Xiao Hu
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LegalGraphRAG introduces a multi-agent, hierarchical graph retrieval-augmented generation framework to overcome the limitations of traditional RAG in legal domains, achieving state-of-the-art reliable legal reasoning.
The paper introduces EHRBench, a large-scale, automated, and reliable benchmark derived from real Electronic Health Records (EHRs) to rigorously evaluate the clinical decision-making capabilities of Large Language Models (LLMs).
MoG proposes a novel Mixture of Experts framework for graph-based RAG, which uses hub graphs to guide the sparse activation of domain-specific expert graphs, significantly improving retrieval accuracy.
This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.
Papers
A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection
Davood Fattahi, Runze Yan, Saurabh Kataria, Zhaoliang Chen +1 more
This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.