Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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AI/ML in 3GPP 5G Advanced -- Services and Architecture

CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration

Concept-Guided Backdoor Attack on Vision Language Models

On the Holographic Geometry of Deterministic Computation

Decoding inner speech with an end-to-end brain-to-text neural interface

Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation

MindEval: Benchmarking Language Models on Multi-turn Mental Health Support

LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling

Data-Augmented Deep Learning for Downhole Depth Sensing and Field Validation

Chinese Discharge Drug Recommendation in Metabolic Diseases with Large Language Models

Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting

Reinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives

TempoControl: Temporal Attention Guidance for Text-to-Video Models

AortaDiff: A Unified Multitask Diffusion Framework For Contrast-Free AAA Imaging

SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction

The AI Productivity Index (APEX)

Uncovering Grounding IDs: How External Cues Shape Multimodal Binding

Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization

V-CECE: Visual Counterfactual Explanations via Conceptual Edits

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios

HARP: Hallucination Detection via Reasoning Subspace Projection

Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders

ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

A Survey on Diffusion Language Models

CodeNER: Code Prompting for Named Entity Recognition

ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation

VERIRAG: A Post-Retrieval Auditing of Scientific Study Summaries

SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models

LittleBit: Ultra Low-Bit Quantization via Latent Factorization

Real-Time Execution of Action Chunking Flow Policies

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents

Enhancing SPARQL Query Rewriting for Complex Ontology Alignments

Exploring Ordinal Bias in Action Recognition for Instructional Videos

Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation

Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software

SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models

Large Language Models: An Applied Econometric Framework

Edge-Only Universal Adversarial Attacks in Distributed Learning

Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection

Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action Recognition

A Scene-aware Models Adaptation Scheme for Cross-scene Online Inference on Mobile Devices

Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm

GTM: Simulating the World of Tools for AI Agents

Self-Transparency Failures in Expert-Persona LLMs: How Instruction-Following Overrides Honesty

Learning the Value of Value Learning

OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe

ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering

Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models

Generalized Parallel Scaling with Interdependent Generations

SOCK: A Benchmark for Measuring Self-Replication in Large Language Models

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems

IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks

FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes

Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning

Rolling in the deep of cognitive and AI biases

Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms

Training-Time Action Conditioning for Efficient Real-Time Chunking

Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity

AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

Trusted AI Agents in the Cloud

Impugan: Learning Conditional Generative Models for Robust Data Imputation

Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception

World Models That Know When They Don't Know: Controllable Video Generation with Calibrated Uncertainty

Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures

Neural Coherence : Find higher performance to out-of-distribution tasks from few samples

Sparse Attention Post-Training for Mechanistic Interpretability

Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework

NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation

Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning

Approximation of Box Decomposition Algorithm for Fast Hypervolume-Based Multi-Objective Optimization

Probing the effectiveness of World Models for Spatial Reasoning through Test-time Scaling

3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering

Mechanistic Interpretability of Antibody Language Models Using SAEs

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Efficient Text Classification with Conformal In-Context Learning

Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias

Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning

Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domains

Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

Feasibility of AI-Assisted Programming for End-User Development

Grounded Multilingual Medical Reasoning for Question Answering with Large Language Models

Modular Jets for Supervised Pipelines: Diagnosing Mirage vs Identifiability

A Comprehensive Framework for Automated Quality Control in the Automotive Industry

AIvailable: A Software-Defined Architecture for LLM-as-a-Service on Heterogeneous and Legacy GPUs

Created by

Haebom

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저자

Pedro Antunes, Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Luis Frazao, Nuno Costa, Antonio Pereira

개요

본 논문은 대규모 언어 모델(LLM) 추론 시스템의 필요성이 증가함에 따라, 자원 제약적인 환경에서도 LLM을 실행할 수 있는 저비용, 고가용성 LLM-as-a-Service(LLMaaS) 플랫폼인 AIvailable을 소개한다. AIvailable은 소프트웨어 정의 접근 방식을 사용하여 NVIDIA 및 AMD 장치를 포함한 이기종 및 레거시 GPU 노드에서 LLM을 실행하며, 각 노드의 VRAM을 최대한 활용하는 데 중점을 둔다. AIvailable은 CPU 폴백 없이 GPU 가속 추론을 수행하며, 단일 논리 단위를 통해 배포된 모든 LLM과 원활하게 상호 작용할 수 있는 통합 클라이언트 인터페이스를 제공한다. AIvailable은 사용자 접근을 위한 클라이언트 인터페이스, 보안 요청 라우팅 및 부하 분산을 위한 서비스 프런트엔드, 오케스트레이션, 배포 및 모니터링을 위한 SDAI 컨트롤러, 워크로드를 실행하는 이기종 GPU 노드의 서비스 백엔드로 구성된다. GPU 특정 세부 정보를 추상화하고 모델의 동적이고 VRAM 인지 할당 및 재할당을 제공함으로써, AIvailable은 효율적인 자원 사용과 장애 또는 워크로드 변동에 대한 복원력을 보장한다. 이 플랫폼은 학술 연구실, 사기업 및 기타 자원 제약적인 조직을 대상으로 하며, 레거시 GPU를 재사용하여 생성형 AI의 민주화를 돕기 위해 다양한 오픈 LLM을 지원한다.

시사점, 한계점

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시사점:

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이기종 GPU 환경에서 LLM 추론을 가능하게 하여 자원 활용도를 극대화함.

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저비용, 고가용성 LLMaaS 플랫폼으로, 접근성이 높은 LLM 환경을 제공함.

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레거시 GPU를 활용하여 생성형 AI의 접근성을 높이는 데 기여함.

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GPU 특정 세부 정보를 추상화하여 사용 편의성을 높임.

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장애 및 워크로드 변동에 대한 복원력을 제공함.

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한계점:

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논문에서 구체적인 성능 지표나 성능 비교에 대한 정보가 부족함.

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구현 세부 사항이나 시스템 아키텍처에 대한 깊이 있는 설명이 부족함.

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특정 LLM 모델에 대한 성능 최적화가 필요한지에 대한 언급이 없음.

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보안 측면에서의 구체적인 구현이나 고려 사항에 대한 정보가 부족함.

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확장성 및 대규모 트래픽 처리에 대한 정보가 부족함.

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