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Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
Dense Video Understanding with Gated Residual Tokenization
Machines are more productive than humans until they aren't, and vice versa
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
Exploring Data and Parameter Efficient Strategies for Arabic Dialect Identifications
The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study
A Graph-Based Approach to Alert Contextualisation in Security Operations Centres
FunAudio-ASR Technical Report
Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering
Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
Pluralistic Alignment for Healthcare: A Role-Driven Framework
ALIGNS: Unlocking nomological networks in psychological measurement through a large language model
A Survey of Reinforcement Learning for Large Reasoning Models
Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network
Reconstruction Alignment Improves Unified Multimodal Models
Moment- and Power-Spectrum-Based Gaussianity Regularization for Text-to-Image Models
FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes
Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance
Exploit Tool Invocation Prompt for Tool Behavior Hijacking in LLM-Based Agentic System
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting
Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification
Deep Learning-Driven Multimodal Detection and Movement Analysis of Objects in Culinary
Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
MovieCORE: COgnitive REasoning in Movies
ASE: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Neural Logic Networks for Interpretable Classification
Roll Your Eyes: Gaze Redirection via Explicit 3D Eyeball Rotation
Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories
Deciding how to respond: A deliberative framework to guide policymaker responses to AI systems
SCORPION: Addressing Scanner-Induced Variability in Histopathology
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimation
FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
EnCoBo: Energy-Guided Concept Bottlenecks for Interpretable Generation
T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
MedVAL: Toward Expert-Level Medical Text Validation with Language Models
Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems
"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning
Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion
Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data
Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis
PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
Preference Isolation Forest for Structure-based Anomaly Detection
Trustless Autonomy: Understanding Motivations, Benefits, and Governance Dilemmas in Self-Sovereign Decentralized AI Agents
GRADA: Graph-based Reranking against Adversarial Documents Attack
Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
Direct Video-Based Spatiotemporal Deep Learning for Cattle Lameness Detection
Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading
Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping
Predicting Multi-Agent Specialization via Task Parallelizability
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection
Superpose Task-specific Features for Model Merging
Examining False Positives under Inference Scaling for Mathematical Reasoning
SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation
Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland
Reconstruction of Differentially Private Text Sanitization via Large Language Models
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
EXPLOR: Extrapolatory Pseudo-Label Matching for Out-of-distribution Uncertainty Based Rejection
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
Heterogeneous Directed Hypergraph Neural Network over Abtract syntax tree (AST) for Code Classification
The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs
Human + AI for Accelerating Ad Localization Evaluation
Statistical Methods in Generative AI
InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning
DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge
Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
Automatic Mapping of AutomationML Files to Ontologies for Graph Queries and Validation
Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
FlowRL: Matching Reward Distributions for LLM Reasoning
Orion: Fuzzing Workflow Automation
TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning
SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting
Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model
Leveraging Geometric Visual Illusions as Perceptual Inductive Biases for Vision Models
Exploring How Audio Effects Alter Emotion with Foundation Models
WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model via Training-Free Guidance
The mechanization of science illustrated by the Lean formalization of the multi-graded Proj construction
Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
TextMine: LLM-Powered Knowledge Extraction for Humanitarian Mine Action
Listening, Imagining \& Refining: A Heuristic Optimized ASR Correction Framework with LLMs
Communication Efficient Split Learning of ViTs with Attention-based Double Compression
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SCORPION: Addressing Scanner-Induced Variability in Histopathology
Created by
Haebom
作者
Jeongun Ryu, Heon Song, Seungeun Lee, Soo Ick Cho, Jiwon Shin, Kyunghyun Paeng, S ergio Pereira
概要
本論文は、計算病理学におけるさまざまなドメインにわたって信頼できるモデル性能を保証する際のデジタルスキャナの違いによるWhole-Slide Images（WSI）のボラティリティの問題について説明します。特に、機関や病院によってスキャン装置が異なる可能性がある実際の環境でモデルがスキャナーに依存しないようにするスキャナー一般化の重要性を強調します。既存の研究が主に標準ドメイン一般化設定に焦点を当てたのとは異なり、この論文はスキャナのボラティリティの下でモデルの信頼性を評価するために明示的に設計された新しいデータセットSCORPIONを提示します。 SCORPIONは、480個の組織サンプルを5個のスキャナーでスキャンし、2400個の空間的に整列したパッチを含み、スキャナー間のばらつきを分離し、組織構成の違いを制御しながらモデルの一貫性を厳密に評価することができます。さらに、拡張ベースのドメイン一般化技術と一貫性の損失を組み合わせて、スキャナ一般化の問題を明示的に解決する柔軟なフレームワークであるSimConsを提案します。実験の結果、SimCons は、課題固有のパフォーマンスを低下させることなく、さまざまなスキャナでモデルの一貫性を向上させることを示しています。 SCORPIONデータセットとSimConsフレームワークを公開することで、研究コミュニティにさまざまなスキャナでモデルの一貫性を評価および改善するための重要なリソースを提供し、信頼性テストのための新しい標準を提供します。
Takeaways、Limitations
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Takeaways：
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スキャナのボラティリティを考慮した計算病理学モデルの信頼性評価のための新しいデータセットSCORPIONとフレームワークSimConsの提示
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スキャナ一般化問題に効果的に対処するSimConsの性能検証
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計算病理学の分野におけるモデルの信頼性の向上と実際の適用性の向上に寄与
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さまざまなスキャナでモデルの一貫性を評価するための新しい標準提示。
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Limitations：
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SCORPIONデータセットのサンプル数とスキャナの種類の制限。
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SimConsの一般化性能が他の種類のスキャナや組織に対しても維持できることをさらに研究する必要があります。
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実際の臨床環境におけるSimConsの有効性の更なる検証の必要性
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