Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Horus: A Protocol for Trustless Delegation Under Uncertainty

Reducing Variability of Multiple Instance Learning Methods for Digital Pathology

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

Towards Safety Evaluations of Theory of Mind in Large Language Models

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Pre-training Large Memory Language Models with Internal and External Knowledge

Towards Universal Semantics With Large Language Models

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Enhancing Robustness to Missing Modalities through Clustered Federated Learning

Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?

Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

SFO: Piloting VLM Feedback for Offline RL

Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks

KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis

Distribution Matching for Self-Supervised Transfer Learning

A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior

SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

A Framework for Mining Collectively-Behaving Bots in MMORPGs

Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models

Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication

Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization

NegMerge: Sign-Consensual Weight Merging for Machine Unlearning

Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems

Backdooring Bias (B^2) into Stable Diffusion Models

Embodied Instruction Following in Unknown Environments

Improving Consistency Models with Generator-Augmented Flows

OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors

Divergent Creativity in Humans and Large Language Models

SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems

Squat: Quant Small Language Models on the Edge

Dataset Distillation via the Wasserstein Metric

The Boolean Solution Problem from the Perspective of Predicate Logic -- Extended Version

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

World-aware Planning Narratives Enhance Large Vision-Language Model Planner

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Adapting Probabilistic Risk Assessment for AI

Beating Transformers using Synthetic Cognition

MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow

Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

MILP-SAT-GNN: Yet Another Neural SAT Solver

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

How Do Vision-Language Models Process Conflicting Information Across Modalities?

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

Probing Evaluation Awareness of Language Models

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

BranchNet: A Neuro-Symbolic Learning Framework for Structured Multi-Class Classification

GPU-based complete search for nonlinear minimization subject to bounds

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow

Created by

Haebom

저자

Ziyue Wang, Junde Wu, Linghan Cai, Chang Han Low, Xihong Yang, Qiaxuan Li, Yueming Jin

개요

본 논문은 의료 진단에 있어서 다양한 모달리티의 환자 데이터를 효과적으로 통합하는 대규모 비전-언어 모델(VLMs)과 에이전트 기반 방법의 잠재력을 활용하는 새로운 에이전트 추론 패러다임인 MedAgent-Pro를 제안합니다. MedAgent-Pro는 현대 의학의 진단 원칙을 따르는 단계별, 증거 기반 추론을 위해 진단 과정을 순차적 구성 요소로 분리합니다. 질병 수준의 표준화된 계획 생성과 환자 수준의 개인화된 단계별 추론으로 구성된 계층적 진단 구조를 제시하며, 질병 수준 계획을 지원하기 위해 의료 지침을 검색하는 RAG 기반 에이전트를 설계하여 임상 표준과의 일치성을 보장합니다. 환자 수준 추론을 위해서는 시각적 모델과 같은 전문 도구를 통합하여 정량적 평가를 가능하게 합니다. 또한, 각 단계의 신뢰성을 검증하여 증거 기반 진단을 달성하고 엄격한 논리적 추론과 근거 있는 결론을 강화합니다. 다양한 해부학적 영역, 영상 모달리티 및 질병에 대한 광범위한 실험을 통해 MedAgent-Pro가 주류 VLM, 에이전트 시스템 및 최첨단 전문가 모델보다 우수함을 보여줍니다. 에이블레이션 연구와 임상 전문가의 인간 평가는 MedAgent-Pro의 강건성과 임상적 관련성을 추가적으로 검증합니다. 코드는 https://github.com/jinlab-imvr/MedAgent-Pro 에서 이용 가능합니다.

GitHub - jinlab-imvr/MedAgent-Pro: The official code for MedAgent_Pro

The official code for MedAgent_Pro. Contribute to jinlab-imvr/MedAgent-Pro development by creating an account on GitHub.

시사점, 한계점

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시사점:

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현대 의학의 진단 원칙을 따르는 새로운 에이전트 추론 패러다임 MedAgent-Pro 제시.

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단계별, 증거 기반 추론을 통한 신뢰성 및 임상 사용성 향상.

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질병 수준 계획 및 환자 수준 추론을 위한 계층적 진단 구조 구현.

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RAG 기반 에이전트와 시각적 모델 통합을 통한 정량적 평가 및 임상 표준 준수.

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광범위한 실험 및 인간 평가를 통한 우수성 및 강건성 검증.

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공개된 코드를 통한 접근성 향상.

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한계점:

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논문에서 구체적으로 언급된 한계점은 없으나, 실제 임상 적용을 위한 추가적인 검증 및 다양한 질병 및 환자 데이터에 대한 일반화 성능 평가가 필요할 수 있음.

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특정 의료 지침이나 데이터셋에 대한 의존성으로 인한 편향 가능성.

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RAG 기반 에이전트의 의료 지침 검색 정확도에 대한 의존성.

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복잡한 질병 진단에 대한 일반화 성능 및 예측 불확실성에 대한 정량적 평가 부족.

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