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.

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

Horus: A Protocol for Trustless Delegation Under Uncertainty

Reducing Variability of Multiple Instance Learning Methods for Digital Pathology

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

Towards Safety Evaluations of Theory of Mind in Large Language Models

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Pre-training Large Memory Language Models with Internal and External Knowledge

Towards Universal Semantics With Large Language Models

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Enhancing Robustness to Missing Modalities through Clustered Federated Learning

Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?

Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

SFO: Piloting VLM Feedback for Offline RL

Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks

KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis

Distribution Matching for Self-Supervised Transfer Learning

A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior

SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

A Framework for Mining Collectively-Behaving Bots in MMORPGs

Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

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

There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models

Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication

Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization

NegMerge: Sign-Consensual Weight Merging for Machine Unlearning

Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems

Backdooring Bias (B^2) into Stable Diffusion Models

Embodied Instruction Following in Unknown Environments

Improving Consistency Models with Generator-Augmented Flows

OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors

Divergent Creativity in Humans and Large Language Models

SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems

Squat: Quant Small Language Models on the Edge

Dataset Distillation via the Wasserstein Metric

The Boolean Solution Problem from the Perspective of Predicate Logic -- Extended Version

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

World-aware Planning Narratives Enhance Large Vision-Language Model Planner

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Adapting Probabilistic Risk Assessment for AI

Beating Transformers using Synthetic Cognition

MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow

Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

MILP-SAT-GNN: Yet Another Neural SAT Solver

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

How Do Vision-Language Models Process Conflicting Information Across Modalities?

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

Probing Evaluation Awareness of Language Models

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

BranchNet: A Neuro-Symbolic Learning Framework for Structured Multi-Class Classification

GPU-based complete search for nonlinear minimization subject to bounds

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

Discrete Diffusion in Large Language and Multimodal Models: A Survey

Created by

Haebom

저자

Runpeng Yu, Qi Li, Xinchao Wang

개요

본 논문은 이산 확산 언어 모델(dLLM)과 이산 확산 다중 모달 언어 모델(dMLLM)에 대한 체계적인 조사를 제공합니다. 자동회귀(AR) 모델과 달리, dLLM과 dMLLM은 전체 어텐션과 잡음 제거 기반 생성 전략을 사용하는 다중 토큰 병렬 디코딩 패러다임을 채택합니다. 이 패러다임은 자연스럽게 병렬 생성, 세분화된 출력 제어 및 동적이고 응답 인식적인 인식을 가능하게 합니다. 이러한 기능들은 이전에는 AR 모델로 달성하기 어려웠습니다. 최근 많은 산업 규모의 독점 d(M)LLM과 다수의 오픈소스 학술 d(M)LLM이 자동회귀 모델과 비슷한 성능을 보이면서 추론 속도를 최대 10배까지 향상시켰습니다. dLLM과 dMLLM의 발전은 자동회귀 LLMs 및 MLLMs의 발전과 이산 확산의 수학적 모델의 발전이라는 두 가지 영역의 진보에 크게 힘입었습니다. 본 논문에서는 dLLM 및 dMLLM 분야의 연구에 대한 포괄적인 개요를 제시하며, dLLM 및 dMLLM의 역사적 발전을 추적하고, 기본 수학적 프레임워크를 공식화하고, 대표적인 모델을 분류합니다. 또한 훈련 및 추론을 위한 주요 기술을 분석하고, 언어, 비전-언어 및 생물학적 영역에서 나타나는 응용 프로그램을 요약합니다. 마지막으로 미래 연구 및 배포 방향에 대해 논의합니다.

시사점, 한계점

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시사점:

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dLLM과 dMLLM이 AR 모델에 비해 병렬 생성, 세분화된 출력 제어, 동적 응답 인식 등의 장점을 제공하며, 최대 10배의 추론 속도 향상을 달성할 수 있음을 보여줍니다.

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자동회귀 모델의 발전과 이산 확산 모델의 발전이 dLLM과 dMLLM의 발전을 촉진했음을 밝힙니다.

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다양한 응용 분야(언어, 비전-언어, 생물학)에서 dLLM과 dMLLM의 활용 가능성을 제시합니다.

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dLLM과 dMLLM 분야의 연구 동향 및 미래 방향에 대한 통찰력을 제공합니다.

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한계점:

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본 논문은 주로 기존 연구에 대한 조사에 집중하며, 새로운 모델이나 실험 결과를 제시하지는 않습니다.

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dLLM과 dMLLM의 한계점이나 단점에 대한 심층적인 논의가 부족할 수 있습니다. (예: 메모리 사용량, 특정 작업에 대한 성능 제약 등)

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제공된 GitHub 링크의 논문 모음집의 질과 완성도에 대한 평가가 필요합니다.

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