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.

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

AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"

Created by

Haebom

저자

Weina Jin, Nicholas Vincent, Ghassan Hamarneh

개요

본 논문은 인공지능(AI) 개발의 목적("왜" AI를 개발하는가)에 대한 비판적 성찰이 부족함을 지적하며, AI에 대한 다양한 상상(visions, frames, mindsets)을 탐구한다. 현재 AI 커뮤니티의 주류적인 상상은 인간 대체 및 생산성 향상에 초점을 맞추는 단일 문화(monoculture)이며, 이러한 상상의 불공정한 가정을 비판적으로 검토한다. 저자는 AI 상상의 다양화를 촉구하며, 윤리적 가정을 처음부터 통합할 것을 제안한다. "정의로운 일을 위한 AI"라는 새로운 상상을 구축하는 과정을 보여주고, 의료 이미지 합성 작업에 적용하여 윤리적인 AI 개발을 위한 사례를 제시한다. 궁극적으로 AI 커뮤니티와 시민 사회 간의 비전 및 목적에 대한 비판적 대화를 촉진하고, 공공의 이익을 위한 다양하고 윤리적인 AI 개발을 위한 기술적 연구 및 옹호를 고무하고자 한다.

시사점, 한계점

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시사점:

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AI 개발의 목적에 대한 비판적 성찰의 중요성 강조

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AI 개발의 단일 문화(monoculture) 문제점 지적 및 다양화 필요성 제기

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윤리적 가정을 AI 개발 초기 단계부터 통합해야 함을 주장

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"정의로운 일을 위한 AI"와 같은 새로운 상상의 구축 및 적용 사례 제시

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AI 커뮤니티와 시민 사회 간의 대화 촉진 및 공공의 이익을 위한 AI 개발 필요성 강조

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한계점:

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제시된 "정의로운 일을 위한 AI" 상상의 구체적인 윤리적 기준 및 실현 가능성에 대한 추가적인 논의 필요

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다양한 AI 상상을 구축하고 평가하는 보다 구체적이고 실용적인 방법론 제시 부족

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단일 사례 연구(의료 이미지 합성)를 넘어 다양한 분야에 대한 적용 및 일반화 가능성에 대한 추가 연구 필요

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AI 상상의 다양화를 위한 구체적인 사회적, 제도적 전략 제시 부족

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