Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges

TempOpt -- Unsupervised Alarm Relation Learning for Telecommunication Networks

A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization

Yan: Foundational Interactive Video Generation

MLLM-CBench:A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis

VGGSounder: Audio-Visual Evaluations for Foundation Models

Capabilities of GPT-5 on Multimodal Medical Reasoning

C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction

Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer

FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities

Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens

Generalizing Scaling Laws for Dense and Sparse Large Language Models

Memp: Exploring Agent Procedural Memory

InfoCausalQA:Can Models Perform Non-explicit Causal Reasoning Based on Infographic?

Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning

Request-Only Optimization for Recommendation Systems

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories

GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy

FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport

GTPO: Trajectory-Based Policy Optimization in Large Language Models

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

Estimating Worst-Case Frontier Risks of Open-Weight LLMs

LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection

DRWKV: Focusing on Object Edges for Low-Light Image Enhancement

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network

GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

WebArXiv: Evaluating Multimodal Agents on Time-Invariant arXiv Tasks

Audio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Grounding

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents

OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness

SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving

The Importance of Being Lazy: Scaling Limits of Continual Learning

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models

HVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment

Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning

Poison Once, Control Anywhere: Clean-Text Visual Backdoors in VLM-based Mobile Agents

MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark

Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning

Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

Exploring Scaling Laws for EHR Foundation Models

MapStory: Prototyping Editable Map Animations with LLM Agents

Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind

Halting Recurrent GNNs and the Graded $\mu$-Calculus

Deep Learning Warm Starts for Trajectory Optimization on the International Space Station

EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting

FedRecon: Missing Modality Reconstruction in Heterogeneous Distributed Environments

AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation

GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes

Mosaic: Composite Projection Pruning for Resource-efficient LLMs

CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization

FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention

The Illusory Normativity of Rights-Based AI Regulation

Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs

Simulating the Real World: A Unified Survey of Multimodal Generative Models

RIZE: Regularized Imitation Learning via Distributional Reinforcement Learning

One-shot Optimized Steering Vectors Mediate Safety-relevant Behaviors in LLMs

EvoP: Robust LLM Inference via Evolutionary Pruning

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions

GenAI Confessions: Black-box Membership Inference for Generative Image Models

Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions

Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction

SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks

Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

Learning Characteristics of Reverse Quaternion Neural Network

Depth-Guided Self-Supervised Human Keypoint Detection via Cross-Modal Distillation

Retrieval-Augmented Decision Transformer: External Memory for In-context RL

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion

Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience

Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models

Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort

CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks

Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions

SpectralEarth: Training Hyperspectral Foundation Models at Scale

Towards flexible perception with visual memory

Integrating Clinical Knowledge Graphs and Gradient-Based Neural Systems for Enhanced Melanoma Diagnosis via the 7-Point Checklist

LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data

Towards Black-Box Membership Inference Attack for Diffusion Models

Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs

From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap

Learning to Defer in Congested Systems: The AI-Human Interplay

LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning

Game-Theoretic Multiagent Reinforcement Learning

SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling

Aryabhata: An exam-focused language model for JEE Math

Rethinking Domain-Specific LLM Benchmark Construction: A Comprehensiveness-Compactness Approach

Large Language Models Do Not Simulate Human Psychology

LLM Robustness Leaderboard v1 --Technical report

One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

StepFun-Prover Preview: Let's Think and Verify Step by Step

MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines

CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics

Created by

Haebom

저자

Shravan Nayak, Mehar Bhatia, Xiaofeng Zhang, Verena Rieser, Lisa Anne Hendricks, Sjoerd van Steenkiste, Yash Goyal, Karolina Stanczak, Aishwarya Agrawal

개요

본 논문은 텍스트-이미지(T2I) 모델이 다양한 문화적 맥락을 정확하게 표현하는 능력에 대한 우려를 제기하며, 명시적 및 암시적 문화적 기대와 T2I 모델 및 평가 지표의 정합성을 체계적으로 정량화하는 최초의 연구를 제시합니다. 이를 위해 연구진은 10개국과 5개의 사회문화적 영역을 아우르는 새로운 벤치마크인 CulturalFrames를 도입했습니다. CulturalFrames는 983개의 프롬프트, 4개의 최첨단 T2I 모델이 생성한 3637개의 이미지, 그리고 1만 개가 넘는 상세한 인간 주석으로 구성됩니다. 연구 결과, 모델과 국가에 걸쳐 문화적 기대가 평균 44%의 비율로 충족되지 못하는 것으로 나타났습니다. 명시적 기대는 놀랍게도 평균 68%의 높은 비율로 충족되지 못했고, 암시적 기대 또한 평균 49%의 비율로 충족되지 못했습니다. 또한 기존 T2I 평가 지표는 내부 추론 방식에 관계없이 문화적 정합성에 대한 인간 판단과 상관성이 낮은 것으로 나타났습니다. 결론적으로 이 연구는 중요한 격차를 드러내고, 구체적인 테스트 환경을 제공하며, 글로벌 사용성을 개선하는 문화적으로 고려된 T2I 모델 및 지표 개발을 위한 실행 가능한 방향을 제시합니다.

시사점, 한계점

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시사점:

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T2I 모델의 문화적 편향성 문제를 정량적으로 측정하고 분석하는 새로운 벤치마크인 CulturalFrames를 제시.

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T2I 모델이 문화적 기대를 충족하지 못하는 비율이 높음을 밝힘 (명시적 68%, 암시적 49%, 전체 44%).

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기존 평가 지표가 문화적 정합성을 제대로 평가하지 못함을 보여줌.

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문화적으로 고려된 T2I 모델 및 평가 지표 개발의 필요성을 강조.

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한계점:

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CulturalFrames 벤치마크가 특정 국가 및 사회문화적 영역에 집중되어 있어 일반화에 제한이 있을 수 있음.

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인간 주석에 대한 주관성이 결과에 영향을 미칠 수 있음.

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다양한 T2I 모델을 다루었지만, 모든 모델을 포괄하지 못할 수 있음.

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암시적 문화적 기대의 해석이 모호할 수 있음.

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