Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
研究通过蒸馏学习和硬件协同设计，将工业基础模型应用于粒子物理探测器边缘，提高数据采集系统性能。
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Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions
本文通过引入文档嵌入几何和反事实消融框架，提供了一种量化检测科学革命的方法，用以测量单个概念对科学知识组织的影响。
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Latent unified smooth Hamiltonians for excited state chemistry
本文提出一种神经网络架构，通过学习电子态哈密顿量的隐式基表示来统一处理分子系统的基态和激发态问题。
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Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics
本文通过点云自蒸馏框架解决了粒子和核物理中基础模型跨传感器复用的问题，提高了模型在不同探测器上的性能。
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Unknown Unknowns: Model Misspecification in Machine Learning for Physics
This study addresses the risks of unknown unknowns arising from model misspecification in physical inverse problems by proposing an iterative diagnosis and mitigation framework. Treating misspecification as an opportunity for discovery, this work establishes a closed-loop detection-mitigation analytical paradigm that integrates complementary diagnostics, iterative updating, and robustness analysis strategies. Consequently, this research develops a systematic methodology for managing unknown unknowns, effectively enhancing model robustness against unforeseen biases and significantly improving the reliability of physical measurements. Ultimately, the proposed framework provides a novel safety assurance mechanism for solving complex inverse problems, ensuring greater confidence in computational reconstructions where model fidelity cannot be fully guaranteed a priori.
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Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
研究通过蒸馏学习和硬件协同设计，将工业基础模型应用于粒子物理探测器边缘，提高数据采集系统性能。
0 citationsRead paper
Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions
本文通过引入文档嵌入几何和反事实消融框架，提供了一种量化检测科学革命的方法，用以测量单个概念对科学知识组织的影响。
0 citationsRead paper
Latent unified smooth Hamiltonians for excited state chemistry
本文提出一种神经网络架构，通过学习电子态哈密顿量的隐式基表示来统一处理分子系统的基态和激发态问题。
0 citationsRead paper
Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics
本文通过点云自蒸馏框架解决了粒子和核物理中基础模型跨传感器复用的问题，提高了模型在不同探测器上的性能。
0 citationsRead paper
Unknown Unknowns: Model Misspecification in Machine Learning for Physics
This study addresses the risks of unknown unknowns arising from model misspecification in physical inverse problems by proposing an iterative diagnosis and mitigation framework. Treating misspecification as an opportunity for discovery, this work establishes a closed-loop detection-mitigation analytical paradigm that integrates complementary diagnostics, iterative updating, and robustness analysis strategies. Consequently, this research develops a systematic methodology for managing unknown unknowns, effectively enhancing model robustness against unforeseen biases and significantly improving the reliability of physical measurements. Ultimately, the proposed framework provides a novel safety assurance mechanism for solving complex inverse problems, ensuring greater confidence in computational reconstructions where model fidelity cannot be fully guaranteed a priori.
0 citationsRead paper