Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

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

SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition

DiffRegCD: Integrated Registration and Change Detection with Diffusion Features

Advancing mathematics research with generative AI

TiS-TSL: Image-Label Supervised Surgical Video Stereo Matching via Time-Switchable Teacher-Student Learning

Quantifying Edits Decay in Fine-tuned LLMs

Self-adaptive weighting and sampling for physics-informed neural networks

Too Good to be Bad: On the Failure of LLMs to Role-Play Villains

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models

Reasoning Up the Instruction Ladder for Controllable Language Models

Prompt Injection as an Emerging Threat: Evaluating the Resilience of Large Language Models

PISA-Bench: The PISA Index as a Multilingual and Multimodal Metric for the Evaluation of Vision-Language Models

Aligning Diffusion Language Models via Unpaired Preference Optimization

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

Global Convergence of Policy Gradient for Entropy Regularized Linear-Quadratic Control with multiplicative noise

SecInfer: Preventing Prompt Injection via Inference-time Scaling

Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey

Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts

Machine Unlearning for Responsible and Adaptive AI in Education

KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning

Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations

Radio Astronomy in the Era of Vision-Language Models: Prompt Sensitivity and Adaptation

SparK: Query-Aware Unstructured Sparsity with Recoverable KV Cache Channel Pruning

Survey of Vision-Language-Action Models for Embodied Manipulation

Mitigating Hallucinations in Large Language Models via Causal Reasoning

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction

The Power of Many: Synergistic Unification of Diverse Augmentations for Efficient Adversarial Robustness

Veli: Unsupervised Method and Unified Benchmark for Low-Cost Air Quality Sensor Correction

Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates

M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation

IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesian

Bridging Synthetic and Real-World Domains: A Human-in-the-Loop Weakly-Supervised Framework for Industrial Toxic Emission Segmentation

Bayesian preference elicitation for decision support in multiobjective optimization

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper

Kodezi Chronos: A Debugging-First Language Model for Repository-Scale Code Understanding

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes

Knowledge-Guided Brain Tumor Segmentation via Synchronized Visual-Semantic-Topological Prior Fusion

Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS

TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving

What Do Latent Action Models Actually Learn?

Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework

Flat Channels to Infinity in Neural Loss Landscapes

Edit Flows: Flow Matching with Edit Operations

MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

An empirical study of task and feature correlations in the reuse of pre-trained models

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

Solver-Free Decision-Focused Learning for Linear Optimization Problems

The Feasibility of Topic-Based Watermarking on Academic Peer Reviews

Military AI Needs Technically-Informed Regulation to Safeguard AI Research and its Applications

FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding

Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity

RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation

Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation

ViSA-Flow: Accelerating Robot Skill Learning via Large-Scale Video Semantic Action Flow

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers

MOSAIC: A Skill-Centric Algorithmic Framework for Long-Horizon Manipulation Planning

DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks

Repetitive Contrastive Learning Enhances Mamba's Selectivity in Time Series Prediction

Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models

Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Tasks

Large Language Models Are Unreliable for Cyber Threat Intelligence

A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination

Evolutionary Policy Optimization

What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models

MARS: Multi-Agent Adaptive Reasoning with Socratic Guidance for Automated Prompt Optimization

Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation

UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning

A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning

Contextual Thompson Sampling via Generation of Missing Data

FedP$^2$EFT: Federated Learning to Personalize PEFT for Multilingual LLMs

Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy

Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control

Training and Evaluating Language Models with Template-based Data Generation

The Visual Counter Turing Test (VCT2): A Benchmark for Evaluating AI-Generated Image Detection and the Visual AI Index (VAI)

Conditional Distribution Learning for Graph Classification

Large Language Model Benchmarks in Medical Tasks

LLM4AD: Large Language Models for Autonomous Driving - Concept, Review, Benchmark, Experiments, and Future Trends

TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data

Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships

An Information Theoretic Evaluation Metric For Strong Unlearning

CSAI: Conditional Self-Attention Imputation for Healthcare Time-series

Formalizing and Benchmarking Prompt Injection Attacks and Defenses

Hyperdimensional Decoding of Spiking Neural Networks

Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression

DigiData: Training and Evaluating General-Purpose Mobile Control Agents

A Theoretical Analysis of Detecting Large Model-Generated Time Series

Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning

Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis

Ariadne: A Controllable Framework for Probing and Extending VLM Reasoning Boundaries

e1: Learning Adaptive Control of Reasoning Effort

Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier

Mixed-Density Diffuser: Efficient Planning with Non-Uniform Temporal Resolution

AD-GPT: Large Language Models in Alzheimer's Disease

Created by

Haebom

저자

Ziyu Liu, Lintao Tang, Zeliang Sun, Zhengliang Liu, Yanjun Lyu, Wei Ruan, Yangshuang Xu, Liang Shan, Jiyoon Shin, Xiaohe Chen, Dajiang Zhu, Tianming Liu, Rongjie Liu, Chao Huang

개요

본 논문은 알츠하이머병(AD) 연구를 위한 도메인 특화 생성형 사전 훈련 변환기인 AD-GPT를 소개한다. AD-GPT는 Llama3과 BERT를 결합한 스택형 LLM 아키텍처로, AD 관련 유전 및 신경생물학적 정보의 검색 및 분석을 향상시키도록 설계되었다. 잠재적 AD 관련 유전자, 분자 유전 정보, 뇌 영역과 관련된 주요 유전자 변이 등 다양한 생의학 데이터 소스를 통합하여, 유전 정보 검색, 유전자-뇌 영역 관계 평가, 유전자-AD 관계 분석, 뇌 영역-AD 관계 매핑 등 AD 연구의 네 가지 중요한 작업에 최적화되었다. 기존 최첨단 LLM과의 비교 평가를 통해 AD-GPT의 우수한 정확성과 신뢰성을 입증하여, AD 연구 및 바이오마커 발견을 위한 강력하고 전문적인 AI 도구로서의 잠재력을 보여준다.

시사점, 한계점

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시사점:

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알츠하이머병 연구를 위한 특화된 LLM인 AD-GPT를 제시하여, 기존 LLM의 한계를 극복하였다.

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다양한 생의학 데이터 소스 통합을 통해 AD 관련 정보 검색 및 분석의 정확성과 신뢰성을 향상시켰다.

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AD 연구의 네 가지 중요한 작업(유전 정보 검색, 유전자-뇌 영역 관계 평가, 유전자-AD 관계 분석, 뇌 영역-AD 관계 매핑)에서 우수한 성능을 보였다.

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AD 연구 및 바이오마커 발견에 기여할 잠재력을 가진 강력한 AI 도구를 제공한다.

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한계점:

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논문에서 AD-GPT의 성능 평가에 사용된 데이터셋과 평가 지표에 대한 자세한 설명이 부족하다.

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AD-GPT의 일반화 성능 및 확장성에 대한 추가적인 연구가 필요하다.

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실제 임상 환경에서의 AD-GPT 적용 가능성 및 안전성에 대한 검증이 필요하다.

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