DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis
研究通过引入DeFiFlowBench和提出Koan-Safe方法，旨在解决自然语言合成的DeFi工作流中的安全性问题，提高安全执行能力。
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本文提出OmniMed-FL框架，通过多模态联邦学习方法解决医学图像和患者记录联合分析问题，同时满足数据隐私要求。
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Physiological Information Reliability: Cross-Layer Adaptive Resource Allocation for Cardiovascular Sensing
本文提出一种跨层框架PIR，通过结合生理信息价值与无线、能量及计算状态，使用上下文强盗算法自适应调整传感和通信决策，以解决心血管监测中信号降质等问题。
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Adaptive Peer Clustering with Hierarchical Random Linear Network Coding for Resilient Decentralized Wireless Networks
本文提出APC-RLNC系统，通过动态分组和分级网络编码解决去中心化无线网络中因节点间信道条件差异导致的通信鲁棒性问题。
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Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
This study addresses the challenge of feedback control under noisy continuous measurements where quantum states are not directly accessible. We propose a Kraus-parameterized belief reinforcement learning method that integrates quantum state geometry into the learning loop. By imposing Stiefel manifold constraints on the encoder, the approach generates physically valid and interpretable density matrix estimates, while employing the PPO algorithm to achieve continuous control mapping. Experimental results demonstrate that this method attains a belief fidelity of 0.77–0.80 with significantly lower return variance compared to LSTM baselines. Consequently, it enables more stable quantum feedback control under non-ideal conditions, effectively resolving the lack of physical constraints in belief representations inherent to traditional approaches.
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DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis
研究通过引入DeFiFlowBench和提出Koan-Safe方法，旨在解决自然语言合成的DeFi工作流中的安全性问题，提高安全执行能力。
0 citationsRead paper
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
本文提出OmniMed-FL框架，通过多模态联邦学习方法解决医学图像和患者记录联合分析问题，同时满足数据隐私要求。
0 citationsRead paper
Physiological Information Reliability: Cross-Layer Adaptive Resource Allocation for Cardiovascular Sensing
本文提出一种跨层框架PIR，通过结合生理信息价值与无线、能量及计算状态，使用上下文强盗算法自适应调整传感和通信决策，以解决心血管监测中信号降质等问题。
0 citationsRead paper
Adaptive Peer Clustering with Hierarchical Random Linear Network Coding for Resilient Decentralized Wireless Networks
本文提出APC-RLNC系统，通过动态分组和分级网络编码解决去中心化无线网络中因节点间信道条件差异导致的通信鲁棒性问题。
0 citationsRead paper
Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
This study addresses the challenge of feedback control under noisy continuous measurements where quantum states are not directly accessible. We propose a Kraus-parameterized belief reinforcement learning method that integrates quantum state geometry into the learning loop. By imposing Stiefel manifold constraints on the encoder, the approach generates physically valid and interpretable density matrix estimates, while employing the PPO algorithm to achieve continuous control mapping. Experimental results demonstrate that this method attains a belief fidelity of 0.77–0.80 with significantly lower return variance compared to LSTM baselines. Consequently, it enables more stable quantum feedback control under non-ideal conditions, effectively resolving the lack of physical constraints in belief representations inherent to traditional approaches.
0 citationsRead paper