Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization

Physics-Informed Neural Networks for Nonlinear Output Regulation

Unlocking the Forgery Detection Potential of Vanilla MLLMs: A Novel Training-Free Pipeline

Generalized Denoising Diffusion Codebook Models (gDDCM): Tokenizing images using a pre-trained diffusion model

Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations

Dimension vs. Precision: A Comparative Analysis of Autoencoders and Quantization for Efficient Vector Retrieval on BEIR SciFact

UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective

PFAvatar: Pose-Fusion 3D Personalized Avatar Reconstruction from Real-World Outfit-of-the-Day Photos

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning

Fine-Grained Representation for Lane Topology Reasoning

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization

A Meta-Heuristic Load Balancer for Cloud Computing Systems

Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)

MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

Who Gets the Reward, Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents

A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space

LocalBench: Benchmarking LLMs on County-Level Local Knowledge and Reasoning

LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls

Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks

FGM-HD: Boosting Generation Diversity of Fractal Generative Models through Hausdorff Dimension Induction

Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis

Resilient by Design -- Active Inference for Distributed Continuum Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation

MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers

Leveraging LLM-based agents for social science research: insights from citation network simulations

Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response

DRIP: Defending Prompt Injection via Token-wise Representation Editing and Residual Instruction Fusion

Clone Deterministic 3D Worlds

Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics

MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding

FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection

From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance

PromptGuard at BLP-2025 Task 1: A Few-Shot Classification Framework Using Majority Voting and Keyword Similarity for Bengali Hate Speech Detection

A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

Towards Sharper Object Boundaries in Self-Supervised Depth Estimation

Patent Language Model Pretraining with ModernBERT

Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs

Efficient Decoding Methods for Language Models on Encrypted Data

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

DepthVision: Enabling Robust Vision-Language Models with GAN-Based LiDAR-to-RGB Synthesis for Autonomous Driving

Automatic Differentiation of Agent-Based Models

Do Retrieval Augmented Language Models Know When They Don't Know?

Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning

RynnEC: Bringing MLLMs into Embodied World

LENS: Learning to Segment Anything with Unified Reinforced Reasoning

MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions

Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework

LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations

SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation

GAIS: Frame-Level Gated Audio-Visual Integration with Semantic Variance-Scaled Perturbation for Text-Video Retrieval

GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation

FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed

OptScale: Probabilistic Optimality for Inference-time Scaling

EvoLM: In Search of Lost Language Model Training Dynamics

Synthetic Geology: Structural Geology Meets Deep Learning

Foundation Models in Medical Imaging: A Review and Outlook

FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning

OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model

Scaling Textual Gradients via Sampling-Based Momentum

MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration

In-context Language Learning for Endangered Languages in Speech Recognition

SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs

Preference Learning with Lie Detectors can Induce Honesty or Evasion

Anti-adversarial Learning: Desensitizing Prompts for Large Language Models

AdCare-VLM: Towards a Unified and Pre-aligned Latent Representation for Healthcare Video Understanding

Non-Uniform Class-Wise Coreset Selection for Vision Model Fine-tuning

ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

A Survey of Cross-domain Graph Learning: Progress and Future Directions

Availability-aware Sensor Fusion via Unified Canonical Space

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem

Personalized Image Generation for Recommendations Beyond Catalogs

IPAD: Inverse Prompt for AI Detection - A Robust and Interpretable LLM-Generated Text Detector

MoM: Linear Sequence Modeling with Mixture-of-Memories

Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Caching

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Higher-Order Transformers With Kronecker-Structured Attention

Optimizing Federated Learning by Entropy-Based Client Selection

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games

Generating Streamlining Constraints with Large Language Models

The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer

Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective

Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

NAIST Academic Travelogue Dataset

SemCo: Toward Semantic Coherent Visual Relationship Forecasting

Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

Requirements for Aligned, Dynamic Resolution of Conflicts in Operational Constraints

CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D

VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation

Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis

Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU

Created by

Haebom

저자

Giulio Malenza, Francesco Targa, Adriano Marques Garcia, Marco Aldinucci, Robert Birke

개요

본 논문은 인공지능(AI) 애플리케이션의 에너지 소비 문제를 해결하기 위한 하드웨어 및 소프트웨어 혁신에 대해 다룹니다. 특히, 개방형이고 확장 가능하며 에너지 효율적인 RISC-V 아키텍처 기반의 64코어 SOPHON SG2042에서 세 가지 주요 AI 프레임워크(PyTorch, ONNX Runtime, TensorFlow)를 사용한 딥러닝 추론 모델의 에너지 소비량을 종합적으로 벤치마킹 분석합니다. XNNPACK 백엔드를 사용하는 ONNX Runtime과 TensorFlow가 OpenBLAS 백엔드를 사용하는 PyTorch보다 에너지 소비량이 적다는 것을 밝혔습니다.

시사점, 한계점

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시사점:

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RISC-V 아키텍처 기반의 AI 시스템에서 프레임워크 선택에 따른 에너지 효율 차이를 정량적으로 제시.

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XNNPACK 백엔드의 에너지 효율성을 실험적으로 입증.

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에너지 효율적인 AI 시스템 개발을 위한 하드웨어 및 소프트웨어 최적화 방향 제시.

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한계점:

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SOPHON SG2042 하나의 특정 하드웨어 플랫폼에 대한 분석으로 일반화에 한계.

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분석된 딥러닝 모델과 프레임워크의 종류가 제한적.

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다양한 워크로드 및 모델 크기에 대한 에너지 소비 분석이 부족.

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소프트웨어 최적화의 세부적인 내용이 부족.

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