Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

MILP-SAT-GNN: Yet Another Neural SAT Solver

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

How Do Vision-Language Models Process Conflicting Information Across Modalities?

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

Probing Evaluation Awareness of Language Models

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

BranchNet: A Neuro-Symbolic Learning Framework for Structured Multi-Class Classification

GPU-based complete search for nonlinear minimization subject to bounds

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness

Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture

Relational Causal Discovery with Latent Confounders

GPT, But Backwards: Exactly Inverting Language Model Outputs

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling

Deep Recommender Models Inference: Automatic Asymmetric Data Flow Optimization

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective

GradMetaNet: An Equivariant Architecture for Learning on Gradients

Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance

Depth Anything at Any Condition

Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation

Prompt Guidance and Human Proximal Perception for HOT Prediction with Regional Joint Loss

Enhanced Influence-aware Group Recommendation for Online Media Propagation

Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems

Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems

Autonomous AI Surveillance: Multimodal Deep Learning for Cognitive and Behavioral Monitoring

Exploring Classical Piano Performance Generation with Expressive Music Variational AutoEncoder

Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware

Self-Guided Process Reward Optimization with Masked Step Advantage for Process Reinforcement Learning

Crafting Hanzi as Narrative Bridges: An AI Co-Creation Workshop for Elderly Migrants

AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions

Chargax: A JAX Accelerated EV Charging Simulator

Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence

Integrating Traditional and Deep Learning Methods to Detect Tree Crowns in Satellite Images

Crop Pest Classification Using Deep Learning Techniques: A Review

BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments

Epistemic Scarcity: The Economics of Unresolvable Unknowns

Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation

Quantum-Assisted Automatic Path-Planning for Robotic Quality Inspection in Industry 4.0

Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems

DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal

Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing

Evaluating LLM Agent Collusion in Double Auctions

Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping

Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound

Distributional Soft Actor-Critic with Diffusion Policy

RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms

Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy

User-guided Generative Source Separation

LEDOM: An Open and Fundamental Reverse Language Model

Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy

ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks

Neural Hamiltonian Operator

VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process

Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization

AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance

PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning

LLM-based Realistic Safety-Critical Driving Video Generation

GAIus: Combining Genai with Legal Clauses Retrieval for Knowledge-based Assistant

Beyond First-Order: Training LLMs with Stochastic Conjugate Subgradients and AdamW

Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration

Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives

Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning

Geometry-aware 4D Video Generation for Robot Manipulation

AI-guided digital intervention with physiological monitoring reduces intrusive memories after experimental trauma

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem

Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services

FAIR-MATCH: A Multi-Objective Framework for Bias Mitigation in Reciprocal Dating Recommendations

Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review

Epitome: Pioneering an Experimental Platform for AI-Social Science Integration

Automated Vehicles Should be Connected with Natural Language

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science

Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis

Long-Sequence Memory with Temporal Kernels and Dense Hopfield Functionals

Can AI be Consentful?

Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization

Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals

Data Classification with Dynamically Growing and Shrinking Neural Networks

Can Argus Judge Them All? Comparing VLMs Across Domains

Fast AI Model Splitting over Edge Networks

The Singapore Consensus on Global AI Safety Research Priorities

Created by

Haebom

저자

Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, Soren Mindermann, Vanessa Wilfred, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Simeon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnes Delaborde, Nouha Dziri, Francisco Eiras, Joshua Engels, Jinyu Fan, Adam Gleave, Noah Goodman, Fynn Heide, Johannes Heidecke, Dan Hendrycks, Cyrus Hodes, Bryan Low Kian Hsiang, Minlie Huang, Sami Jawhar, Wang Jingyu, Adam Tauman Kalai, Meindert Kamphuis, Mohan Kankanhalli, Subhash Kantamneni, Mathias Bonde Kirk, Thomas Kwa, Jeffrey Ladish, Kwok-Yan Lam, Wan Lee Sie, Taewhi Lee, Xiaojian Li, Jiajun Liu, Chaochao Lu, Yifan Mai, Richard Mallah, Julian Michael, Nick Moes, Simon Moller, Kihyuk Nam, Kwan Yee Ng, Mark Nitzberg, Besmira Nushi, Sean O hEigeartaigh, Alejandro Ortega, Pierre Peigne, James Petrie, Benjamin Prud'Homme, Reihaneh Rabbany, Nayat Sanchez-Pi, Sarah Schwettmann, Buck Shlegeris, Saad Siddiqui, Aradhana Sinha, Martin Soto, Cheston Tan, Dong Ting, William Tjhi, Robert Trager, Brian Tse, Anthony Tung K. H., Vanessa Wilfred, John Willes, Denise Wong, Wei Xu, Rongwu Xu, Yi Zeng, HongJiang Zhang, Djordje \v{Z}ikelic

개요

본 논문은 싱가포르에서 개최된 2025 AI 안전 국제 학술 교류회(SCAI)를 바탕으로 작성된 보고서를 요약한 것이다. 급속히 발전하는 AI의 능력과 자율성은 변혁의 가능성을 제시하지만, 동시에 AI의 안전성(신뢰성, 안정성, 보안) 확보에 대한 논의를 촉진하고 있다. 이에 따라, 신뢰할 수 있는 AI 생태계 구축이 필수적이며, 이를 위해 AI 안전 연구의 우선순위를 확인하고 종합하는 것을 목표로 한다. 본 보고서는 Yoshua Bengio가 주도하고 33개국 정부가 지원하는 국제 AI 안전 보고서를 기반으로 하며, 방어 심층 모델을 채택하여 AI 안전 연구 영역을 신뢰할 수 있는 AI 시스템 생성의 과제(개발), 위험 평가의 과제(평가), 배포 후 모니터링 및 개입의 과제(제어)의 세 가지 유형으로 구성한다.

시사점, 한계점

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시사점:

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AI 안전 연구의 우선순위를 체계적으로 정리하고, 국제적인 협력을 통해 연구 방향을 제시한다.

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방어 심층 모델을 활용하여 AI 안전 연구 영역을 효과적으로 분류하고, 각 영역의 과제를 명확히 제시한다.

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AI 안전 생태계 구축을 위한 중요한 논의를 제공하며, AI 기술 발전과 안전성 확보 사이의 균형을 모색한다.

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한계점:

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보고서의 구체적인 연구 내용 및 결과가 제한적으로 제시되어 있다.

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각 연구 영역에 대한 세부적인 분석과 전략이 부족할 수 있다.

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실제 AI 시스템 개발 및 배포 과정에서의 적용 가능성에 대한 검토가 필요하다.

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