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.

Enhancing Password Security Through a High-Accuracy Scoring Framework Using Random Forests

Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm

Leveraging Large Language Models for Use Case Model Generation from Software Requirements

Enhancing PIBT via Multi-Action Operations

PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery

Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning

PAN: A World Model for General, Interactable, and Long-Horizon World Simulation

Learning the Basis: A Kolmogorov-Arnold Network Approach Embedding Green's Function Priors

Automatic Grid Updates for Kolmogorov-Arnold Networks using Layer Histograms

Remodeling Semantic Relationships in Vision-Language Fine-Tuning

Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models

MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs

Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from LDCT

Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations

Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support

Search Is Not Retrieval: Decoupling Semantic Matching from Contextual Assembly in RAG

Does AI-Assisted Coding Deliver? A Difference-in-Differences Study of Cursor's Impact on Software Projects

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

Text to Robotic Assembly of Multi Component Objects using 3D Generative AI and Vision Language Models

WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios

Xiaoice: Training-Free Video Understanding via Self-Supervised Spatio-Temporal Clustering of Semantic Features

A Critical Review of the Need for Knowledge-Centric Evaluation of Quranic Recitation

The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning

Artificial-Intelligence Grading Assistance for Handwritten Components of a Calculus Exam

Computing Wasserstein Barycenters through Gradient Flows

Enhancing the development of Cherenkov Telescope Array control software with Large Language Models

GPT and Prejudice: A Sparse Approach to Understanding Learned Representations in Large Language Models

One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences

FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answering

Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation

Inference Offloading for Cost-Sensitive Binary Classification at the Edge

Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning

Zero-Shot Referring Expression Comprehension via Vison-Language True/False Verification

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance

ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset

Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-commerce Models

Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search

Beyond Frequency: Seeing Subtle Cues Through the Lens of Spatial Decomposition for Fine-Grained Visual Classification

Test-Time Reinforcement Learning for GUI Grounding via Region Consistency

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction

Application-Specific Component-Aware Structured Pruning of Deep Neural Networks in Control via Soft Coefficient Optimization

Cameras as Relative Positional Encoding

The Prompt War: How AI Decides on a Military Intervention

xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection

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

Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease

Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption

Understanding Human-AI Trust in Education

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Scaling Textual Gradients via Sampling-Based Momentum

Caption This, Reason That: VLMs Caught in the Middle

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks

Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery

OODTE: A Differential Testing Engine for the ONNX Optimizer

Constructing an Optimal Behavior Basis for the Option Keyboard

Bridging LMS and generative AI: dynamic course content integration (DCCI) for enhancing student satisfaction and engagement via the ask ME assistant

ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation

Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction

Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA

FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models

MMTEB: Massive Multilingual Text Embedding Benchmark

Enhanced Structured Lasso Pruning with Class-wise Information

CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning

Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation

Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors

Enhanced Suicidal Ideation Detection from Social Media Using a CNN-BiLSTM Hybrid Model

Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations

Reducing the Scope of Language Models

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs

Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data

Multi-Turn Interactions for Text-to-SQL with Large Language Models

Differentiating between human-written and AI-generated texts using linguistic features automatically extracted from an online computational tool

Spikingformer: A Key Foundation Model for Spiking Neural Networks

SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models

An Efficient Training Pipeline for Reasoning Graphical User Interface Agents

National Institute on Aging PREPARE Challenge: Early Detection of Cognitive Impairment Using Speech -- The SpeechCARE Solution

Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression

Enhancing Logical Expressiveness in Graph Neural Networks via Path-Neighbor Aggregation

WaterMod: Modular Token-Rank Partitioning for Probability-Balanced LLM Watermarking

Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets

A Brain Cell Type Resource Created by Large Language Models and a Multi-Agent AI System for Collaborative Community Annotation

LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics

PsychCounsel-Bench: Evaluating the Psychology Intelligence of Large Language Models

DOoM: Difficult Olympiads of Math

From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning

PITA: Preference-Guided Inference-Time Alignment for LLM Post-Training

Small Models Struggle to Learn from Strong Reasoners

Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models

Enhancing Conflict Resolution in Language Models via Abstract Argumentation

Discussion Graph Semantics of First-Order Logic with Equality for Reasoning about Discussion and Argumentation

A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning

Black-Box On-Policy Distillation of Large Language Models

ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs

Created by

Haebom

저자

Tooraj Helmi

개요

자연어로 표현된 소프트웨어 요구사항은 종종 장황하고, 모호하며, 불일치합니다. 이는 시스템에 적합한 아키텍처를 선택하고 다양한 아키텍처 대안을 평가하는 것을 포함한 다양한 문제를 야기합니다. 자연어 요구사항을 아키텍처에 매핑하는 작업에 인간 전문 지식에 의존하는 것은 시간이 많이 걸리고 오류가 발생하기 쉽습니다. 본 논문에서는 (1) 시스템에 대한 자연어 요구사항 집합, (2) 아키텍처 관련 소프트웨어 품질 속성을 지정하는 기존 표준, (3) 쉽게 사용 가능한 대규모 언어 모델(LLM)을 활용하여 이 작업을 자동화하는 ARLO라는 접근 방식을 제안합니다. 구체적으로 ARLO는 주어진 시스템에 대해 아키텍처와 관련된 자연어 요구사항의 하위 집합을 결정하고 해당 하위 집합을 아키텍처 선택의 조정 가능한 매트릭스에 매핑합니다. ARLO는 아키텍처 선택 매트릭스에 정수 선형 계획법을 적용하여 현재 요구사항에 대한 최적의 아키텍처를 결정합니다. 실제 예시를 사용하여 ARLO의 효과를 보여줍니다. 또한 ARLO는 (1) 선택된 아키텍처 선택을 요구사항에 추적하고 (2) 시스템 아키텍처에 특정한 영향을 미치는 자연어 요구사항을 분리하는 기능을 강조합니다. 이를 통해 요구사항 및 제약 조건에 따라 대안적인 아키텍처 선택을 식별, 비교 평가 및 탐색할 수 있습니다.

시사점, 한계점

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시사점:

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자연어로 된 소프트웨어 요구사항을 자동으로 아키텍처에 매핑하는 효율적인 방법을 제공합니다.

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선택된 아키텍처 선택을 요구사항에 추적하고 아키텍처에 영향을 미치는 요구사항을 식별하여 투명성을 높입니다.

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대규모 언어 모델을 활용하여 기존의 수작업 방식보다 빠르고 정확하게 아키텍처 설계를 지원합니다.

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다양한 아키텍처 대안을 비교 평가하고 최적의 아키텍처를 선택하는 데 도움을 줍니다.

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한계점:

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사용된 LLM의 성능에 의존하며, LLM의 한계가 ARLO의 성능에 영향을 미칠 수 있습니다.

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아키텍처 관련 소프트웨어 품질 속성을 정의하는 기존 표준에 의존하므로, 표준의 적절성 및 완전성이 중요합니다.

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실제 시스템에 적용하기 위한 추가적인 검증 및 평가가 필요합니다.

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복잡한 시스템이나 모호한 요구사항의 경우 정확도가 떨어질 수 있습니다.

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