Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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Distillation Robustifies Unlearning

Cartridges: Lightweight and general-purpose long context representations via self-study

Text-to-LoRA: Instant Transformer Adaption

Information Bargaining: Bilateral Commitment in Bayesian Persuasion

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models

Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

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

TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood

A Red Teaming Roadmap Towards System-Level Safety

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation

Context Is Not Comprehension

Feature-Based Lie Group Transformer for Real-World Applications

Horizon Reduction Makes RL Scalable

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO AMD Progression Challenge

CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

Rethinking the effects of data contamination in Code Intelligence

MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction

SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race

Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws

Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert

Large Language Models Often Know When They Are Being Evaluated

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation

PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation

RainFusion: Adaptive Video Generation Acceleration via Multi-Dimensional Visual Redundancy

VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation

When Two LLMs Debate, Both Think They'll Win

Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias

GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains

Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps

Sample Complexity of Diffusion Model Training Without Empirical Risk Minimizer Access

EVADE: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications

Simulating Macroeconomic Expectations using LLM Agents

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

Mechanistic evaluation of Transformers and state space models

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

Pel, A Programming Language for Orchestrating AI Agents

MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models

Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning

BLEUBERI: BLEU is a surprisingly effective reward for instruction following

Position: We Need Responsible, Application-Driven (RAD) AI Research

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach

LookAlike: Consistent Distractor Generation in Math MCQs

Tree-Sliced Wasserstein Distance with Nonlinear Projection

Test-time Correlation Alignment

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

MIB: A Mechanistic Interpretability Benchmark

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization

Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure

Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation

sudo rm -rf agentic_security

Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models

Towards Achieving Perfect Multimodal Alignment

nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning

FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA

RONA: Pragmatically Diverse Image Captioning with Coherence Relations

Unifying 2D and 3D Vision-Language Understanding

Revisiting semi-supervised learning in the era of foundation models

AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation

Generalized Interpolating Discrete Diffusion

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models

Straight-Line Diffusion Model for Efficient 3D Molecular Generation

Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach

Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets

Dialogue Without Limits: Constant-Sized KV Caches for Extended Responses in LLMs

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement

from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

EgoNormia: Benchmarking Physical Social Norm Understanding

PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation

NeoBERT: A Next-Generation BERT

From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients

AMPO: Active Multi-Preference Optimization for Self-play Preference Selection

SYNTHIA: Novel Concept Design with Affordance Composition

DBudgetKV: Dynamic Budget in KV Cache Compression for Ensuring Optimal Performance

AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay

DISC: DISC: Dynamic Decomposition Improves LLM Inference Scaling

Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach

Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G

MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding

Improving the Diffusability of Autoencoders

Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis

MMTEB: Massive Multilingual Text Embedding Benchmark

From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MARKERGEN

How Expressive are Knowledge Graph Foundation Models?

Machine Learning Should Maximize Welfare, but Not by (Only) Maximizing Accuracy

How Expressive are Knowledge Graph Foundation Models?

Created by

Haebom

저자

Xingyue Huang, Pablo Barcelo, Michael M. Bronstein, Ismail Ilkan Ceylan, Mikhail Galkin, Juan L Reutter, Miguel Romero Orth

개요

본 논문은 지식 그래프 기반의 심층 학습 모델인 지식 그래프 기반 모델(KGFMs)의 표현 능력에 대한 이론적 연구를 수행합니다. KGFMs는 서로 다른 관계 어휘를 가진 완전히 새로운 지식 그래프로 일반화될 수 있지만, 그 표현 능력에 대한 이론적 이해는 매우 제한적입니다. 본 논문에서는 KGFMs의 표현 능력이 관계 표현을 학습하는 데 사용되는 모티프에 직접적으로 의존한다는 것을 보여줍니다. 기존 연구에서 가장 일반적으로 사용되는 모티프는 이진 모티프이며, 이는 관계쌍의 상호 작용을 기반으로 표현을 학습하기 때문에 모델의 표현 능력을 제한합니다. 따라서 본 논문에서는 더 풍부한 모티프를 사용하는 더욱 표현력이 뛰어난 KGFMs를 설계하고, 이를 통해 예를 들어 세 개의 관계가 서로 상호 작용하는 방식을 기반으로 관계 표현을 학습합니다. 마지막으로 다양한 도메인에서 추출한 광범위한 데이터 세트에서 실험적으로 이론적 발견을 검증하여 더 풍부한 모티프의 사용이 성능 향상으로 이어짐을 보여줍니다.

시사점, 한계점

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시사점:

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KGFMs의 표현 능력이 사용되는 모티프에 의존함을 이론적으로 규명.

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기존 이진 모티프의 한계를 지적하고, 더 풍부한 모티프를 사용한 KGFMs 설계 제안.

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더 풍부한 모티프 사용이 다양한 데이터셋에서 성능 향상으로 이어짐을 실험적으로 검증.

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한계점:

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제안된 더 풍부한 모티프 기반 KGFMs의 일반화 성능에 대한 추가적인 연구 필요.

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다양한 종류의 지식 그래프에 대한 포괄적인 실험적 평가가 필요.

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모티프의 복잡도와 모델 성능 간의 관계에 대한 더욱 심층적인 분석 필요.

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