Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
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

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning

Created by

Haebom

저자

Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna

개요

본 논문에서는 개인정보 보호를 준수하는 분산 머신러닝 접근 방식인 개인정보 보호 분산 학습(PPFL)에서 데이터 준비성(DR)을 보장하기 위한 새로운 프레임워크인 CADRE(Customizable Assurance of Data REadiness)를 제시합니다. CADRE는 사용자가 특정 FL 작업에 맞게 사용자 정의 DR 표준, 메트릭, 규칙 및 해결책을 정의할 수 있도록 합니다. 사용자 정의 메트릭, 규칙 및 해결책을 기반으로 포괄적인 DR 보고서를 생성하여 데이터 세트가 FL에 최적으로 준비되도록 보장하면서 개인정보를 보호합니다. 6개의 다양한 데이터 세트에서 7가지의 다른 DR 문제를 해결하는 실험을 통해 프레임워크의 다양성과 효과를 보여줍니다. 이는 데이터 품질, 개인 정보 보호 및 공정성을 포함한 다양한 측면에서 DR을 보장합니다. 이 접근 방식은 FL 모델의 성능과 안정성을 향상시키고 교육 단계 전에 데이터 관련 문제를 식별하고 해결함으로써 귀중한 리소스를 활용합니다.

시사점, 한계점

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시사점:

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사용자 정의 가능한 데이터 준비성 표준, 메트릭, 규칙 및 해결책을 제공하여 다양한 FL 작업에 적용 가능한 유연한 프레임워크를 제공합니다.

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개인정보 보호를 유지하면서 데이터 품질, 개인 정보 보호 및 공정성을 포함한 다양한 측면에서 데이터 준비성을 보장합니다.

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FL 모델의 성능과 안정성을 향상시키고, 데이터 관련 문제를 사전에 해결함으로써 리소스 낭비를 줄입니다.

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포괄적인 DR 보고서를 통해 데이터 준비성 상태를 명확하게 파악할 수 있습니다.

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한계점:

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제시된 프레임워크의 성능 및 효율성은 사용자 정의 DR 표준, 메트릭, 규칙 및 해결책의 질에 따라 크게 영향을 받을 수 있습니다.

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다양한 데이터 유형 및 FL 작업에 대한 일반화 가능성에 대한 추가적인 연구가 필요합니다.

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실제 환경에서의 적용 가능성 및 확장성에 대한 검증이 필요합니다.

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특정 DR 문제에 대한 최적의 해결책을 찾는 과정이 복잡하고 시간이 오래 걸릴 수 있습니다.

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