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.

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Power Stabilization for AI Training Datacenters

A Systematic Study of Deep Learning Models and xAI Methods for Region-of-Interest Detection in MRI Scans

Documenting Deployment with Fabric: A Repository of Real-World AI Governance

Surya: Foundation Model for Heliophysics

Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets

MCLPD:Multi-view Contrastive Learning for EEG-based PD Detection Across Datasets

FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog

Kourkoutas-Beta: A Sunspike-Driven Adam Optimizer with Desert Flair

SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip

Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats

LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients

Preacher: Paper-to-Video Agentic System

Agoran: An Agentic Open Marketplace for 6G RAN Automation

Architectural Co-Design for Zero-Shot Anomaly Detection: Decoupling Representation and Dynamically Fusing Features in CLIP

IBPS: Indian Bail Prediction System

Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time

TS-Insight: Visualizing Thompson Sampling for Verification and XAI

When Better Eyes Lead to Blindness: A Diagnostic Study of the Information Bottleneck in CNN-LSTM Image Captioning Models

Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters

Generation of structure-guided pMHC-I libraries using Diffusion Models

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients

MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation

KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis

Empirical Evidence for Alignment Faking in a Small LLM and Prompt-Based Mitigation Techniques

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Deep regularization networks for inverse problems with noisy operators

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Lossless Token Sequence Compression via Meta-Tokens

Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer

Flexible Tool Selection through Low-dimensional Attribute Alignment of Vision and Language

Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model

MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning

Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

Sadeed: Advancing Arabic Diacritization Through Small Language Model

Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs

CaRL: Learning Scalable Planning Policies with Simple Rewards

On the Consistency of GNN Explanations for Malware Detection

Cequel: Cost-Effective Querying of Large Language Models for Text Clustering

Kuwain 1.5B: An Arabic SLM via Language Injection

MuSeD: A Multimodal Spanish Dataset for Sexism Detection in Social Media Videos

TextSplat: Text-Guided Semantic Fusion for Generalizable Gaussian Splatting

VerifiAgent: a Unified Verification Agent in Language Model Reasoning

Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation

Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm

A Case for Specialisation in Non-Human Entities

Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language

Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection

Innamark: A Whitespace Replacement Information-Hiding Method

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation

Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users

Self-Supervised Prompt Optimization

Learning to Generate Unit Tests for Automated Debugging

Modeling Discrimination with Causal Abstraction

Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models

Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review

Fine-tuning foundational models to code diagnoses from veterinary health records

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models

Continual Learning for Multimodal Data Fusion of a Soft Gripper

BoostTrack++: using tracklet information to detect more objects in multiple object tracking

OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data

CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains

Generating 3D Terrain with 2D Cellular Automata

Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time

Using a cognitive architecture to consider antiBlackness in design and development of AI systems

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings

ThinkTuning: Instilling Cognitive Reflections without Distillation

A "good regulator theorem" for embodied agents

Prescriptive Agents based on RAG for Automated Maintenance (PARAM)

One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning

Opus: A Prompt Intention Framework for Complex Workflow Generation

Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues

It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics

GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy

Automatic Curriculum Design for Zero-Shot Human-AI Coordination

PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data

SycEval: Evaluating LLM Sycophancy

CopyrightShield: Enhancing Diffusion Model Security against Copyright Infringement Attacks

VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making

Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in Healthcare

On Learning Action Costs from Input Plans

Human-Object Interaction from Human-Level Instructions

Non-linear Welfare-Aware Strategic Learning

CRISPR-GPT for Agentic Automation of Gene-editing Experiments

SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass

Discovering Hidden Algebraic Structures via Transformers with Rank-Aware Beam GRPO

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Neural Robot Dynamics

Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis

"Does the cafe entrance look accessible? Where is the door?" Towards Geospatial AI Agents for Visual Inquiries

End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning

Numerical models outperform AI weather forecasts of record-breaking extremes

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

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI

StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding

Power Stabilization for AI Training Datacenters

Created by

Haebom

저자

Esha Choukse, Brijesh Warrier, Scot Heath, Luz Belmont, April Zhao, Hassan Ali Khan, Brian Harry, Matthew Kappel, Russell J. Hewett, Kushal Datta, Yu Pei, Caroline Lichtenberger, John Siegler, David Lukofsky, Zaid Kahn, Gurpreet Sahota, Andy Sullivan, Charles Frederick, Hien Thai, Rebecca Naughton, Daniel Jurnove, Justin Harp, Reid Carper, Nithish Mahalingam, Srini Varkala, Alok Gautam Kumbhare, Satyajit Desai, Venkatesh Ramamurthy, Praneeth Gottumukkala, Girish Bhatia, Kelsey Wildstone, Laurentiu Olariu, Ileana Incorvaia, Alex Wetmore, Prabhat Ram, Melur Raghuraman, Mohammed Ayna, Mike Kendrick, Ricardo Bianchini, Aaron Hurst, Reza Zamani, Xin Li, Michael Petrov, Gene Oden, Rory Carmichael, Tom Li, Apoorv Gupta, Pratikkumar Patel, Nilesh Dattani, Lawrence Marwong, Rob Nertney, Hirofumi Kobayashi, Jeff Liott, Miro Enev, Divya Ramakrishnan, Ian Buck, Jonah Alben

개요

본 논문은 수만 개의 GPU를 사용하는 대규모 AI 학습 작업의 전력 관리 문제를 다룹니다. 학습 중 전력 소비의 높은 변동성으로 인해, 각 반복마다 연산 집약적 단계와 통신 집약적 단계에서 전력 소비량이 크게 달라져 큰 전력 변동이 발생합니다. 이러한 변동의 진폭은 학습 작업의 규모가 커짐에 따라 증가하며, 변동의 주파수가 유틸리티의 중요 주파수와 일치할 경우 전력망 인프라에 물리적 손상을 입힐 수 있습니다. 따라서 AI 학습 작업의 안전한 확장을 위해서는 전력 안정화가 필수적입니다. 본 논문에서는 실제 데이터를 사용하여 이 문제를 제시하고, 소프트웨어, GPU 하드웨어 및 데이터 센터 인프라 전반에 걸쳐 혁신적인 해결책을 탐구합니다. 각 접근 방식의 장단점을 제시하고, 다각적인 접근 방식을 제안합니다. 제안된 해결책은 실제 하드웨어와 Microsoft의 사내 클라우드 전력 시뮬레이터를 결합하여 엄격하게 테스트되어 실제 환경에서의 효과에 대한 중요한 통찰력을 제공합니다.

시사점, 한계점

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시사점:

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대규모 AI 학습 작업의 전력 관리 문제를 체계적으로 분석하고 다각적인 해결 방안을 제시함.

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실제 데이터 및 시뮬레이션을 활용한 실험적 검증을 통해 해결 방안의 효과를 입증함.

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소프트웨어, 하드웨어, 인프라 전반에 걸친 포괄적인 접근 방식 제시.

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한계점:

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Microsoft의 사내 클라우드 전력 시뮬레이터를 사용하여 실제 환경과의 차이가 존재할 가능성.

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제안된 해결책의 장기적인 효과 및 유지보수 비용에 대한 분석 부족.

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다양한 유형의 AI 학습 작업에 대한 일반화 가능성에 대한 추가 연구 필요.

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