QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
QALPA通过结合E(3)等变扩散模型、主动学习和量子力学方法，有效探索柔性分子化学空间中的稀疏区域，提高分子采样和模型可靠性。
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Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
This work addresses the high cost of annotating real-world data and the lack of quantitative guidance for mitigating domain gaps between synthetic and real images in scientific vision tasks. To this end, the authors propose a programmable 3D rendering framework that systematically enhances the realism, diversity, and scale of synthetic data by incorporating quantitative metrics—such as gradient similarity and zero-shot detection performance—and encapsulates the rendering pipeline as an agent skill for automated parameter optimization. This approach represents the first integration of quantitatively guided synthetic data refinement into an agent-based framework, substantially improving model visual perception: it boosts zero-shot object detection performance and further refines small-object detection when trained on mixed real-synthetic datasets. The implementation leverages the authors’ custom Python toolkit, GraNatPy, which includes the SynthClaw agent.
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QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
QALPA通过结合E(3)等变扩散模型、主动学习和量子力学方法，有效探索柔性分子化学空间中的稀疏区域，提高分子采样和模型可靠性。
0 citationsRead paper
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
This work addresses the high cost of annotating real-world data and the lack of quantitative guidance for mitigating domain gaps between synthetic and real images in scientific vision tasks. To this end, the authors propose a programmable 3D rendering framework that systematically enhances the realism, diversity, and scale of synthetic data by incorporating quantitative metrics—such as gradient similarity and zero-shot detection performance—and encapsulates the rendering pipeline as an agent skill for automated parameter optimization. This approach represents the first integration of quantitatively guided synthetic data refinement into an agent-based framework, substantially improving model visual perception: it boosts zero-shot object detection performance and further refines small-object detection when trained on mixed real-synthetic datasets. The implementation leverages the authors’ custom Python toolkit, GraNatPy, which includes the SynthClaw agent.
0 citationsRead paper