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

Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications

Created by

Haebom

저자

Sangyoon Bae, Junbeom Kwon, Shinjae Yoo, Jiook Cha

개요

본 논문은 뇌의 복잡한 비선형 동역학이 적응적 인지 및 행동을 생성하는 과정을 이해하는 것을 목표로 한다. 뇌의 동역학은 척도 불변성 및 다중 분형 특성을 나타내며, 신경망 재구성에 영향을 미친다. 기존 신경 영상 모델은 선형성 및 정상성 가정에 제한되어 이러한 과정을 포착하는 데 어려움이 있다. 본 논문에서는 장거리 의존성을 포착하는 것으로 알려진 Transformer 기반 구조를 활용하여 주파수 특이적 시공간 뇌 역학을 모델링하는 Multi-Band Brain Net (MBBN) 프레임워크를 제시한다. MBBN은 척도 불변 네트워크 원리를 주파수 분해 다중 대역 자기 주의 메커니즘과 통합한다. UK Biobank, ABCD, ABIDE 세 개의 대규모 신경 영상 코호트(총 45,951명)를 사용하여 훈련된 MBBN은 이전에는 감지할 수 없었던 주파수 의존적 네트워크 상호 작용을 밝혀내어 정신 질환(ADHD, ASD, 우울증)의 연결성 장애에 대한 통찰력을 제공한다. MBBN은 최첨단 방법보다 최대 30.59% 높은 예측 정확도를 달성하여 주파수 정보를 활용한 시공간 모델링의 이점을 보여준다. 해석 가능한 프레임워크를 제공하여 신경 계산이 인지 기능과 정신 질환 취약성을 어떻게 뒷받침하는지에 대한 통찰력을 제공하며, 뇌 디코딩, 인지 신경 과학 및 정밀 정신 의학에 시사점을 제공한다.

시사점, 한계점

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시사점:

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Transformer 기반의 새로운 신경 영상 분석 프레임워크(MBBN) 제시.

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주파수 특이적 시공간 뇌 역학 모델링을 통해 기존 방법보다 향상된 예측 정확도 달성 (최대 30.59% 향상).

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ADHD, ASD, 우울증과 같은 정신 질환에서의 연결성 장애에 대한 새로운 통찰력 제공.

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주파수 특이적 바이오마커 발견을 통한 신경 발달 장애 이해 증진.

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뇌 디코딩, 인지 신경 과학 및 정밀 정신 의학 분야에 대한 시사점 제시.

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대규모 데이터셋을 활용한 견고한 일반화 성능 입증.

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한계점:

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본 논문에서는 구체적인 한계점이 언급되지 않았다. 추가 연구를 통해 알고리즘의 robustness 및 scalability에 대한 검증이 필요할 수 있다. 다양한 인구집단 및 질환에 대한 추가적인 검증도 필요하다. 해석 가능성에 대한 추가적인 연구가 필요할 수 있다.

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