Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners

Benchmarking the Pedagogical Knowledge of Large Language Models

ReDit: Reward Dithering for Improved LLM Policy Optimization

Multimodal Fusion SLAM with Fourier Attention

Understanding Reasoning in Thinking Language Models via Steering Vectors

KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation

Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems

AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning

PBFT-Backed Semantic Voting for Multi-Agent Memory Pruning

Long-Context Generalization with Sparse Attention

SycnMapV2: Robust and Adaptive Unsupervised Segmentation

TrainVerify: Equivalence-Based Verification for Distributed LLM Training

Human-Centered Editable Speech-to-Sign-Language Generation via Streaming Conformer-Transformer and Resampling Hook

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making

DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series

Understanding Human-AI Trust in Education

cuVSLAM: CUDA accelerated visual odometry and mapping

TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning

SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification

Disentangling Reasoning and Knowledge in Medical Large Language Models

Process Reward Models That Think

AI-Assisted Transport of Radioactive Ion Beams

Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning

Defeating Prompt Injections by Design

CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities

AI-Facilitated Episodic Future Thinking For Adults with Obesity

AI-Enhanced Deliberative Democracy and the Future of the Collective Will

Robust Optimization with Diffusion Models for Green Security

VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA

SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation

Language Model Re-rankers are Fooled by Lexical Similarities

Evaluating link prediction: New perspectives and recommendations

"I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?

Towards Unsupervised Multi-Agent Reinforcement Learning via Task-Agnostic Exploration

Controllable Video Generation with Provable Disentanglement

Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges

Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production

Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research

Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference

LAuReL: Learned Augmented Residual Layer

Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees

Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning

Rational Metareasoning for Large Language Models

MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning

ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model

ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval

Evaluating Transparent Reasoning in Large Language Models for Accountable Critical Tasks

Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges

Rich Interoperable Metadata for Cultural Heritage Projects at Jagiellonian University

Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated

Exclusive Style Removal for Cross Domain Novel Class Discovery

ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis

Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges

Unified Neural Backdoor Removal with Only Few Clean Samples through Unlearning and Relearning

A Certified Proof Checker for Deep Neural Network Verification in Imandra

ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction

ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping

The Elements of Differentiable Programming

Align and Distill: Unifying and Improving Domain Adaptive Object Detection

Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT

Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups

DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing

Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages

DF2: Distribution-Free Decision-Focused Learning

jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval

ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation

HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges

RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning

MCP-Zero: Active Tool Discovery for Autonomous LLM Agents

What do professional software developers need to know to succeed in an age of Artificial Intelligence?

Emergent Risk Awareness in Rational Agents under Resource Constraints

Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies

TRAIL: Trace Reasoning and Agentic Issue Localization

Lemmanaid: Neuro-Symbolic Lemma Conjecturing

Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation

Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving

Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models

Large language models for automated scholarly paper review: A survey

ChatSR: Multimodal Large Language Models for Scientific Formula Discovery

Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation

Orthogonal Finetuning Made Scalable

Improving Progressive Generation with Decomposable Flow Matching

A standard transformer and attention with linear biases for molecular conformer generation

Persona Features Control Emergent Misalignment

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

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model

Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects

SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning

Cross-regularization: Adaptive Model Complexity through Validation Gradients

Arabic Dialect Classification using RNNs, Transformers, and Large Language Models: A Comparative Analysis

NeRF-based CBCT Reconstruction needs Normalization and Initialization

Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units

Geometric-Aware Variational Inference: Robust and Adaptive Regularization with Directional Weight Uncertainty

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders

Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models

When Can We Reuse a Calibration Set for Multiple Conformal Predictions?

Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance

Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager

Evaluating link prediction: New perspectives and recommendations

Created by

Haebom

저자

Bhargavi Kalyani I, A Rama Prasad Mathi, Niladri Sett

개요

본 논문은 네트워크 과학 및 머신러닝 분야에서 중요한 문제인 링크 예측(Link Prediction, LP) 방법의 평가에 대한 기존 연구의 한계를 지적하고, 보다 엄격하고 통제된 실험 설정을 제안합니다. 기존 연구들이 네트워크 유형, 문제 유형, 노드 간 지오데식 거리, LP 방법의 특성 및 적용 가능성, 클래스 불균형 등 다양한 요소들을 고려하지 않고 균일한 설정에서 평가되었던 점을 문제 삼습니다. 본 논문에서는 이러한 요소들을 고려한 실험 설정을 제시하고, 다양한 실제 네트워크 데이터셋을 사용하여 광범위한 실험을 수행합니다. 실험 결과를 바탕으로 LP 방법의 성능에 영향을 미치는 요소들의 상호작용에 대한 통찰력을 제공하고, LP 방법 평가를 위한 모범 사례를 제시합니다.

시사점, 한계점

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시사점:

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링크 예측 방법 평가를 위한 보다 엄격하고 통제된 실험 설정을 제시함으로써, 기존 연구의 한계를 극복하고 더욱 신뢰할 수 있는 결과를 얻을 수 있음.

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다양한 요소(네트워크 유형, 문제 유형, 지오데식 거리, LP 방법의 특성, 클래스 불균형 등)들이 링크 예측 성능에 미치는 영향을 체계적으로 분석하고, 그 상호작용에 대한 통찰력을 제공함.

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링크 예측 방법 평가를 위한 모범 사례를 제시하여 연구의 재현성 및 신뢰성을 높임.

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한계점:

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제시된 실험 설정 및 모범 사례가 모든 유형의 네트워크 및 링크 예측 문제에 완벽하게 적용될 수 있는지는 추가적인 연구가 필요함.

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본 논문에서 고려한 요소 외에도 링크 예측 성능에 영향을 미칠 수 있는 다른 요소들이 존재할 가능성이 있음.

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사용된 데이터셋의 특성에 따라 결과가 제한될 수 있음. 다양한 종류의 데이터셋을 사용한 추가적인 실험이 필요함.

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