惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Recent Announcements
Recent Announcements
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Last Week in AI
Last Week in AI
Scott Helme
Scott Helme
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
L
LINUX DO - 最新话题
S
Security @ Cisco Blogs
Webroot Blog
Webroot Blog
S
Security Affairs
H
Hacker News: Front Page
TaoSecurity Blog
TaoSecurity Blog
W
WeLiveSecurity
G
GRAHAM CLULEY
T
Tenable Blog
Schneier on Security
Schneier on Security
S
Securelist
Cyberwarzone
Cyberwarzone
P
Privacy International News Feed
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
S
Schneier on Security
Hacker News - Newest:
Hacker News - Newest: "LLM"
Recent Commits to openclaw:main
Recent Commits to openclaw:main
O
OpenAI News
N
News and Events Feed by Topic
AWS News Blog
AWS News Blog
C
Cisco Blogs
T
Threat Research - Cisco Blogs
S
Secure Thoughts
大猫的无限游戏
大猫的无限游戏
C
Check Point Blog
The GitHub Blog
The GitHub Blog
G
Google Developers Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
美
美团技术团队
Martin Fowler
Martin Fowler
Microsoft Security Blog
Microsoft Security Blog
L
LangChain Blog
Apple Machine Learning Research
Apple Machine Learning Research
爱范儿
爱范儿
D
DataBreaches.Net
博
博客园_首页
MyScale Blog
MyScale Blog
博
博客园 - 叶小钗
博
博客园 - 三生石上(FineUI控件)
P
Proofpoint News Feed
J
Java Code Geeks
SecWiki News
SecWiki News
P
Palo Alto Networks Blog
Know Your Adversary
Know Your Adversary
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org

cs.CL updates on arXiv.org

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Evaluating Memory Capability in Continuous Lifelog Scenario How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series TInR: Exploring Tool-Internalized Reasoning in Large Language Models Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation BlasBench: An Open Benchmark for Irish Speech Recognition Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models Learning and Enforcing Context-Sensitive Control for LLMs HeceTokenizer: A Syllable-Based Tokenization Approach for Turkish Retrieval Efficient Process Reward Modeling via Contrastive Mutual Information Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models LLMs Should Incorporate Explicit Mechanisms for Human Empathy ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation Dynamic Adaptive Attention and Supervised Contrastive Learning: A Novel Hybrid Framework for Text Sentiment Classification EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning Instruction Data Selection via Answer Divergence CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning Turing or Cantor: That is the Question LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection A Structured Clustering Approach for Inducing Media Narratives Adaptive Multi-Expert Reasoning via Difficulty-Aware Routing and Uncertainty-Guided Aggregation Comparative Analysis of Large Language Models in Healthcare CodeComp: Structural KV Cache Compression for Agentic Coding Relational Probing: LM-to-Graph Adaptation for Financial Prediction FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification Nationality encoding in language model hidden states: Probing culturally differentiated representations in persona-conditioned academic text Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations CircuitSynth: Reliable Synthetic Data Generation Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models Weird Generalization is Weirdly Brittle Human vs. Machine Deception: Distinguishing AI-Generated and Human-Written Fake News Using Ensemble Learning Should We be Pedantic About Reasoning Errors in Machine Translation? Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering GIANTS: Generative Insight Anticipation from Scientific Literature Many-Tier Instruction Hierarchy in LLM Agents Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition Regime-Conditional Retrieval: Theory and a Transferable Router for Two-Hop QA ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs $p1$: Better Prompt Optimization with Fewer Prompts Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition Skip-Connected Policy Optimization for Implicit Advantage PRAGMA: Revolut Foundation Model Linear Representations of Hierarchical Concepts in Language Models Generating High Quality Synthetic Data for Dutch Medical Conversations HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations Self-Calibrating Language Models via Test-Time Discriminative Distillation Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows with FHIR Integration Reasoning Models Will Sometimes Lie About Their Reasoning
Jingfang: An LLM-Based Multi-Agent System for Precise Medical Consultation and Syndrome Differentiation in Traditional Chinese Medicine
Yehan Yang, Tianhao Ma, Ruotai Li, Xinhan Zheng, Guodong Shan · 2025-02-04 · via cs.CL updates on arXiv.org
AI 总结

  1. 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。

  2. 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。

  3. 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。

The practice of Traditional Chinese Medicine (TCM) requires profound expertise and extensive clinical experience. While Large Language Models (LLMs) offer significant potential in this domain, current TCM-oriented LLMs suffer two critical limitations: (1) a rigid consultation framework that fails to conduct comprehensive and patient-tailored interactions, often resulting in diagnostic inaccuracies; and (2) treatment recommendations generated without rigorous syndrome differentiation, which deviates from the core diagnostic and therapeutic principles of TCM. To address these issues, we develop \textbf{JingFang (JF)}, an advanced LLM-based multi-agent system for TCM that facilitates the implementation of AI-assisted TCM diagnosis and treatment. JF integrates various TCM Specialist Agents in accordance with authentic diagnostic and therapeutic scenarios of TCM, enabling personalized medical consultations, accurate syndrome differentiation and treatment recommendations. A \textbf{Multi-Agent Collaborative Consultation Mechanism (MACCM)} for TCM is constructed, where multiple Agents collaborate to emulate real-world TCM diagnostic workflows, enhancing the diagnostic ability of base LLMs to provide accurate and patient-tailored medical consultation. Moreover, we introduce a dedicated \textbf{Syndrome Differentiation Agent} fine-tuned on a preprocessed dataset, along with a designed \textbf{Dual-Stage Recovery Scheme (DSRS)} within the Treatment Agent, which together substantially improve the model's accuracy of syndrome differentiation and treatment. Comprehensive evaluations and experiments demonstrate JF's superior performance in medical consultation, and also show improvements of at least 124% and 21.1% in the precision of syndrome differentiation compared to existing TCM models and State of the Art (SOTA) LLMs, respectively.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。