Jingfang: An LLM-Based Multi-Agent System for Precise Med...

cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods

Yehan Yang, Tianhao Ma, Ruotai Li, Xinhan Zheng, Guodong Shan · 2025-02-04 · via cs.LG updates on arXiv.org

AI 总结

现有面向中医的大型语言模型存在两个关键缺陷：僵化的咨询框架无法进行全面的个性化问诊，常导致诊断偏差；治疗建议缺乏严格的辨证论治，偏离中医核心诊疗原则。为解决这些问题，研究团队开发了基于大语言模型的多智能体系统JingFang（JF），通过整合多个中医专科智能体，模拟真实诊疗场景，实现个性化医疗咨询、精准辨证和推荐治疗方案。
JingFang系统构建了中医多智能体协同咨询机制（MACCM），多个智能体协作模拟真实的中医诊断流程，提升基础大语言模型的诊断能力，以提供准确且个性化的医疗服务。系统还引入了专用的辨证智能体，该智能体在预处理数据集上进行了微调，并在治疗智能体内设计了双阶段恢复方案（DSRS），共同显著提高了模型在辨证和治疗方面的准确性。
综合评估和实验表明，JingFang在医疗咨询方面表现出优越性能。与现有中医模型相比，其辨证精确度提升了至少124%；与最先进的大语言模型（SOTA LLMs）相比，精确度也提升了至少21.1%。这些结果证实了多智能体协同和专用模块设计的有效性，为人工智能辅助中医诊疗提供了可行方案。

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cs.LG updates on arXiv.org