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.

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts

Preacher: Paper-to-Video Agentic System

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis

Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation

Biased AI improves human decision-making but reduces trust

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving

To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA

ASPD: Unlocking Adaptive Serial-Parallel Decoding by Exploring Intrinsic Parallelism in LLMs

BiasGym: Fantastic LLM Biases and How to Find (and Remove) Them

Yan: Foundational Interactive Video Generation

M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction

LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework

The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs

On Understanding of the Dynamics of Model Capacity in Continual Learning

WeChat-YATT: A Simple, Scalable and Balanced RLHF Trainer

Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback

Hardness-Aware Dynamic Curriculum Learning for Robust Multimodal Emotion Recognition with Missing Modalities

Echoes of Automation: The Increasing Use of LLMs in Newsmaking

SIFThinker: Spatially-Aware Image Focus for Visual Reasoning

Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle

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

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioning

Self-Questioning Language Models

Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base

Class-Proportional Coreset Selection for Difficulty-Separable Data

Warehouse Spatial Question Answering with LLM Agent

CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks

AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration

EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision

GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study

Discrepancy-Aware Graph Mask Auto-Encoder

Semantic Structure-Aware Generative Attacks for Enhanced Adversarial Transferability

Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Images

PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods

Data Pruning by Information Maximization

CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting

Security Concerns for Large Language Models: A Survey

Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing

Unraveling the iterative CHAD

FreeKV: Boosting KV Cache Retrieval for Efficient LLM Inference

LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation

Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free

Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints

Vision Transformers in Precision Agriculture: A Comprehensive Survey

Goal-Oriented Time-Series Forecasting: Foundation Framework Design

CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting

FinSage: A Multi-aspect RAG System for Financial Filings Question Answering

GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes

Hyperflux: Pruning Reveals the Importance of Weights

ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning

UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving

VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models

BitDecoding: Unlocking Tensor Cores for Long-Context LLMs with Low-Bit KV Cache

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

Continual Learning for Multiple Modalities

Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)

LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint

Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks

Measuring Diversity in Synthetic Datasets

Delayed Feedback Modeling with Influence Functions

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization

Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations

A Lightweight Transformer with Phase-Only Cross-Attention for Illumination-Invariant Biometric Authentication

Understanding Transformer-based Vision Models through Inversion

INSIGHT: Explainable Weakly-Supervised Medical Image Analysis

Visual SLAMMOT Considering Multiple Motion Models

A Training-Free Approach for Music Style Transfer with Latent Diffusion Models

Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need

DiRW: Path-Aware Digraph Learning for Heterophily

Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity

Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience

Neural Networks Generalize on Low Complexity Data

Knowledge-based Consistency Testing of Large Language Models

Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning

An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach

Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions

Mathematical Computation and Reasoning Errors by Large Language Models

OpenCUA: Open Foundations for Computer-Use Agents

Compass-Thinker-7B Technical Report

TextQuests: How Good are LLMs at Text-Based Video Games?

On the Definition of Intelligence

Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making

LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization

FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory

MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models

A Random-Key Optimizer for Combinatorial Optimization

Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity

Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval

INSIGHT: Explainable Weakly-Supervised Medical Image Analysis

Created by

Haebom

저자

Wenbo Zhang, Junyu Chen, Christopher Kanan

개요

본 논문은 대용량 의료 영상(3D CT 스캔 및 전자현미경 이미지) 분석을 위한 새로운 약지도 학습 기반 어그리게이터인 INSIGHT를 제안합니다. 기존 방법들의 한계점인 작지만 중요한 세부 정보의 국소화 실패 및 사후 시각화 기법 의존성을 해결하기 위해, INSIGHT는 히트맵 생성을 유도적 편향으로 통합합니다. 미리 학습된 특징 맵으로부터 시작하여, 작은 합성곱 커널을 사용하는 탐지 모듈과 넓은 수용 영역을 가진 컨텍스트 모듈을 활용하여 미세한 세부 정보를 포착하고 국소적인 오탐을 억제합니다. 결과적으로 생성된 내부 히트맵은 진단적으로 중요한 영역을 강조합니다. CT 및 WSI 벤치마크에서 INSIGHT는 최첨단 분류 성능과 높은 약지도 학습 기반 의미론적 분할 성능을 달성합니다.

시사점, 한계점

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시사점:

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대용량 의료 영상에서 작고 중요한 세부 정보의 정확한 국소화 가능

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사후 시각화 기법 없이도 진단적으로 중요한 영역을 효과적으로 강조

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CT 및 WSI 데이터셋에서 최첨단 성능 달성

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약지도 학습 기반 의미론적 분할에서 우수한 성능

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공개된 코드와 웹사이트를 통해 재현성 및 접근성 확보

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한계점:

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약지도 학습에 의존하므로, 정확한 어노테이션이 부족할 경우 성능 저하 가능성 존재

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특정 유형의 의료 영상에 대한 성능이 다른 유형에 비해 제한적일 수 있음

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다양한 의료 영상 유형에 대한 일반화 성능 평가 필요

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