm. The method synergistically combines the DINO architecture, multi-view knowledge distillation, cross-view feature alignment, and contrastive learning. Trained on 5 million canine radiographs, it achieves state-of-the-art performance across multiple downstream tasks, significantly enhancing anatomical understanding and generalization to synthetic or out-of-distribution data.
0 citationsRead paper