Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Structure Transfer: an Inference-Based Calculus for the Transformation of Representations

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification

AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation

Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks

DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression

Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation

First Order Model-Based RL through Decoupled Backpropagation

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms

Beacon: Post-Training Quantization with Integrated Grid Selection

Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?

Vectorized Attention with Learnable Encoding for Quantum Transformer

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

MultiGen: Child-Friendly Multilingual Speech Generator with LLMs

StreetViewAI: Making Street View Accessible Using Context-Aware Multimodal AI

Street-Level AI: Are Large Language Models Ready for Real-World Judgments?

The KG-ER Conceptual Schema Language

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Conditional Video Generation for High-Efficiency Video Compression

TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP

Demographic-aware fine-grained classification of pediatric wrist fractures

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values

Stochastic Parameter Decomposition

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

MiniCPM4: Ultra-Efficient LLMs on End Devices

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling

How Can I Publish My LLM Benchmark Without Giving the True Answers Away?

Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks

Transferable Mask Transformer: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation

RBT4DNN: Requirements-based Testing of Neural Networks

Robust Offline Imitation Learning Through State-level Trajectory Stitching

Beyond holography: the entropic quantum gravity foundations of image processing

KNighter: Transforming Static Analysis with LLM-Synthesized Checkers

FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response

CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection

Rapid Word Learning Through Meta In-Context Learning

Image Embedding Sampling Method for Diverse Captioning

Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Extended Histogram-based Outlier Score (EHBOS)

A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

Breaking the Context Bottleneck on Long Time Series Forecasting

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision

ACING: Actor-Critic for Instruction Learning in Black-Box LLMs

Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance

Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames

AutoPETIII: The Tracer Frontier. What Frontier?

Long Input Sequence Network for Long Time Series Forecasting

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers

MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming

Diffusion on language model encodings for protein sequence generation

Style Transfer to Calvin and Hobbes comics using Stable Diffusion

Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems

Plan Verification for LLM-Based Embodied Task Completion Agents

EigenBench: A Comparative Behavioral Measure of Value Alignment

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Extending FKG.in: Towards a Food Claim Traceability Network

DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning

Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation

CP-Bench: Evaluating Large Language Models for Constraint Modelling

Axiomatics of Restricted Choices by Linear Orders of Sets with Minimum as Fallback

DMN-Guided Prompting: A Framework for Controlling LLM Behavior

Computational Basis of LLM's Decision Making in Social Simulation

Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers

Enhancing FKG.in: automating Indian food composition analysis

WASP: A Weight-Space Approach to Detecting Learned Spuriousness

Transferable Belief Model on Quantum Circuits

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions

Intelligence Primer

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset

Delta Activations: A Representation for Finetuned Large Language Models

DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation

Towards a Unified View of Large Language Model Post-Training

No Thoughts Just AI: Biased LLM Recommendations Limit Human Agency in Resume Screening

IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation

SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds

From Editor to Dense Geometry Estimator

Decoupled Entity Representation Learning for Pinterest Ads Ranking

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning

Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification

An Empirical Study of Vulnerabilities in Python Packages and Their Detection

How many patients could we save with LLM priors?

Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding

MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds

YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components

Attention as an Adaptive Filter

TAGAL: Tabular Data Generation using Agentic LLM Methods

Enhancing Technical Documents Retrieval for RAG

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

Created by

Haebom

저자

Juraj Vladika, Mahdi Dhaini, Florian Matthes

개요

본 논문은 대규모 언어 모델(LLMs)이 의료 연구 및 의사를 지원함으로써 의료 분야를 향상시킬 수 있는 잠재력을 가지고 있지만, 정적인 훈련 데이터에 의존하기 때문에 새로운 연구 및 발전에 따라 의료 권고안이 변화할 때 주요 위험이 발생한다는 점을 다룹니다. LLMs가 구식 의학 지식을 기억하면 유해한 조언을 제공하거나 임상 추론 과제에 실패할 수 있습니다. 이 문제를 조사하기 위해, 체계적 검토에서 파생된 두 가지 새로운 질문-응답(QA) 데이터 세트인 MedRevQA(일반적인 생의학 지식을 다루는 16,501개의 QA 쌍)와 MedChangeQA(의료 합의가 시간이 지남에 따라 변한 512개의 QA 쌍의 하위 집합)를 제시합니다. 8개의 주요 LLMs에 대한 데이터 세트 평가는 모든 모델에서 구식 지식에 대한 일관된 의존성을 보여줍니다. 또한 이 현상을 설명하기 위해 쓸모없는 사전 훈련 데이터와 훈련 전략의 영향을 분석하고, 완화를 위한 향후 방향을 제안하여 더욱 최신적이고 신뢰할 수 있는 의료 AI 시스템을 개발하기 위한 기반을 마련합니다.

시사점, 한계점

•

시사점:

◦

LLMs의 의료 분야 적용 시 구식 의학 지식에 대한 의존성 문제를 명확히 제시.

◦

구식 지식 문제를 평가하기 위한 새로운 QA 데이터셋(MedRevQA, MedChangeQA) 제시.

◦

다양한 LLMs에서 구식 지식에 대한 일관된 의존성을 실험적으로 입증.

◦

구식 지식 문제의 원인 분석 및 완화 방안 제시.

◦

더욱 신뢰할 수 있는 의료 AI 시스템 개발 위한 기반 마련.

•

한계점:

◦

제시된 데이터셋의 규모가 추가적인 연구를 위해서는 더 확장될 필요가 있음.

◦

분석에 사용된 LLM의 종류가 제한적임.

◦

제안된 완화 방안의 실효성 검증이 추가적으로 필요함.

Made with Slashpage