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.

Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks

Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space

SageAttention2++: A More Efficient Implementation of SageAttention2

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis

Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement

Cooperation of Experts: Fusing Heterogeneous Information with Large Margin

RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph

CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models

VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models

In-context Language Learning for Endangered Languages in Speech Recognition

OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

Inference-time Alignment in Continuous Space

Incentivizing Strong Reasoning from Weak Supervision

JEDI: Latent End-to-end Diffusion Mitigates Agent-Human Performance Asymmetry in Model-Based Reinforcement Learning

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems

Towards Large Reasoning Models for Agriculture

VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use

OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models

Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding

Towards Practical Defect-Focused Automated Code Review

EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion

PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Association

Text Generation Beyond Discrete Token Sampling

AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping

Advancing Sequential Numerical Prediction in Autoregressive Models

Towards Visuospatial Cognition via Hierarchical Fusion of Visual Experts

Visuospatial Cognitive Assistant

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs

Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL

LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

Continuous Thought Machines

Robust Localization, Mapping, and Navigation for Quadruped Robots

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline

Progressive Language-guided Visual Learning for Multi-Task Visual Grounding

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Layers at Similar Depths Generate Similar Activations Across LLM Architectures

A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

Token embeddings violate the manifold hypothesis

HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment

Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models

Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration

Language-Enhanced Representation Learning for Single-Cell Transcriptomics

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation

Wanda++: Pruning Large Language Models via Regional Gradients

LINGOLY-TOO: Disentangling Reasoning from Knowledge with Templatised Orthographic Obfuscation

Deficient Excitation in Parameter Learning

Interpreting CLIP with Hierarchical Sparse Autoencoders

BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction

Self-Taught Agentic Long Context Understanding

ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

How Do LLMs Perform Two-Hop Reasoning in Context?

ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails

MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections

ReLearn: Unlearning via Learning for Large Language Models

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL

A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems

Non-Markovian Discrete Diffusion with Causal Language Models

CoSER: Coordinating LLM-Based Persona Simulation of Established Roles

TransMLA: Migrating GQA Models to MLA with Full DeepSeek Compatibility and Speedup

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control

Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs

Advancing Reasoning in Large Language Models: Promising Methods and Approaches

Path Planning for Masked Diffusion Model Sampling

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models

Robust LLM Alignment via Distributionally Robust Direct Preference Optimization

Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations

Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations

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

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective

A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment

Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario

Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?

Redundancy Principles for MLLMs Benchmarks

K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor

An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring

Diffusion Adversarial Post-Training for One-Step Video Generation

Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts

PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning

Revisiting In-Context Learning with Long Context Language Models

Energy and polarization based on-line interference mitigation in radio interferometry

How to Synthesize Text Data without Model Collapse?

Preference Adaptive and Sequential Text-to-Image Generation

Sample Efficient Robot Learning in Supervised Effect Prediction Tasks

Federated Continual Graph Learning

Democratizing Differential Privacy: A Participatory AI Framework for Public Decision-Making

Created by

Haebom

저자

Wenjun Yang, Eyhab Al-Masri

개요

본 논문은 공공 부문 응용 프로그램에서 차등적 프라이버시 AI 시스템의 참여적 설계를 가능하게 하는 대화형 인터페이스 시스템을 소개합니다. 수학적 프라이버시 보장과 민주적 책임성 간의 균형을 맞추는 과제를 해결하기 위해, (1) 시민의 선호도를 차등적 프라이버시(DP) 매개변수와 정렬하기 위한 TOPSIS 다기준 의사결정 분석을 활용하는 적응형 ε-선택 프로토콜, (2) 실시간 평균 절대 오차(MAE) 시각화와 GPT-4 기반 영향 분석을 특징으로 하는 설명 가능한 잡음 주입 프레임워크, (3) 진화하는 규제 제약 조건에 따라 프라이버시 예산을 동적으로 조정하는 통합 법적 준수 메커니즘이라는 세 가지 주요 기여를 제안합니다. 본 연구 결과는 대화형 인터페이스가 공공 부문 거버넌스에서 프라이버시 보호 AI가 수학적으로 강력하고 민주적으로 책임감 있도록 알고리즘 프라이버시 메커니즘에 대한 공공 참여를 어떻게 향상시킬 수 있는지 보여줌으로써 참여적 AI 관행을 발전시킵니다.

시사점, 한계점

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시사점:

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공공 부문 AI 시스템의 차등적 프라이버시 설계에 대한 참여적 접근 방식을 제시합니다.

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시민 선호도와 DP 매개변수 간의 조정을 위한 새로운 적응형 ε-선택 프로토콜을 제공합니다.

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설명 가능한 잡음 주입 프레임워크를 통해 AI 시스템의 프라이버시 보호 메커니즘에 대한 투명성을 높입니다.

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진화하는 규제 환경에 적응하는 동적인 법적 준수 메커니즘을 통합합니다.

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대화형 인터페이스를 통해 시민 참여를 증진시켜 민주적 책임성을 강화합니다.

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한계점:

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제안된 시스템의 실제 공공 부문 적용에 대한 광범위한 평가가 필요합니다.

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TOPSIS와 GPT-4와 같은 특정 기술에 대한 의존성이 시스템의 일반화 가능성을 제한할 수 있습니다.

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다양한 이해 관계자의 요구 사항과 선호도를 충족하는 프라이버시-책임성 균형을 달성하는 것이 어려울 수 있습니다.

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진화하는 규제 환경에 대한 적응력을 유지하는 것이 어려울 수 있습니다.

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