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.

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation

VRAIL: Vectorized Reward-based Attribution for Interpretable Learning

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs

Accurate and scalable exchange-correlation with deep learning

Working Document -- Formalising Software Requirements with Large Language Models

AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs

Bures-Wasserstein Flow Matching for Graph Generation

Distributional Training Data Attribution

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning

Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization

Exploring the Secondary Risks of Large Language Models

Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis

Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards

C-SEO Bench: Does Conversational SEO Work?

xInv: Explainable Optimization of Inverse Problems

Effective Red-Teaming of Policy-Adherent Agents

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

PlantDeBERTa: An Open Source Language Model for Plant Science

Reinforcement Learning Teachers of Test Time Scaling

DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis

Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning

Dual Debiasing for Noisy In-Context Learning for Text Generation

Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX

Maximizing Confidence Alone Improves Reasoning

Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)

Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation

Pretraining Language Models to Ponder in Continuous Space

SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback

Learning to Insert for Constructive Neural Vehicle Routing Solver

LightRetriever: A LLM-based Hybrid Retrieval Architecture with 1000x Faster Query Inference

MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence

Introducing voice timbre attribute detection

The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU

Learning to Reason under Off-Policy Guidance

Protecting Your Voice: Temporal-aware Robust Watermarking

Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data

AutoPDL: Automatic Prompt Optimization for LLM Agents

PiCo: Jailbreaking Multimodal Large Language Models via $\textbf{Pi}$ctorial $\textbf{Co}$de Contextualization

From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis

Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights

Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics

Shapley Revisited: Tractable Responsibility Measures for Query Answers

Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation

Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study

TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning

A Dual-Directional Context-Aware Test-Time Learning for Text Classification

LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation

HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge

Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions

POPGym Arcade: Parallel Pixelated POMDPs

BAnG: Bidirectional Anchored Generation for Conditional RNA Design

Directional Gradient Projection for Robust Fine-Tuning of Foundation Models

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

Steering LLMs for Formal Theorem Proving

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Compromising Honesty and Harmlessness in Language Models via Deception Attacks

Large Language Model Guided Self-Debugging Code Generation

ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently

Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment

AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement

SEAL: Scaling to Emphasize Attention for Long-Context Retrieval

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs

The Impact of Input Order Bias on Large Language Models for Software Fault Localization

GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

Rethinking Cancer Gene Identification through Graph Anomaly Analysis

SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation

DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models

Human Action CLIPs: Detecting AI-generated Human Motion

FLARE: Toward Universal Dataset Purification against Backdoor Attacks

G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation

Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning

Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control

Generating Energy-efficient code with LLMs

DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models

How Far is Video Generation from World Model: A Physical Law Perspective

One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models

The Hive Mind is a Single Reinforcement Learning Agent

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs

FutureFill: Fast Generation from Convolutional Sequence Models

Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models

Machine-learning based high-bandwidth magnetic sensing

MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning

Bridging Geometric Diffusion and Energy Minimization: A Unified Framework for Neural Message Passing

Large Language Models for Disease Diagnosis: A Scoping Review

RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming

Smooth InfoMax -- Towards Easier Post-Hoc Interpretability

PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference

UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation

Handling Numeric Expressions in Automatic Speech Recognition

"I understand why I got this grade": Automatic Short Answer Grading with Feedback

DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training

Rich Interoperable Metadata for Cultural Heritage Projects at Jagiellonian University

Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study

Created by

Haebom

저자

Mathew J. Walter, Aaron Barrett, Kimberly Tam

개요

본 논문은 자율 운항 선박과 같이 AI에 의존하는 자율 주행 시스템에 대한 적대적 AI 공격의 위협을 다룹니다. 기존 방어의 한계, 부적절한 보안 지표, 모델 수준 방어를 넘어선 복원력 구축의 필요성이라는 세 가지 주요 연구 과제를 제시합니다. 이러한 과제를 해결하기 위해, 다중 입력과 데이터 융합을 활용한 방어 구성 요소 및 AI 보안 지표를 구축하는 Data Fusion Cyber Resilience (DFCR) 방법을 제안합니다. 실제 시연과 정량적 분석을 통해 DFCR 방법을 기반으로 구축된 시스템을 단일 입력 모델 및 최첨단 방어를 사용하는 모델과 비교 평가합니다. 그 결과, DFCR 접근 방식이 해양 자율 시스템 운영에서 적대적 기계 학습 공격에 대한 복원력을 크게 향상시켜 다각적 섭동 공격의 손실을 최대 35%, 적대적 패치 공격의 손실을 최대 100%, 스푸핑 공격의 손실을 최대 100%까지 감소시키는 것을 보여줍니다. 또한, DFCR 및 DFCR 신뢰도 점수가 적대적 AI 접촉 신뢰도를 낮추고 시스템의 의사 결정을 개선하는 방법을 보여줍니다.

시사점, 한계점

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시사점:

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다중 입력과 데이터 융합을 활용한 DFCR 방법이 적대적 AI 공격에 대한 자율 운항 시스템의 복원력을 크게 향상시킨다는 것을 실증적으로 보여줌.

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DFCR 방법이 다양한 유형의 적대적 공격(섭동, 패치, 스푸핑)에 대해 효과적임을 확인.

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DFCR 신뢰도 점수를 활용하여 적대적 공격에 대한 시스템의 의사 결정을 개선할 수 있음을 제시.

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자율 주행 시스템의 보안 강화를 위한 새로운 접근 방식을 제시.

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한계점:

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논문에서 제시된 DFCR 방법의 일반화 가능성에 대한 추가 연구가 필요.

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다양한 유형의 적대적 공격 및 환경에 대한 DFCR 방법의 로버스트니스에 대한 추가적인 검증 필요.

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실제 해양 환경에서의 장기간 테스트 및 검증이 필요.

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계산 비용 및 복잡성에 대한 분석이 부족.

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