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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.

Photonic Fabric Platform for AI Accelerators

Achieving Robust Channel Estimation Neural Networks by Designed Training Data

Can Mental Imagery Improve the Thinking Capabilities of AI Systems?

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training

Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling

A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n

HMID-Net: An Exploration of Masked Image Modeling and Knowledge Distillation in Hyperbolic Space

Synchronizing Task Behavior: Aligning Multiple Tasks during Test-Time Training

Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis

Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning

Learning Software Bug Reports: A Systematic Literature Review

Rethinking Data Protection in the (Generative) Artificial Intelligence Era

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving

"Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision People

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration

FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization

Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models

Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions

DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

Draft-based Approximate Inference for LLMs

Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels

Adversarial bandit optimization for approximately linear functions

Know Or Not: a library for evaluating out-of-knowledge base robustness

Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI

DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization

CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

Return Capping: Sample-Efficient CVaR Policy Gradient Optimisation

AnyTSR: Any-Scale Thermal Super-Resolution for UAV

Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis

Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

The Dual-Route Model of Induction

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

SWI: Speaking with Intent in Large Language Models

A Study of LLMs' Preferences for Libraries and Programming Languages

TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data

Sampling Decisions

Federated Continual Instruction Tuning

Fine-Tuning Diffusion Generative Models via Rich Preference Optimization

BriLLM: Brain-inspired Large Language Model

Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective

RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity

DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability

Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning

OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting

Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs

Attend or Perish: Benchmarking Attention in Algorithmic Reasoning

Can Optical Denoising Clean Sonar Images? A Benchmark and Fusion Approach

Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery

Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents

Detecting Benchmark Contamination Through Watermarking

MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation

Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

Analyze the Neurons, not the Embeddings: Understanding When and Where LLM Representations Align with Humans

MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs

An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

Layerwise Recall and the Geometry of Interwoven Knowledge in LLMs

Learning in Strategic Queuing Systems with Small Buffers

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

Understanding the Design Decisions of Retrieval-Augmented Generation Systems

DOGR: Towards Versatile Visual Document Grounding and Referring

Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking

DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation

PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding

AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization

Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications

FlexiTex: Enhancing Texture Generation via Visual Guidance

ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions

The unknotting number, hard unknot diagrams, and reinforcement learning

Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation

Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

CVPT: Cross Visual Prompt Tuning

Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models

Stimulating Imagination: Towards General-purpose "Something Something Placement"

Why Does New Knowledge Create Messy Ripple Effects in LLMs?

A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems

How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning

Towards the Next Frontier in Speech Representation Learning Using Disentanglement

Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles

Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences

Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View

OCK: Unsupervised Dynamic Video Prediction with Object-Centric Kinematics

Benchmarking Mobile Device Control Agents across Diverse Configurations

Photonic Fabric Platform for AI Accelerators

Created by

Haebom

저자

Jing Ding, Trung Diep

개요

본 논문은 저지연, 고대역폭, 저 에너지 소비를 제공하는 광자 기반 스위치 및 메모리 서브시스템인 Photonic Fabric™과 Photonic Fabric Appliance™ (PFA)를 제시한다. 고대역폭 HBM3E 메모리, 온모듈 광 스위치, 외부 DDR5를 2.5D 전기광학 시스템 인 패키지에 통합하여 최대 32TB의 공유 메모리와 115Tbps의 전대역 디지털 스위칭을 제공한다. Photonic Fabric™은 분산 AI 학습 및 추론에서 병렬 처리 전략을 더 효율적으로 실행할 수 있도록 한다. 기존 XPU 가속기 설계의 고정된 메모리-연산 비율을 제한하는 실리콘 면적 제약을 해결한다. XPU의 로컬 HBM 스택을 Photonic Fabric에 연결되는 칩렛으로 교체하여 메모리 용량과 대역폭을 확장한다. NVIDIA H100 및 H200 시스템에서 검증된 경량 분석 시뮬레이터 CelestiSim을 사용하여 GPU 코어 설계 변경 없이 PFA에서 LLM 추론 성능 및 에너지 절약 효과를 평가했다. 시뮬레이션 결과, 405B 매개변수 LLM 추론에서 최대 3.66배의 처리량 및 1.40배의 지연 시간 개선, 1T 매개변수에서 최대 7.04배의 처리량 및 1.41배의 지연 시간 개선, 모든 LLM 학습 시나리오에서 데이터 이동 에너지 소비 60-90% 절감을 보였다. NVIDIA GPU에 대한 결과이지만, 동일한 메모리-연산 제약을 가진 다른 AI 가속기 설계(XPU)에도 유사하게 적용 가능하다.

시사점, 한계점

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시사점:

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고정된 메모리-연산 비율의 한계를 극복하는 새로운 광자 기반 아키텍처 제시

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LLM 추론 및 학습 성능과 에너지 효율을 크게 향상시킬 가능성 제시 (최대 7배 이상의 처리량 향상 및 60-90% 에너지 절약)

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다양한 XPU 아키텍처에 적용 가능성

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경량 분석 시뮬레이터 CelestiSim을 통한 효율적인 성능 평가

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한계점:

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현재는 시뮬레이션 결과에 기반하며, 실제 구현 및 검증이 필요함.

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PFA의 실제 구현 비용 및 복잡도에 대한 분석 부족.

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다양한 XPU 아키텍처에 대한 일반화 가능성을 실제로 검증해야 함.

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CelestiSim 시뮬레이터의 정확도 및 한계에 대한 추가적인 검토 필요.

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