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.

Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism

EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework

Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation

HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation

Potemkin Understanding in Large Language Models

OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track

Arabic Dialect Classification using RNNs, Transformers, and Large Language Models: A Comparative Analysis

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education

GLIMPSE: Gradient-Layer Importance Mapping for Prompted Visual Saliency Explanation for Generative LVLMs

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey

Generalizing vision-language models to novel domains: A comprehensive survey

Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance

AI-Generated Song Detection via Lyrics Transcripts

KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation

Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning

Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion

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

Value-Free Policy Optimization via Reward Partitioning

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption

Enabling Precise Topic Alignment in Large Language Models Via Sparse Autoencoders

Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization

CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following

StepProof: Step-by-step verification of natural language mathematical proofs

Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed

VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code

Multi Layered Autonomy and AI Ecologies in Robotic Art Installations

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison

Quantum computing and artificial intelligence: status and perspectives

Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization

A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems

Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field

From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data

Error Optimization: Overcoming Exponential Signal Decay in Deep Predictive Coding Networks

TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks

Super-Resolution Generative Adversarial Networks based Video Enhancement

Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix

INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models

SConU: Selective Conformal Uncertainty in Large Language Models

MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation

Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization

Achieving binary weight and activation for LLMs using Post-Training Quantization

A Consequentialist Critique of Binary Classification Evaluation Practices

Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models

Test-Time Reasoning Through Visual Human Preferences with VLMs and Soft Rewards

FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments

Automating Adjudication of Cardiovascular Events Using Large Language Models

ATTENTION2D: Communication Efficient Distributed Self-Attention Mechanism

Visual Position Prompt for MLLM based Visual Grounding

Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding

Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI

Characterizing GPU Resilience and Impact on AI/HPC Systems

Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning

Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction

The Problem of the Priors, or Posteriors?

Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding

Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy

What can large language models do for sustainable food?

Enough Coin Flips Can Make LLMs Act Bayesian

How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization

Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning

HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning

LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention

No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data

Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning

A general language model for peptide identification

Cluster and Predict Latent Patches for Improved Masked Image Modeling

Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks

KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy

Mechanistic Interpretability of Emotion Inference in Large Language Models

Multimodal Medical Code Tokenizer

Time to Rethink AI for Combinatorial Optimization: Classical Algorithms Remain Tough to Match

Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models

Environment-Driven Online LiDAR-Camera Extrinsic Calibration

Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation

DReSS: Data-driven Regularized Structured Streamlining for Large Language Models

Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection

Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models

DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection

An Investigation into Seasonal Variations in Energy Forecasting for Student Residences

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

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

AlignGuard: Scalable Safety Alignment for Text-to-Image Generation

A Library for Learning Neural Operators

ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality

Pretrained Reversible Generation as Unsupervised Visual Representation Learning

FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait

SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?

Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters

Foundation Models for Wearable Movement Data in Mental Health Research

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements

ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality

Created by

Haebom

저자

Yefei He, Feng Chen, Yuanyu He, Shaoxuan He, Hong Zhou, Kaipeng Zhang, Bohan Zhuang

개요

본 논문은 자기회귀(AR) 방식의 이미지 생성 속도를 높이기 위한 훈련이 필요 없는 플러그 앤 플레이 병렬 디코딩 프레임워크인 ZipAR을 제안합니다. 이미지는 국소 구조를 가지고 있으며 공간적으로 먼 영역은 상호 의존성이 최소화된다는 점에 착안하여 개발되었습니다. 부분적으로 디코딩된 시각 토큰 집합이 주어지면, 기존의 행 차원에서의 다음 토큰 예측 방식 외에, 열 차원에서 공간적으로 인접한 영역에 해당하는 토큰들을 병렬로 디코딩하여 "다음 집합 예측" 패러다임을 가능하게 합니다. 단일 순전파에서 여러 토큰을 동시에 디코딩함으로써 이미지 생성에 필요한 순전파 횟수를 크게 줄여 생성 효율을 상당히 향상시킵니다. 실험 결과, ZipAR은 추가적인 재훈련 없이 Emu3-Gen 모델에서 모델 순전파 횟수를 최대 91%까지 줄일 수 있음을 보여줍니다. 코드는 https://github.com/ThisisBillhe/ZipAR 에서 이용 가능합니다.

GitHub - ThisisBillhe/ZipAR: [ICML 2025] This is the official PyTorch implementation of "ZipAR: Accelerating Auto-regressive Image Generation through Spatial Locality"

[ICML 2025] This is the official PyTorch implementation of "ZipAR: Accelerating Auto-regressive Image Generation through Spatial Locality" - ThisisBillhe/ZipAR

시사점, 한계점

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시사점:

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자기회귀 방식 이미지 생성 모델의 속도를 획기적으로 향상시킬 수 있는 새로운 병렬 디코딩 프레임워크 제시

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추가적인 훈련 없이 기존 모델에 적용 가능한 플러그 앤 플레이 방식 채택

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이미지의 국소 구조 특성을 효과적으로 활용하여 계산 효율성 증대

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Emu3-Gen 모델에서 최대 91%의 순전파 횟수 감소 효과 확인

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한계점:

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제시된 방법이 모든 AR 이미지 생성 모델에 동일한 효과를 보일지는 추가적인 연구가 필요

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특정 모델(Emu3-Gen)에 대한 실험 결과만 제시되어 일반화 가능성에 대한 검증 부족

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열 차원의 병렬 디코딩이 이미지의 모든 종류에 효율적인지에 대한 추가적인 분석 필요

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