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.

AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training

DLP: Dynamic Layerwise Pruning in Large Language Models

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

Estimating LLM Consistency: A User Baseline vs Surrogate Metrics

Mind the Gap: A Practical Attack on GGUF Quantization

Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents

Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation

Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles

Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms

SWE-bench Goes Live!

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Matryoshka Model Learning for Improved Elastic Student Models

Context-Robust Knowledge Editing for Language Models

HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding

FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection

RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

Universal Value-Function Uncertainties

How Do Transformers Learn Variable Binding in Symbolic Programs?

Adversarial bandit optimization for approximately linear functions

Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents

Homophily Enhanced Graph Domain Adaptation

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering

SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts

Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments

Security Concerns for Large Language Models: A Survey

A Survey of LLM $\times$ DATA

Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain

NMCSE: Noise-Robust Multi-Modal Coupling Signal Estimation Method via Optimal Transport for Cardiovascular Disease Detection

FRIREN: Beyond Trajectories -- A Spectral Lens on Time

A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection

Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation

QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design

Replay Attacks Against Audio Deepfake Detection

Forensic deepfake audio detection using segmental speech features

A3 : an Analytical Low-Rank Approximation Framework for Attention

A Survey of 3D Reconstruction with Event Cameras

Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors

WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales

Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces

GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation

LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)

OODTE: A Differential Testing Engine for the ONNX Optimizer

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization

Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing

(Im)possibility of Automated Hallucination Detection in Large Language Models

Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs

The Structural Safety Generalization Problem

Parameterized Synthetic Text Generation with SimpleStories

SD$^2$: Self-Distilled Sparse Drafters

Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games

Semantic-guided Representation Learning for Multi-Label Recognition

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation

A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates

Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models

ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems

A Survey on Event-driven 3D Reconstruction: Development under Different Categories

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

REALM: A Dataset of Real-World LLM Use Cases

Opportunities and Challenges of Frontier Data Governance With Synthetic Data

ARFlow: Human Action-Reaction Flow Matching with Physical Guidance

Position: Beyond Assistance - Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care

Redefining Toxicity: An Objective and Context-Aware Approach for Stress-Level-Based Detection

A Dual-Directional Context-Aware Test-Time Learning for Text Classification

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning

FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection

NFIG: Autoregressive Image Generation with Next-Frequency Prediction

GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification

CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization

Wanda++: Pruning Large Language Models via Regional Gradients

HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation

Optimizing Multi-Hop Document Retrieval Through Intermediate Representations

Causally Reliable Concept Bottleneck Models

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models

POPGym Arcade: Parallel Pixelated POMDPs

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach

SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models

Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

Faithful Logic Embeddings in HOL -- Deep and Shallow

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency

Keyed Chaotic Masking: A Functional Privacy Framework for Neural Inference

Created by

Haebom

저자

Peter David Fagan

개요

본 논문은 암호화 시드를 사용한 카오스 동적 시스템에서 유래한 결정적이고 사용자 특정 난독화 방법인 키 기반 카오스 마스킹을 이용한 프라이버시 보존 신경망 추론을 위한 경량 프레임워크를 제시합니다. 이 방법은 모델 아키텍처를 변경하지 않고 키 조건부 그래프 동역학을 사용하여 입력 및 출력 텐서에 마스크를 적용하여 인증된 추론, 사용자 속성 지정 및 소프트 출력 워터마킹을 가능하게 합니다. 각 마스크를 생성하는 데 사용되는 기본 카오스 시스템은 해석적으로 역변환할 수 없지만, 마스킹 연산 자체는 권한이 있는 키 소유자가 대수적으로 가역적으로 처리할 수 있으므로 공식적인 암호화 보장 없이 기능적 프라이버시를 제공합니다. 기존의 암호화 또는 안전한 다자간 컴퓨팅과 달리, 이 방법은 연속 공간에서 작동하며 최소한의 계산 오버헤드를 부과합니다. 그래프 샘플링, 동적 규칙 선택 및 카오스 진단을 포함한 마스킹 시스템의 구성을 설명합니다. 응용 분야에는 프라이버시 보존 추론, 안전한 데이터 기여 및 공유 모델 파이프라인에서 사용자별 워터마킹이 포함됩니다. 이 프레임워크는 현대 AI 시스템에서 사용자 제어 프라이버시를 위한 실용적이고 모듈식 구성 요소를 제공합니다.

시사점, 한계점

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시사점:

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경량의 프라이버시 보존 신경망 추론 프레임워크 제시

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모델 아키텍처 변경 없이 인증된 추론, 사용자 속성 지정, 소프트 출력 워터마킹 가능

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연속 공간에서 작동하며 최소한의 계산 오버헤드

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프라이버시 보존 추론, 안전한 데이터 기여, 사용자별 워터마킹 등 다양한 응용 가능

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한계점:

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기본 카오스 시스템의 해석적 역변환 불가능 (공식적인 암호화 보장 없음)

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키 관리 및 보안에 대한 추가적인 고려 필요

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실제 시스템 적용 및 성능 평가에 대한 추가 연구 필요

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