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.

Private Frequency Estimation Via Residue Number Systems

A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication

Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions

Virtual Width Networks

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types

DialogGraph-LLM: Graph-Informed LLMs for End-to-End Audio Dialogue Intent Recognition

A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space

MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns

DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile

BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages

T2IBias: Uncovering Societal Bias Encoded in the Latent Space of Text-to-Image Generative Models

History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting

C$^3$TG: Conflict-aware, Composite, and Collaborative Controlled Text Generation

Good-for-MDP State Reduction for Stochastic LTL Planning

PAN: A World Model for General, Interactable, and Long-Horizon World Simulation

HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios

MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System

Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking

Self-Correction Distillation for Structured Data Question Answering

Test-driven Reinforcement Learning

Learning Quantized Continuous Controllers for Integer Hardware

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity

Ghost in the Transformer: Tracing LLM Lineage with SVD-Fingerprint

Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

Measuring Model Performance in the Presence of an Intervention

Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification

MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers

Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery

DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms

Multi-Personality Generation of LLMs at Decoding-time

LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation

Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography

Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies

MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding

VeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Verus

Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions

Geometric Algorithms for Neural Combinatorial Optimization with Constraints

SelecTKD: Selective Token-Weighted Knowledge Distillation for LLMs

Integrating Genomics into Multimodal EHR Foundation Models

CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata

Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making

When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA

Local Guidance for Configuration-Based Multi-Agent Pathfinding

3D Optimization for AI Inference Scaling: Balancing Accuracy, Cost, and Latency

SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios

DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

Dedelayed: Deleting remote inference delay via on-device correction

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM

On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning

A Human Behavioral Baseline for Collective Governance in Software Projects

Implicit-Knowledge Visual Question Answering with Structured Reasoning Traces

Reasoning under Vision: Understanding Visual-Spatial Cognition in Vision-Language Models for CAPTCHA

Global Convergence of Policy Gradient for Entropy Regularized Linear-Quadratic Control with Multiplicative Noise

Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology

Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosis

A Measurement Study of Model Context Protocol Ecosystem

Energy Guided Geometric Flow Matching

HyperCore: Coreset Selection under Noise via Hypersphere Models

VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction

Understanding Post-Training Structural Changes in Large Language Models

Highly Imbalanced Regression with Tabular Data in SEP and Other Applications

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

A GPU-Accelerated RAG-Based Telegram Assistant for Supporting Parallel Processing Students

Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

Realism Control One-step Diffusion for Real-World Image Super-Resolution

An Ontology-Based Approach to Optimizing Geometry Problem Sets for Skill Development

AHAMask: Reliable Task Specification for Large Audio Language Models without Instructions

NoLBERT: A No Lookahead(back) Foundational Language Model

SoK: Large Language Model Copyright Auditing via Fingerprinting

PAX-TS: Model-agnostic multi-granular explanations for time series forecasting via localized perturbations

Multi-Metric Preference Alignment for Generative Speech Restoration

Error analysis for the deep Kolmogorov method

Interpreting the Effects of Quantization on LLMs

Few-shot Class-incremental Fault Diagnosis by Preserving Class-Agnostic Knowledge with Dual-Granularity Representations

STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach

Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation

edgeVLM: Cloud-edge Collaborative Real-time VLM based on Context Transfer

What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains

Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression

HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models

NLP Methods May Actually Be Better Than Professors at Estimating Question Difficulty

Landsat30-AU: A Vision-Language Dataset for Australian Landsat Imagery

Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System

MoCHA: Advanced Vision-Language Reasoning with MoE Connector and Hierarchical Group Attention

Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs

TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model

Machine Unlearning of Traffic State Estimation and Prediction

Benchmarking LLM Privacy Recognition for Social Robot Decision Making

Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars

AI-Native Open RAN for Non-Terrestrial Networks: An Overview

Sequential Attention-based Sampling for Histopathological Analysis

RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges

Created by

Haebom

저자

Joshua Hatherley, Robert Sparrow

개요

본 논문은 의료 분야에서 지속적으로 진화하는 적응형 머신러닝(ML) 시스템의 윤리적 문제에 대한 튜토리얼을 제공한다. 기존 연구에서 간과되었던 적응형 ML 시스템의 두 가지 변동성, 즉 시간에 따른 변화(diachronic evolution)와 공시적 차이(synchronic variation)를 구분하고, 특히 공시적 차이의 중요성을 강조한다. 시간에 따른 변화와 공시적 차이가 환자 치료의 질, 동의된 정보, 형평성에 미치는 영향과 관련된 어려움을 제시하고, 이러한 시스템 설계에 내포된 복잡한 윤리적 상충 관계를 논의한다. 대상 독자는 머신러닝 AI 시스템 개발자, 의료 규제 기관, 의료 정보학 커뮤니티 및 임상의이다.

시사점, 한계점

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시사점: 의료 분야 적응형 ML 시스템의 윤리적 문제에 대한 체계적인 논의를 제공하여 개발자, 규제기관, 의료 전문가들의 인식 제고에 기여한다. 시간에 따른 변화와 공시적 차이라는 두 가지 중요한 변동성을 구분하여 분석함으로써 보다 균형적인 윤리적 고찰을 가능하게 한다. 환자 치료의 질, 동의된 정보, 형평성에 대한 영향을 분석하여 적응형 ML 시스템 설계 및 구현에 대한 윤리적 지침을 마련하는 데 도움을 준다.

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한계점: 본 논문은 주로 윤리적 측면에 초점을 맞추고 있으며, 기술적인 해결책이나 구체적인 규제 방안에 대한 논의는 부족하다. 실제 의료 현장 적용에 대한 구체적인 사례 연구나 분석이 부족하여, 제시된 윤리적 문제의 실질적인 영향을 정확히 파악하기 어렵다. 다양한 의료 환경과 적응형 ML 시스템의 다양성을 충분히 반영하지 못할 수 있다.

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