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Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

GRAM: A Generative Foundation Reward Model for Reward Generalization

VideoMAR: Autoregressive Video Generatio with Continuous Tokens

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Serving Large Language Models on Huawei CloudMatrix384

PLD: A Choice-Theoretic List-Wise Knowledge Distillation

TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy

Refactoring Codebases through Library Design

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Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

Multi-Task Reward Learning from Human Ratings

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers

Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning

Vision Transformers Don't Need Trained Registers

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LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients

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

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Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

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UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions

Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs

Influential Bandits: Pulling an Arm May Change the Environment

SCAM: A Real-World Typographic Robustness Evaluation for Multimodal Foundation Models

Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning

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A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do\~nana

KANITE: Kolmogorov-Arnold Networks for ITE estimation

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization

Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing

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EgoBlind: Towards Egocentric Visual Assistance for the Blind

PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data

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CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Perspective Transition of Large Language Models for Solving Subjective Tasks

Can LLMs Ask Good Questions?

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REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization

FLARE: Towards Universal Dataset Purification against Backdoor Attacks

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Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation

LL\"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes

The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers

Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs

Deep Graph Anomaly Detection: A Survey and New Perspectives

A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS

Style-Preserving Lip Sync via Audio-Aware Style Reference

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Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Informed Correctors for Discrete Diffusion Models

RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

A Systematic Survey of Natural Language Processing for the Greek Language

Predicting the Understandability of Computational Notebooks through Code Metrics Analysis

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HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

The NordDRG AI Benchmark for Large Language Models

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Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Entropy-based Exploration Conduction for Multi-step Reasoning

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Sekai: A Video Dataset towards World Exploration

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

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GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

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Understanding Generative AI Capabilities in Everyday Image Editing Tasks

Created by

Haebom

저자

Mohammad Reza Taesiri, Brandon Collins, Logan Bolton, Viet Dac Lai, Franck Dernoncourt, Trung Bui, Anh Totti Nguyen

개요

본 논문은 2013년부터 2025년까지 Reddit 커뮤니티의 8만 3천 건의 이미지 편집 요청과 30만 5천 건의 전문가 편집 결과를 분석하여 생성형 AI(GenAI) 기반 이미지 편집기의 개선 방향을 모색하는 연구이다. GPT-4o, Gemini-2.0-Flash, SeedEdit 등 최신 AI 편집기를 포함하여, 현재 AI 편집기가 사람의 요청을 충족하는 비율은 약 33%에 불과함을 밝혔다. 특히 AI는 정확한 편집이 필요한 창의성이 낮은 요청에 대해서는 성능이 저조하며, 사람이나 동물의 특징을 유지하는 데 어려움을 겪고 의도치 않은 수정을 자주 하는 것으로 나타났다. 반면, VLM 기반 평가 모델(예: o1)은 사람 심사자와는 다른 평가 기준을 가지고 있으며, AI 편집 결과를 사람 편집 결과보다 더 선호하는 경향을 보였다. 연구 데이터와 예시는 https://psrdataset.github.io 에서 확인 가능하다.

Understanding Generative AI Capabilities in Everyday Image Editing Tasks

Project page for the paper: Understanding Generative AI Capabilities in Everyday Image Editing Tasks

psrdataset.github.io

시사점, 한계점

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시사점:

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실제 이미지 편집 요청에 대한 대규모 데이터 분석을 통해 AI 이미지 편집기의 현황과 개선 방향을 제시하였다.

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AI 편집기의 강점과 약점을 명확히 밝힘으로써 향후 연구 개발의 초점을 제시하였다. (정확성이 요구되는 작업과 창의성이 요구되는 작업에 대한 차별적 성능 분석)

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사람과 AI 평가 기준의 차이를 보여줌으로써 AI 평가 모델 개발의 중요성을 시사하였다.

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한계점:

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Reddit 데이터의 편향성으로 인한 일반화의 어려움.

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VLM 평가 모델의 주관성 및 한계.

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AI 편집기의 성능 향상을 위한 구체적인 기술적 제안 부재.

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