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.

VGR: Visual Grounded Reasoning

Improving Multimodal Learning Balance and Sufficiency through Data Remixing

Autonomous Computer Vision Development with Agentic AI

A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation

Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models

From Reasoning to Code: GRPO Optimization for Underrepresented Languages

Farseer: A Refined Scaling Law in Large Language Models

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors

VideoDeepResearch: Long Video Understanding With Agentic Tool Using

Data Shifts Hurt CoT: A Theoretical Study

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations

Disclosure Audits for LLM Agents

A Survey of Generative Categories and Techniques in Multimodal Large Language Models

Resa: Transparent Reasoning Models via SAEs

EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

Latent Multi-Head Attention for Small Language Models

Intra-Trajectory Consistency for Reward Modeling

Foundation Models in Medical Imaging -- A Review and Outlook

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model

Segment Concealed Objects with Incomplete Supervision

Inherently Faithful Attention Maps for Vision Transformers

DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression

Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework

Reparameterized LLM Training via Orthogonal Equivalence Transformation

Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries

LeVo: High-Quality Song Generation with Multi-Preference Alignment

FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement

SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments

MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks

Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

SAFE: Finding Sparse and Flat Minima to Improve Pruning

Tactile MNIST: Benchmarking Active Tactile Perception

SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System

MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP

Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

From Argumentative Text to Argument Knowledge Graph: A New Framework for Structured Argumentation

An Incremental Framework for Topological Dialogue Semantics: Efficient Reasoning in Discrete Spaces

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning

R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

Quantum computing and artificial intelligence: status and perspectives

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks

On the performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

SOSBENCH: Benchmarking Safety Alignment on Scientific Knowledge

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Transparency in Healthcare AI: Testing European Regulatory Provisions against Users' Transparency Needs

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision

Fixed Point Explainability

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

JAEGER: Dual-Level Humanoid Whole-Body Controller

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

AI Recommendations and Non-instrumental Image Concerns

Entropic Time Schedulers for Generative Diffusion Models

JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture

Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design

Scholar Inbox: Personalized Paper Recommendations for Scientists

On Synthesizing Data for Context Attribution in Question Answering

Evaluating how LLM annotations represent diverse views on contentious topics

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion

Transformers without Normalization

Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model

From Euler to AI: Unifying Formulas for Mathematical Constants

C2-DPO: Constrained Controlled Direct Preference Optimization

Less is More: Improving LLM Alignment via Preference Data Selection

Quantifying Memorization and Parametric Response Rates in Retrieval-Augmented Vision-Language Models

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series

Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

HARBOR: Exploring Persona Dynamics in Multi-Agent Competition

SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models

Truth Knows No Language: Evaluating Truthfulness Beyond English

PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

Is attention all you need to solve the correlated electron problem?

Optimizing Temperature for Language Models with Multi-Sample Inference

Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training

The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search

Activation-Informed Merging of Large Language Models

Layer by Layer: Uncovering Hidden Representations in Language Models

Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss

Disclosure Audits for LLM Agents

Created by

Haebom

저자

Saswat Das, Jameson Sandler, Ferdinando Fioretto

개요

본 논문은 대규모 언어 모델(LLM) 에이전트의 개인정보 유출 위험을 정량적으로 평가하고 감사하는 프레임워크인 Conversational Manipulation for Privacy Leakage (CMPL)을 제안합니다. CMPL은 엄격한 개인정보 보호 지침을 적용하는 에이전트를 스트레스 테스트하기 위해 설계된 반복적인 조사 전략으로, 단일 유출 사건에만 초점을 맞추는 것이 아니라 현실적인 다중 턴 상호 작용을 시뮬레이션하여 잠재적인 취약성을 체계적으로 발견합니다. 다양한 도메인, 데이터 모달리티 및 안전 구성에 대한 평가를 통해 기존의 단일 턴 방어로는 막을 수 없는 개인정보 위험을 드러낼 수 있음을 보여줍니다. CMPL을 진단 도구로 제시하는 것 외에도, 정량화 가능한 위험 지표를 기반으로 하는 감사 절차와 에이전트 구현 전반에 걸친 대화형 개인정보 보호 평가를 위한 공개 벤치마크를 제공합니다.

시사점, 한계점

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시사점:

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LLM 에이전트의 개인정보 유출 위험을 효과적으로 평가하고 감사하는 새로운 프레임워크(CMPL) 제시

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기존의 단일 턴 방어로는 탐지하기 어려운 다중 턴 상호작용을 통한 잠재적 취약성 발견 가능성 제시

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정량화 가능한 위험 지표를 기반으로 한 감사 절차 및 공개 벤치마크 제공으로 연구의 투명성과 재현성 증가

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다양한 도메인 및 데이터 모달리티에 대한 적용 가능성을 입증

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한계점:

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CMPL 프레임워크의 실제 환경 적용 및 효과에 대한 추가 연구 필요

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다양한 유형의 LLM 에이전트 및 개인정보 보호 정책에 대한 일반화 가능성 검증 필요

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벤치마크 데이터셋의 범위 및 대표성에 대한 추가적인 고려 필요

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복잡하고 정교한 공격 기법에 대한 CMPL의 효과성 검증 필요

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