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.

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

DeepEyesV2: Toward Agentic Multimodal Model

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

Addressing divergent representations from causal interventions on neural networks

Alternative Fairness and Accuracy Optimization in Criminal Justice

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

Conversational Collective Intelligence (CCI) using Hyperchat AI in a Real-world Forecasting Task

Explaining Human Choice Probabilities with Simple Vector Representations

Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control

GraphCliff: Short-Long Range Gating for Subtle Differences but Critical Changes

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation

Reasoning Planning for Language Models

Aligning Brain Signals with Multimodal Speech and Vision Embeddings

Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models

Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets

Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs

Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees

Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models

Meronymic Ontology Extraction via Large Language Models

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops

The Markovian Thinker

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Pure Vision Language Action (VLA) Models: A Comprehensive Survey

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning

Normality and the Turing Test

GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach

ANO : Faster is Better in Noisy Landscape

Generative Medical Event Models Improve with Scale

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment

Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning

Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor

Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning

RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection

Controllable Hybrid Captioner for Improved Long-form Video Understanding

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning

When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

Evaluating LLM-based Workflows for Switched-Mode Power Supply Design

Monitoring Risks in Test-Time Adaptation

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery

CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

GPT, But Backwards: Exactly Inverting Language Model Outputs

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

ReCode: Updating Code API Knowledge with Reinforcement Learning

Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

Sekai: A Video Dataset towards World Exploration

Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation

A Fairness-Aware Strategy for B5G Physical-layer Security Leveraging Reconfigurable Intelligent Surfaces

Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

GRAM: Spatial general-purpose audio representation models for real-world applications

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications

VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video

When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering

Rethinking Text-based Protein Understanding: Retrieval or LLM?

Language Model Distillation: A Temporal Difference Imitation Learning Perspective

TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute

DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations

When Bias Helps Learning: Bridging Initial Prejudice and Trainability

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

Video CLIP Model for Multi-View Echocardiography Interpretation

Quantum Doubly Stochastic Transformers

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

Simulator Ensembles for Trustworthy Autonomous Driving Testing

Continual Pre-training of MoEs: How robust is your router?

Adaptive Orchestration for Large-Scale Inference on Heterogeneous Accelerator Systems Balancing Cost, Performance, and Resilience

Created by

Haebom

저자

Yahav Biran, Imry Kissos

개요

본 논문은 생성형 AI 워크로드의 급증에 따라 GPU와 전문화된 가속기 모두를 유연하게 활용하면서 운영 비용을 절감할 수 있는 확장 가능한 추론 시스템에 대한 필요성을 제기합니다. 본 논문에서는 실시간 비용 및 용량 신호에 따라 이기종 가속기 간에 요청을 적응적으로 할당하는 하드웨어 비종속 제어 루프를 제안합니다. 이 방법은 비용 최적화 모드와 용량 최적화 모드 간의 동적 전환을 통해 낮은 지연 시간과 높은 처리량을 유지하여 변동하는 가용성 하에서 고가의 컴퓨팅 리소스를 가장 효율적으로 사용할 수 있도록 합니다. Stable Diffusion 모델을 사용하여 평가한 결과, 이 프레임워크는 지연 시간 목표를 일관되게 충족하고, 용량 부족 시 트래픽을 자동으로 리디렉션하며, 가능한 경우 저렴한 가속기를 활용합니다. 이러한 결과는 소프트웨어 및 하드웨어 스택 전체에 걸친 피드백 기반 배포 전략을 통해 조직이 제한된 가속기 용량에 직면하여 탄력성을 유지하면서 생성형 AI 워크로드를 효율적으로 확장할 수 있음을 강조합니다.

시사점, 한계점

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시사점:

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실시간 비용 및 용량 신호를 기반으로 한 적응형 가속기 할당을 통해 생성형 AI 추론 시스템의 효율성 및 확장성을 향상시킬 수 있음을 보여줍니다.

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하드웨어 비종속적인 제어 루프를 통해 다양한 가속기를 유연하게 활용할 수 있는 가능성을 제시합니다.

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비용 최적화 및 용량 최적화 모드의 동적 전환을 통해 운영 비용을 절감하고 서비스 품질을 유지할 수 있는 전략을 제시합니다.

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Stable Diffusion 모델을 통한 실험 결과는 제안된 방법의 실효성을 입증합니다.

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한계점:

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Stable Diffusion 모델 하나만을 사용한 평가로, 다른 생성형 AI 모델이나 워크로드에 대한 일반화 가능성은 추가 연구가 필요합니다.

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제안된 제어 루프의 복잡성 및 구현의 어려움에 대한 논의가 부족합니다.

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다양한 가속기 환경에 대한 실험 및 평가가 제한적입니다.

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장기적인 운영 환경에서의 안정성 및 내구성에 대한 검증이 필요합니다.

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