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.

Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions

On The Impact of Merge Request Deviations on Code Review Practices

Societal AI Research Has Become Less Interdisciplinary

Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks

Teaching Physical Awareness to LLMs through Sounds

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization

TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration

Your Agent Can Defend Itself against Backdoor Attacks

Learnable Spatial-Temporal Positional Encoding for Link Prediction

Unable to Forget: Proactive lnterference Reveals Working Memory Limits in LLMs Beyond Context Length

IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting

Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts

Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining

Vision Transformers Don't Need Trained Registers

Decoupling the Image Perception and Multimodal Reasoning for Reasoning Segmentation with Digital Twin Representations

AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking

Synthesis by Design: Controlled Data Generation via Structural Guidance

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

Pre-trained Large Language Models Learn Hidden Markov Models In-context

Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test

Can LLMs Generate Reliable Test Case Generators? A Study on Competition-Level Programming Problems

A Reinforcement Learning Approach for RIS-aided Fair Communications

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs

Context Is Not Comprehension: Unmasking LLM reasoning blind spots with VLO

HoliSafe: Holistic Safety Benchmarking and Modeling with Safety Meta Token for Vision-Language Model

Technical Report for Ego4D Long-Term Action Anticipation Challenge 2025

GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation

Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis

Bayesian Neural Scaling Law Extrapolation with Prior-Fitted Networks

DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography

SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting

Large Language Models Miss the Multi-Agent Mark

Rethinking Text-based Protein Understanding: Retrieval or LLM?

Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models

Discovering Forbidden Topics in Language Models

LIFEBench: Evaluating Length Instruction Following in Large Language Models

Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps

Reciprocity as the Foundational Substrate of Society: How Reciprocal Dynamics Scale into Social Systems

LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification

Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective

Convert Language Model into a Value-based Strategic Planner

Griffin: Towards a Graph-Centric Relational Database Foundation Model

Value Portrait: Assessing Language Models' Values through Psychometrically and Ecologically Valid Items

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism

Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment

Assessment of Evolving Large Language Models in Upper Secondary Mathematics

TerraMind: Large-Scale Generative Multimodality for Earth Observation

LEMUR Neural Network Dataset: Towards Seamless AutoML

Style over Substance: Distilled Language Models Reason Via Stylistic Replication

Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition

Chem42: a Family of chemical Language Models for Target-aware Ligand Generation

AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification

FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts

Weakly Supervised Multiple Instance Learning for Whale Call Detection and Temporal Localization in Long-Duration Passive Acoustic Monitoring

Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation

AAD-LLM: Neural Attention-Driven Auditory Scene Understanding

Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models

Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation

Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?

Conformal Prediction as Bayesian Quadrature

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis

Trustworthy AI: Safety, Bias, and Privacy -- A Survey

NestQuant: Nested Lattice Quantization for Matrix Products and LLMs

Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies

MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents

Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms

PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification

Bias Detection via Maximum Subgroup Discrepancy

Irony Detection, Reasoning and Understanding in Zero-shot Learning

TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval

An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems

Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field

7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement

Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation

Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning

CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization

GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints

Code-Switching Curriculum Learning for Multilingual Transfer in LLMs

CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

Phonology-Guided Speech-to-Speech Translation for African Languages

The Causal Information Bottleneck and Optimal Causal Variable Abstractions

Multimodal Pragmatic Jailbreak on Text-to-image Models

Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning

A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges

LogProber: Disentangling confidence from contamination in LLM responses

Holistic Uncertainty Estimation For Open-Set Recognition

AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling

XMeCap: Meme Caption Generation with Sub-Image Adaptability

CHOSEN: Compilation to Hardware Optimization Stack for Efficient Vision Transformer Inference

The Remarkable Robustness of LLMs: Stages of Inference?

BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection

Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistants

Created by

Haebom

저자

Yash Vekaria (UC Davis), Aurelio Loris Canino (Mediterranea University of Reggio Calabria), Jonathan Levitsky (UC Davis), Alex Ciechonski (University College London), Patricia Callejo (Universidad Carlos III de Madrid), Anna Maria Mandalari (University College London), Zubair Shafiq (UC Davis)

개요

본 논문은 10개의 인기있는 생성형 AI 브라우저 어시스턴트 확장 프로그램을 분석하여 사용자 데이터 수집, 저장, 처리 및 공유 방식을 조사했습니다. 네트워크 트래픽 분석과 새로운 프롬프팅 프레임워크를 사용하여 추적, 프로파일링 및 개인화 기능을 감사한 결과, 이러한 어시스턴트들은 대부분 로컬 브라우저 모델에 의존하지 않고 서버측 API에 의존하며, 사용자 상호작용 없이도 자동으로 호출될 수 있다는 것을 발견했습니다. 호출될 때, 웹 페이지 콘텐츠(전체 HTML DOM 및 사용자 입력 양식 포함), 식별자 및 사용자 프롬프트를 자체 서버 및 Google Analytics와 같은 제3자 추적기에 공유합니다. 민감한 정보(건강 정보, 이름, SSN 등)가 포함된 웹 페이지에서도 데이터 수집 및 공유가 지속됩니다. 또한, 여러 어시스턴트가 사용자의 연령, 성별, 소득, 관심사와 같은 인구 통계적 속성을 추론하여 브라우징 컨텍스트에 걸쳐 프로파일을 생성하고 응답을 개인화하는 것을 확인했습니다. 요약하자면, 본 연구는 생성형 AI 브라우저 어시스턴트가 안전장치 없이 개인 및 민감한 정보를 수집하여 프로파일링 및 개인화에 사용할 수 있음을 보여줍니다.

시사점, 한계점

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시사점:

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생성형 AI 브라우저 어시스턴트의 데이터 수집 및 사용 관행에 대한 심각한 우려 제기.

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사용자의 개인정보 보호 및 데이터 보안에 대한 위험성을 명확히 제시.

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생성형 AI 어시스턴트 개발 및 사용에 대한 윤리적, 법적 고려의 필요성 강조.

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더욱 강력한 개인정보 보호 및 데이터 보안 정책과 규제의 필요성 제시.

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한계점:

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분석 대상이 10개의 인기있는 확장 프로그램으로 제한됨.

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다양한 브라우저 환경 및 사용자 행동 패턴에 대한 일반화의 어려움.

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장기간에 걸친 데이터 수집 및 사용 패턴에 대한 분석이 부족.

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수집된 데이터의 실제 사용 목적 및 그 영향에 대한 추가 연구 필요.

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