Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

CoRT: Code-integrated Reasoning within Thinking

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Policy-Based Trajectory Clustering in Offline Reinforcement Learning

Understanding Human-AI Trust in Education

ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO

Robotic Policy Learning via Human-assisted Action Preference Optimization

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

QuantMCP: Grounding Large Language Models in Verifiable Financial Reality

Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery

Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment

Sample Complexity and Representation Ability of Test-time Scaling Paradigms

Context Is Not Comprehension

High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset

SALAD: Systematic Assessment of Machine Unlearing on LLM-Aided Hardware Design

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning

Quantum AIXI: Universal Intelligence via Quantum Information

VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization

QuXAI: Explainers for Hybrid Quantum Machine Learning Models

Convert Language Model into a Value-based Strategic Planner

PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications

Token-Efficient RL for LLM Reasoning

MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark

Elucidating the Design Space of Multimodal Protein Language Models

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves

Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts

Content ARCs: Decentralized Content Rights in the Age of Generative AI

PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play

Computation Mechanism Behind LLM Position Generalization

CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting

Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges

Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations

An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem

Generative Uncertainty in Diffusion Models

EgoNormia: Benchmarking Physical Social Norm Understanding

Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

Object-Centric Latent Action Learning

Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

TransMLA: Multi-Head Latent Attention Is All You Need

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Prompt-based Depth Pruning of Large Language Models

Great Models Think Alike and this Undermines AI Oversight

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting

Latent Action Learning Requires Supervision in the Presence of Distractors

SR-Reward: Taking The Path More Traveled

Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs

SoK: Watermarking for AI-Generated Content

Engagement-Driven Content Generation with Large Language Models

PyGen: A Collaborative Human-AI Approach to Python Package Creation

DAWN: Designing Distributed Agents in a Worldwide Network

Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling

Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery

LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement

Deploying Open-Source Large Language Models: A performance Analysis

Neural Networks Generalize on Low Complexity Data

M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Predictive Embedding Architecture

Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs

The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation

TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation

Multi-group Uncertainty Quantification for Long-form Text Generation

Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique

Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things

IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language

Incentivizing Quality Text Generation via Statistical Contracts

Visually Descriptive Language Model for Vector Graphics Reasoning

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

Noise Balance and Stationary Distribution of Stochastic Gradient Descent

The Packing Chromatic Number of the Infinite Square Grid is 15

Reinforcing Multimodal Understanding and Generation with Dual Self-rewards

A Proposal to Extend the Common Model of Cognition with Metacognition

The Optimization Paradox in Clinical AI Multi-Agent Systems

CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDE

Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems

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Griffin: Towards a Graph-Centric Relational Database Foundation Model

Created by

Haebom

저자

Yanbo Wang, Xiyuan Wang, Quan Gan, Minjie Wang, Qibin Yang, David Wipf, Muhan Zhang

개요

Griffin은 관계형 데이터베이스(RDBs)를 위해 특별히 설계된 최초의 기초 모델 구현입니다. 기존의 단일 RDB 작업에 중점을 둔 소규모 모델과 달리, Griffin은 데이터 인코더와 작업 디코더를 통합하여 다양한 작업을 처리합니다. 또한, 크로스 어텐션 모듈과 새로운 집계기를 통합하여 아키텍처를 개선했습니다. Griffin은 단일 테이블 및 RDB 데이터셋 모두에 대한 사전 훈련을 활용하며, 범주형, 수치형 및 메타데이터 기능을 위한 고급 인코더와 크로스 어텐션 모듈 및 향상된 메시지 전달 신경망(MPNNs)과 같은 혁신적인 구성 요소를 사용하여 관계형 데이터의 복잡성을 포착합니다. 다양한 도메인의 RDB에서 추출한 대규모, 이종 및 시간적 그래프(1억 5천만 개 이상의 노드)에서 평가된 Griffin은 개별적으로 훈련된 모델에 비해 우수하거나 비슷한 성능을 보여주며, 데이터가 부족한 시나리오에서도 탁월하고, 새로운 데이터셋과 작업에 대한 사전 훈련의 유사성과 다양성을 통해 강력한 전이성을 보여줍니다. 이는 RDB에 대한 보편적으로 적용 가능한 기초 모델로서의 잠재력을 강조합니다. 코드는 https://github.com/yanxwb/Griffin 에서 사용할 수 있습니다.

GitHub - yanxwb/Griffin: This is the official implementation of the paper “Griffin: Towards a Graph-Centric Relational Database Foundation Model.”

This is the official implementation of the paper “Griffin: Towards a Graph-Centric Relational Database Foundation Model.” - yanxwb/Griffin

시사점, 한계점

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시사점:

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관계형 데이터베이스 작업을 위한 최초의 기초 모델 제공.

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다양한 작업에 대한 통합된 데이터 인코더 및 작업 디코더 활용.

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크로스 어텐션 모듈과 향상된 MPNN을 통해 관계형 데이터의 복잡성을 효과적으로 포착.

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대규모 데이터셋에서 우수한 성능 및 데이터 부족 환경에서의 강점 입증.

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새로운 데이터셋과 작업에 대한 강력한 전이 학습 성능.

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한계점:

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본 논문에서는 구체적인 한계점이 명시적으로 언급되지 않음. 추가적인 실험 및 분석을 통해 한계점을 밝힐 필요가 있음.

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모델의 확장성 및 다른 유형의 데이터베이스에 대한 적용 가능성에 대한 추가 연구 필요.

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