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。
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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

Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models

Created by

Haebom

作者

Sanket Jantre, Deepak Akhare, Xiaoning Qian, Nathan M. Urban

概要

本論文は,偏微分方程式(PDE)モデリングのための効率的なデータ増強戦略を提案する。従来の数値PDEソルバーの代わりにニューラルネットワークを用いたニューラルPDEを使用しますが、従来の方法では、PDEソルバーの長時間統合で得られた年の軌跡を利用して学習するために多くのデータが必要です。本論文では、局所的な「ステンシル」状態の空間充填サンプリングを介してコンピュータモデルから神経PDE学習データを生成するためのサンプル効率的なデータ増強戦略を提示します。この方法は、軌道データに存在する多くの時空間的冗長性を排除し、まれに訪問するが、神経PDEが状態空間全体で一般化するのに役立つ状態を過大サンプリングする。合成データを用いた実験により、提案された方法の効果を検証し、従来の方法より優れた性能を示すことを確認します。

Takeaways、Limitations

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Takeaways：

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サンプル効率的なデータ増強戦略により、神経PDE学習に必要なデータ量を大幅に削減できます。

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従来の方法よりも正確で一般化性能に優れたニューラルPDEステンシル演算子を学習できます。

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様々なPDEシステムに適用可能な一般的な方法論を提示する。

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10倍少ない計算コストで同等またはより良いパフォーマンスを達成できます。

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Limitations：

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提案された方法の性能は、使用するPDEシステムとサンプリング戦略によって異なります。

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合成データを使用した実験結果であるため、実際のアプリケーションでのパフォーマンスには追加の検証が必要です。

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ステンシルサイズとサンプリング戦略の最適化に関するさらなる研究が必要である。

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