Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

NLKI: A lightweight Natural Language Knowledge Integration Framework for Improving Small VLMs in Commonsense VQA Tasks

Interact-Custom: Customized Human Object Interaction Image Generation

A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation

MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation

From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity

Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks

STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems

LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions

Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning

Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework

Humans Perceive Wrong Narratives from AI Reasoning Texts

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning

Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services

Learning to Drive Ethically: Embedding Moral Reasoning into Autonomous Driving

Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation

Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics

Steering Towards Fairness: Mitigating Political Bias in LLMs

Dynamic Context Compression for Efficient RAG

Irredundant $k$-Fold Cross-Validation

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity

A Highly Clean Recipe Dataset with Ingredient States Annotation for State Probing Task

Entropy-Memorization Law: Evaluating Memorization Difficulty of Data in LLMs

The Joys of Categorical Conformal Prediction

Adversarial Manipulation of Reasoning Models using Internal Representations

Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models

A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems (Changes are marked)

GLProtein: Global-and-Local Structure Aware Protein Representation Learning

Program Semantic Inequivalence Game with Large Language Models

DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness

Improving Quantization with Post-Training Model Expansion

Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features

A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

Federated nnU-Net for Privacy-Preserving Medical Image Segmentation

ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation

Enhancing Automated Loop Invariant Generation for Complex Programs with Large Language Models

RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis

Categorical Data Clustering via Value Order Estimated Distance Metric Learning

Application of AI to formal methods - an analysis of current trends

Reconsidering the Performance of GAE in Link Prediction

See then Tell: Enhancing Key Information Extraction with Vision Grounding

Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference

SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking

Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off

Network Formation and Dynamics Among Multi-LLMs

NetGPT: Generative Pretrained Transformer for Network Traffic

OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset

Explainability of Text Processing and Retrieval Methods: A Survey

The Ramon Llull's Thinking Machine for Automated Ideation

RLMR: Reinforcement Learning with Mixed Rewards for Creative Writing

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence

MSARL: Decoupling Reasoning and Tool Use with Multi-Small-Agent Reinforcement Learning

Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground

Possible Principles for Aligned Structure Learning Agents

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

Prompt-to-Product: Generative Assembly via Bimanual Manipulation

OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models

Mixture of Contexts for Long Video Generation

FakeParts: a New Family of AI-Generated DeepFakes

Enabling Equitable Access to Trustworthy Financial Reasoning

Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning

Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop

Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance

ChainReaction! Structured Approach with Causal Chains as Intermediate Representations for Improved and Explainable Causal Video Question Answering

Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees

ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts

WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents

Research Challenges in Relational Database Management Systems for LLM Queries

Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant

AI Agentic Vulnerability Injection And Transformation with Optimized Reasoning

JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring

Learning Primitive Embodied World Models: Towards Scalable Robotic Learning

Multi-Agent Penetration Testing AI for the Web

Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting

Exploring Machine Learning and Language Models for Multimodal Depression Detection

Speech Emotion Recognition via Entropy-Aware Score Selection

Surfel-based 3D Registration with Equivariant SE(3) Features

Evaluating Compositional Generalisation in VLMs and Diffusion Models

Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML

Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI

Signs of Struggle: Spotting Cognitive Distortions across Language and Register

Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection

Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding

SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer

Occlusion Robustness of CLIP for Military Vehicle Classification

SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding

Provable Benefits of In-Tool Learning for Large Language Models

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol

EEGDM: Learning EEG Representation with Latent Diffusion Model

Generative Annotation for ASR Named Entity Correction

MobileCLIP2: Improving Multi-Modal Reinforced Training

Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning

Artificial Intelligence and Generative Models for Materials Discovery -- A Review

Created by

Haebom

저자

Albertus Denny Handoko, Riko I Made

개요

본 논문은 고처리량 실험 도구, 머신러닝(ML) 방법 및 공개 소재 데이터베이스가 새로운 소재 발견 방식을 혁신적으로 변화시키고 있음을 다룹니다. 과거 실험 중심 접근 방식에서 인공지능(AI) 중심 접근 방식으로 빠르게 이동하여 원하는 특성을 가진 새로운 소재를 발견할 수 있는 '역설계' 기능을 구현하고 있습니다. 본 논문에서는 소재 발견에 적용 가능한 AI 기반 생성 모델의 다양한 원리와 이를 위한 다양한 소재 표현 방식을 논의합니다. 또한 새로운 촉매, 반도체, 고분자 또는 결정 설계에서 생성 모델의 특정 응용 분야를 강조하고, 데이터 부족, 계산 비용, 해석 가능성, 합성 가능성 및 데이터 세트 편향과 같은 과제를 해결합니다. 제한 사항을 극복하고 AI를 실험적 워크플로우와 통합하기 위한 새로운 접근 방식(다중 모드 모델, 물리 정보 아키텍처 및 폐쇄 루프 발견 시스템 포함)을 논의하며, 지속 가능성, 의료 및 에너지 혁신을 위한 소재 발견 가속화에 AI의 혁신적인 잠재력을 활용하려는 연구원에게 통찰력을 제공하는 것을 목표로 합니다.

시사점, 한계점

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시사점: AI 기반 생성 모델을 활용한 소재 발견의 다양한 원리와 응용 분야를 제시하여 소재 개발의 효율성을 높일 수 있는 방향을 제시합니다. 다중 모드 모델, 물리 정보 아키텍처, 폐쇄 루프 발견 시스템 등의 새로운 접근 방식을 통해 AI와 실험적 워크플로우의 통합을 위한 가능성을 보여줍니다. 지속 가능성, 의료, 에너지 혁신 등 다양한 분야에 기여할 수 있는 소재 발견을 가속화할 수 있습니다.

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한계점: 데이터 부족, 계산 비용, 해석 가능성, 합성 가능성 및 데이터 세트 편향 등 AI 기반 소재 발견의 어려움을 명확히 제시하지만, 이러한 문제에 대한 구체적인 해결 방안은 추가적인 연구가 필요합니다. 모든 유형의 소재에 대한 일반화된 접근 방식이 아닌 특정 소재에 대한 사례 연구에 집중되어 있어 일반화에 한계가 있을 수 있습니다.

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