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.

Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons

MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection

Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving

FOLIAGE: Towards Physical Intelligence World Models Via Unbounded Surface Evolution

UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds

DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving

Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models

Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction

Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy

ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration

ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems

Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon

Inclusive, Differentially Private Federated Learning for Clinical Data

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)

Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

In-context Language Learning for Endangered Languages in Speech Recognition

Rethinking Text-based Protein Understanding: Retrieval or LLM?

DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning

Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities

STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution

FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models

Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation

The Role of Diversity in In-Context Learning for Large Language Models

MetaGen Blended RAG: Unlocking Zero-Shot Precision for Specialized Domain Question-Answering

Bottlenecked Transformers: Periodic KV Cache Abstraction for Generalised Reasoning

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

SUS backprop: linear backpropagation algorithm for long inputs in transformers

Optimizing Anytime Reasoning via Budget Relative Policy Optimization

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping

MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge

AGENTFUZZER: Generic Black-Box Fuzzing for Indirect Prompt Injection against LLM Agents

Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

Goal-Oriented Time-Series Forecasting: Foundation Framework Design

Biased by Design: Leveraging Inherent AI Biases to Enhance Critical Thinking of News Readers

Hearing Anywhere in Any Environment

NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark

LLM Social Simulations Are a Promising Research Method

Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions

COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing

On The Sample Complexity Bounds In Bilevel Reinforcement Learning

TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults

Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment

Knowledge Retention for Continual Model-Based Reinforcement Learning

When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits

ATLaS: Agent Tuning via Learning Critical Steps

Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable

From Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice

SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference

Contrastive Visual Data Augmentation

SNaRe: Domain-aware Data Generation for Low-Resource Event Detection

AnyTop: Character Animation Diffusion with Any Topology

Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization

GoRA: Gradient-driven Adaptive Low Rank Adaptation

Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models

Bandit Multiclass List Classification

CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection

Can Large Language Models Understand Intermediate Representations in Compilers?

Contrastive Representation Distillation via Multi-Scale Feature Decoupling

Blackout DIFUSCO

Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs

Analytical Lyapunov Function Discovery: An RL-based Generative Approach

Explainability in Practice: A Survey of Explainable NLP Across Various Domains

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

Spectro-Riemannian Graph Neural Networks

The Lessons of Developing Process Reward Models in Mathematical Reasoning

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

Hybrid deep convolution model for lung cancer detection with transfer learning

Uncovering Memorization Effect in the Presence of Spurious Correlations

OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning

An analytic theory of creativity in convolutional diffusion models

HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing

From Intention To Implementation: Automating Biomedical Research via LLMs

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost

Functional relevance based on the continuous Shapley value

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective

Focus On This, Not That! Steering LLMs with Adaptive Feature Specification

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks

David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences

Beyond Position: the emergence of wavelet-like properties in Transformers

The Disparate Benefits of Deep Ensembles

Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing

Evaluating Morphological Compositional Generalization in Large Language Models

Stein Variational Evolution Strategies

Biased AI can Influence Political Decision-Making

MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

Created by

Haebom

저자

Maya Varma, Ashwin Kumar, Rogier van der Sluijs, Sophie Ostmeier, Louis Blankemeier, Pierre Chambon, Christian Bluethgen, Jip Prince, Curtis Langlotz, Akshay Chaudhari

개요

본 논문은 의료 영상의 고해상도와 광시야로 인한 딥러닝 모델 학습의 높은 계산 비용 문제를 해결하기 위해, 의학적으로 중요한 특징을 유지하면서 영상 크기를 줄이는 방법을 제시합니다. MedVAE라는 6개의 대규모 2D 및 3D 오토인코더를 도입하여 의료 영상을 축소된 잠재 표현으로 인코딩하고, 다시 고해상도 영상으로 디코딩하는 기술을 개발했습니다. 1,052,730개의 의료 영상을 사용하여 2단계 학습 방식으로 MedVAE를 학습시켰으며, 20개의 의료 영상 데이터셋에서 다양한 작업을 통해 고해상도 영상 대신 MedVAE 잠재 표현을 사용하여 downstream 모델을 학습시키면 처리량이 최대 70배까지 향상될 수 있으며, 동시에 임상적으로 중요한 특징이 유지됨을 보여줍니다. 또한 MedVAE가 잠재 표현을 고해상도 영상으로 높은 충실도로 디코딩할 수 있음을 증명했습니다. 결론적으로, 대규모 일반화 가능한 오토인코더가 의료 분야의 중요한 효율성 문제를 해결하는 데 도움이 될 수 있음을 보여줍니다. 코드는 https://github.com/StanfordMIMI/MedVAE 에서 확인할 수 있습니다.

GitHub - StanfordMIMI/MedVAE: [MIDL 2025] Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

[MIDL 2025] Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders - StanfordMIMI/MedVAE

시사점, 한계점

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시사점:

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의료 영상의 고해상도 문제로 인한 계산 비용을 획기적으로 줄일 수 있는 효율적인 방법 제시. (최대 70배 처리량 향상)

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임상적으로 중요한 특징을 유지하면서 영상 크기를 줄이는 기술 개발.

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대규모 의료 영상 데이터셋을 활용한 일반화 가능한 오토인코더 모델 개발.

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고해상도 영상으로의 높은 충실도 재구성 가능성 증명.

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한계점:

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본 논문에서 제시된 20개의 의료 영상 데이터셋의 종류 및 특징에 대한 자세한 설명 부족.

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다양한 의료 영상 modality에 대한 일반화 성능에 대한 추가적인 검증 필요.

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잠재 공간의 해석 가능성에 대한 추가적인 연구 필요.

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특정 의료 영상 분석 작업에 대한 최적의 잠재 표현 차원 및 모델 구조에 대한 추가 연구 필요.

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