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.

Surgical Agent Orchestration Platform for Voice-directed Patient Data Interaction

FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models

Learning Quantized Continuous Controllers for Integer Hardware

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

TiS-TSL: Image-Label Supervised Surgical Video Stereo Matching via Time-Switchable Teacher-Student Learning

Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling

Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

A Remarkably Efficient Paradigm to Multimodal Large Language Models for Sequential Recommendation

EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion

Enhancing Diffusion Model Guidance through Calibration and Regularization

DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning

Report from Workshop on Dialogue alongside Artificial Intelligence

Token Is All You Need: Cognitive Planning through Belief-Intent Co-Evolution

Selective Diabetic Retinopathy Screening with Accuracy-Weighted Deep Ensembles and Entropy-Guided Abstention

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Alternative Fairness and Accuracy Optimization in Criminal Justice

I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging

OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms

Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices

How to Evaluate Speech Translation with Source-Aware Neural MT Metrics

GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding

Normalization in Attention Dynamics

Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options

Language over Content: Tracing Cultural Understanding in Multilingual Large Language Models

TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding

TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration

Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs

Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions

Evolutionary Profiles for Protein Fitness Prediction

Epistemic Diversity and Knowledge Collapse in Large Language Models

MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images

Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering

Decoding Latent Attack Surfaces in LLMs: Prompt Injection via HTML in Web Summarization

Towards Methane Detection Onboard Satellites

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis

Beyond Algorethics: Addressing the Ethical and Anthropological Challenges of AI Recommender Systems

Imbalance in Balance: Online Concept Balancing in Generation Models

ReCode: Updating Code API Knowledge with Reinforcement Learning

Rethinking Losses for Diffusion Bridge Samplers

Zeroth-Order Optimization Finds Flat Minima

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection

A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

FB-RAG: Improving RAG with Forward and Backward Lookup

FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models

Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning

RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

FALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Alignment

How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference

FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors

On the generalization of language models from in-context learning and finetuning: a controlled study

Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation

A Multimodal Recaptioning Framework to Account for Perceptual Diversity Across Languages in Vision-Language Modeling

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

COPA: Comparing the incomparable in multi-objective model evaluation

CLEV: LLM-Based Evaluation Through Lightweight Efficient Voting for Free-Form Question-Answering

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples

Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance

Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees

MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications

On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses

Generalizing Weisfeiler-Lehman Kernels to Subgraphs

SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins

GeMID: Generalizable Models for IoT Device Identification

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

Selection of LLM Fine-Tuning Data based on Orthogonal Rules

Benchmarking Domain Generalization Algorithms in Computational Pathology

Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA

Identifying treatment response subgroups in observational time-to-event data

Informed Correctors for Discrete Diffusion Models

Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions

Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships

Spikingformer: A Key Foundation Model for Spiking Neural Networks

DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas

Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture

A Theoretical Analysis of Detecting Large Model-Generated Time Series

Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads

When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks

ScRPO: From Errors to Insights

Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences

Glia: A Human-Inspired AI for Automated Systems Design and Optimization

JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization

Created by

Haebom

저자

Kai Liu, Wei Li, Lai Chen, Shengqiong Wu, Yanhao Zheng, Jiayi Ji, Fan Zhou, Rongxin Jiang, Jiebo Luo, Hao Fei, Tat-Seng Chua

개요

JavisDiT은 동기화된 오디오-비디오 생성(JAVG)을 위한 새로운 조인트 오디오-비디오 확산 트랜스포머입니다. Diffusion Transformer(DiT) 아키텍처를 기반으로 하며, 열린 끝 사용자 프롬프트로부터 고품질 오디오 및 비디오 콘텐츠를 동시에 생성할 수 있습니다. 최적의 동기화를 보장하기 위해 계층적 공간-시간 동기화 사전(HiST-Sypo) 추정기를 통해 미세한 공간-시간 정렬 메커니즘을 도입했습니다. 이 모듈은 전역 및 미세한 공간-시간 사전을 추출하여 시각적 및 청각적 구성 요소 간의 동기화를 안내합니다. 또한 다양한 장면과 복잡한 실제 시나리오를 포괄하는 10,140개의 고품질 텍스트 캡션이 포함된 사운드 비디오로 구성된 새로운 벤치마크인 JavisBench를 제안합니다. 더 나아가 실제 복잡한 콘텐츠에서 생성된 오디오-비디오 쌍 간의 동기화를 평가하기 위한 강력한 지표를 특별히 고안했습니다. 실험 결과는 JavisDiT가 고품질 생성과 정밀한 동기화를 모두 보장함으로써 기존 방법보다 훨씬 뛰어나며 JAVG 작업에 대한 새로운 표준을 설정함을 보여줍니다. 코드, 모델 및 데이터셋은 https://javisdit.github.io/ 에서 공개적으로 제공될 것입니다.

JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization

javisdit.github.io

시사점, 한계점

•

시사점:

◦

고품질 오디오 및 비디오 콘텐츠의 동시 생성을 가능하게 하는 새로운 모델 JavisDiT 제시.

◦

HiST-Sypo 추정기를 통한 효과적인 오디오-비디오 동기화 메커니즘 제안.

◦

JAVG 작업을 위한 새로운 벤치마크 JavisBench 및 동기화 평가 지표 제시.

◦

기존 방법보다 우수한 성능을 보이는 JavisDiT의 실험 결과 제시.

◦

코드, 모델 및 데이터셋 공개를 통한 연구의 재현성 및 확장성 증진.

•

한계점:

◦

논문에서는 JavisDiT의 한계점에 대한 명시적인 언급이 없습니다. 추가적인 분석 및 실험을 통해 한계점을 밝힐 필요가 있습니다. (예: 특정 유형의 오디오-비디오 콘텐츠에 대한 성능 저하, 계산 비용, 훈련 데이터 편향 등)

Made with Slashpage