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.

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization

Physics-Informed Neural Networks for Nonlinear Output Regulation

Unlocking the Forgery Detection Potential of Vanilla MLLMs: A Novel Training-Free Pipeline

Generalized Denoising Diffusion Codebook Models (gDDCM): Tokenizing images using a pre-trained diffusion model

Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations

Dimension vs. Precision: A Comparative Analysis of Autoencoders and Quantization for Efficient Vector Retrieval on BEIR SciFact

UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective

PFAvatar: Pose-Fusion 3D Personalized Avatar Reconstruction from Real-World Outfit-of-the-Day Photos

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning

Fine-Grained Representation for Lane Topology Reasoning

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization

A Meta-Heuristic Load Balancer for Cloud Computing Systems

Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)

MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

Who Gets the Reward, Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents

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

LocalBench: Benchmarking LLMs on County-Level Local Knowledge and Reasoning

LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls

Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks

FGM-HD: Boosting Generation Diversity of Fractal Generative Models through Hausdorff Dimension Induction

Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis

Resilient by Design -- Active Inference for Distributed Continuum Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation

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

Leveraging LLM-based agents for social science research: insights from citation network simulations

Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response

DRIP: Defending Prompt Injection via Token-wise Representation Editing and Residual Instruction Fusion

Clone Deterministic 3D Worlds

Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics

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

FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection

From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance

PromptGuard at BLP-2025 Task 1: A Few-Shot Classification Framework Using Majority Voting and Keyword Similarity for Bengali Hate Speech Detection

A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

Towards Sharper Object Boundaries in Self-Supervised Depth Estimation

Patent Language Model Pretraining with ModernBERT

Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs

Efficient Decoding Methods for Language Models on Encrypted Data

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

DepthVision: Enabling Robust Vision-Language Models with GAN-Based LiDAR-to-RGB Synthesis for Autonomous Driving

Automatic Differentiation of Agent-Based Models

Do Retrieval Augmented Language Models Know When They Don't Know?

Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning

RynnEC: Bringing MLLMs into Embodied World

LENS: Learning to Segment Anything with Unified Reinforced Reasoning

MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions

Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework

LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations

SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation

GAIS: Frame-Level Gated Audio-Visual Integration with Semantic Variance-Scaled Perturbation for Text-Video Retrieval

GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation

FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed

OptScale: Probabilistic Optimality for Inference-time Scaling

EvoLM: In Search of Lost Language Model Training Dynamics

Synthetic Geology: Structural Geology Meets Deep Learning

Foundation Models in Medical Imaging: A Review and Outlook

FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning

OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model

Scaling Textual Gradients via Sampling-Based Momentum

MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration

In-context Language Learning for Endangered Languages in Speech Recognition

SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs

Preference Learning with Lie Detectors can Induce Honesty or Evasion

Anti-adversarial Learning: Desensitizing Prompts for Large Language Models

AdCare-VLM: Towards a Unified and Pre-aligned Latent Representation for Healthcare Video Understanding

Non-Uniform Class-Wise Coreset Selection for Vision Model Fine-tuning

ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

A Survey of Cross-domain Graph Learning: Progress and Future Directions

Availability-aware Sensor Fusion via Unified Canonical Space

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem

Personalized Image Generation for Recommendations Beyond Catalogs

IPAD: Inverse Prompt for AI Detection - A Robust and Interpretable LLM-Generated Text Detector

MoM: Linear Sequence Modeling with Mixture-of-Memories

Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Caching

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Higher-Order Transformers With Kronecker-Structured Attention

Optimizing Federated Learning by Entropy-Based Client Selection

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games

Generating Streamlining Constraints with Large Language Models

The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer

Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective

Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

NAIST Academic Travelogue Dataset

SemCo: Toward Semantic Coherent Visual Relationship Forecasting

Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

Requirements for Aligned, Dynamic Resolution of Conflicts in Operational Constraints

CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D

VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation

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

Dynamical errors in machine learning forecasts

Created by

Haebom

저자

Zhou Fang, Gianmarco Mengaldo

개요

본 논문은 기계학습 예측에서 MAE와 MSE와 같은 표준 오차 측정법이 예측과 목표값 사이의 차이만을 정량화하고, 예측의 물리적 및/또는 동역학적 일관성을 직접 평가하지 못하는 점을 지적합니다. 따라서 기계학습 예측이 기저 시스템의 동역학적 행동을 유지하는지 여부를 탐구하고, 순간 차원(d)과 역 지속성(θ)이라는 두 가지 동역학 지표를 활용하여 표준 오차 측정법과 기저 시스템의 동역학적 특성 간의 관계를 조사합니다. Lorenz 시스템, Kuramoto-Sivashinsky 방정식, Kolmogorov 흐름 세 가지 표준 데이터셋과 실제 기상 예보 작업에 대한 직접 및 재귀적 예측 전략을 분석하여, 특히 긴 예측 리드 타임이나 긴 재귀적 시뮬레이션에서 기계학습 예측의 동역학적 특성 왜곡을 밝혀냅니다. 이를 통해 기계학습 모델 개선에 활용될 수 있는 기계학습 예측 충실도에 대한 보완적인 정보를 제공합니다.

시사점, 한계점

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시사점:

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기존 오차 측정법(MAE, MSE)의 한계를 지적하고, 동역학적 지표를 활용한 새로운 오차 측정법을 제시함으로써 기계학습 예측의 동역학적 일관성 평가에 대한 새로운 관점을 제공합니다.

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긴 예측 리드 타임이나 긴 재귀적 시뮬레이션에서 기계학습 예측의 동역학적 특성 왜곡을 밝히고, 이를 개선하기 위한 방향을 제시합니다.

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제안된 동역학 지표 기반 오차 측정법은 기계학습 모델의 충실도를 평가하고 잠재적인 오류 모드를 식별하는 데 유용한 보완적 정보를 제공합니다.

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한계점:

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제안된 동역학 지표 기반 오차 측정법의 일반화 가능성에 대한 추가 연구가 필요합니다.

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다양한 기계학습 모델과 데이터셋에 대한 광범위한 실험이 필요합니다.

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동역학적 지표의 선택이 결과에 미치는 영향에 대한 추가 분석이 필요합니다.

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