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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。
Merge-of-Thought Distillation
OTESGN: Optimal Transport-Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis
MESH - Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning
AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs
Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis
Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference
MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining
Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards
Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting
On Synthesis of Timed Regular Expressions
TinyDef-DETR: A DETR-based Framework for Defect Detection in Transmission Lines from UAV Imagery
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
From Vision to Validation: A Theory- and Data-Driven Construction of a GCC-Specific AI Adoption Index
A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier
FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training
Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt
The Information Dynamics of Generative Diffusion
Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models
Group Expectation Policy Optimization for Heterogeneous Reinforcement Learning
Pretrained Conformers for Audio Fingerprinting and Retrieval
Towards Scalable Training for Handwritten Mathematical Expression Recognition
To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA
Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs
MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning
Villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models
New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants
Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Uncertainty Estimation by Human Perception versus Neural Models
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity
Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets
MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering
An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach
Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)
Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation
Entropy-Gated Branching for Efficient Test-Time Reasoning
SWI: Speaking with Intent in Large Language Models
Byzantine-Robust Federated Learning Using Generative Adversarial Networks
VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification
MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue
V-HOP: Visuo-Haptic 6D Object Pose Tracking
EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds
Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution
IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings
Rethinking Disentanglement under Dependent Factors of Variation
Discovering physical laws with parallel symbolic enumeration
Semantic Augmentation in Images using Language
Algorithmic Collusion by Large Language Models
A minimal coalition logic
Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints
Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference
CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning
TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms
Robix: A Unified Model for Robot Interaction, Reasoning and Planning
KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models
Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
Optimizing Length Compression in Large Reasoning Models
LLMs for sensory-motor control: Combining in-context and iterative learning
Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics
Simulating Human-like Daily Activities with Desire-driven Autonomy
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition
ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
Explaining Concept Drift through the Evolution of Group Counterfactuals
LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
ObjectReact: Learning Object-Relative Control for Visual Navigation
Fluent but Unfeeling: The Emotional Blind Spots of Language Models
Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification
An improved educational competition optimizer with multi-covariance learning operators for global optimization problems
Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization
Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
Explainable AI for Accelerated Microstructure Imaging: A SHAP-Guided Protocol on the Connectome 2.0 scanner
Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India
OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection
Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts
Resource-Efficient Glioma Segmentation on Sub-Saharan MRI
ENSI: Efficient Non-Interactive Secure Inference for Large Language Models
We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
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Explainable AI for Accelerated Microstructure Imaging: A SHAP-Guided Protocol on the Connectome 2.0 scanner
Created by
Haebom
作者
Quentin Uhl, Tommaso Pavan, Julianna Gerold, Kwok-Shing Chan, Yohan Jun, Shohei Fujita, Aneri Bhatt, Yixin Ma, Qiaochu Wang, Hong-Hsi Lee, Susie Y. Huang, Berkin Bilgic, Ileana Jelescu
概要
本研究は、神経膠細胞の微細構造を探索するのに有望な拡散MRI神経膠細胞交換イメージングモデルの獲得時間を短縮するための新しい方法を提示します。既存のプロトコルは長いスキャン時間を必要とするため、研究チームはConnectome 2.0スキャナ用の最適化された8基の特徴を持つデータベースのサブセットを、説明可能な人工知能と導かれた再帰的特徴除去戦略を使用して開発しました。この最適化されたプロトコルは、合成データおよびインビボ実験によって検証され、従来のプロトコルおよび他の短縮方法と比較分析された。その結果、最適化されたプロトコルはモデル精度を維持しながらスキャン時間を14分に大幅に短縮し、正確なパラメータ推定と解剖学的対照度を維持し、再検査の再現性に影響を与えませんでした。特に、従来の理論ベースおよびヒューリスティック減少方式と比較して、水交換時間推定値の偏差を2倍以上減らす優れた堅牢性を示しました。
Takeaways、Limitations
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Takeaways：
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拡散MRI神経膠細胞交換イメージングのスキャン時間を劇的に短縮（14分）し、神経科学および臨床研究での適用可能性を高めました。
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データ駆動型の説明可能な人工知能技術を利用して、取得プロトコル最適化の一般化可能な方法論を提示しました。
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従来の理論ベースおよびヒューリスティック低減方式よりも優れた精度と堅牢性を備えた最適化されたプロトコルを開発しました。
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神経膠細胞微細構造分析の効率性を増大させました。
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Limitations：
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この研究で使用されているConnectome 2.0スキャナに特化した方法論であるため、他のスキャナの一般化の可能性には追加の研究が必要です。
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最適化された8特徴サブセットの選択に関する生物学的根拠の追加の説明が必要になる場合があります。
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さまざまな人口集団の一般化の可能性を検証するためのさらなる研究が必要です。
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