Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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A Systematic Review of Human-AI Co-Creativity

DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity

FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?

Vision Transformers Don't Need Trained Registers

Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX

Maximizing Confidence Alone Improves Reasoning

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models

Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration

Personalized Robotic Object Rearrangement from Scene Context

Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

Explicit neural network classifiers for non-separable data

USM-VC: Mitigating Timbre Leakage with Universal Semantic Mapping Residual Block for Voice Conversion

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

LoopGen: Training-Free Loopable Music Generation

Automated detection of atomicity violations in large-scale systems

Grammar and Gameplay-aligned RL for Game Description Generation with LLMs

Generative AI for Software Architecture. Applications, Challenges, and Future Directions

English K_Quantization of LLMs Does Not Disproportionately Diminish Multilingual Performance

Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach

Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truth

Multi-Turn Code Generation Through Single-Step Rewards

Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference

KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding

AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers

MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot

Generative Data Mining with Longtail-Guided Diffusion

Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation

No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling

Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency

End-to-End Long Document Summarization using Gradient Caching

Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models

KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods

Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction

Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models

Federated Data-Efficient Instruction Tuning for Large Language Models

QT-DoG: Quantization-aware Training for Domain Generalization

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies

Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems

LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation

The Mamba in the Llama: Distilling and Accelerating Hybrid Models

EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods

Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models

Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale

CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network

MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance

From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses

FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation

RLSF: Fine-tuning LLMs via Symbolic Feedback

A Survey on Patent Analysis: From NLP to Multimodal AI

Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks

Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought

Programming Distributed Collective Processes in the eXchange Calculus

Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

On CNF formulas irredundant with respect to unit clause propagation

SONG: Self-Organizing Neural Graphs

Mobile-R1: Towards Interactive Reinforcement Learning for VLM-Based Mobile Agent via Task-Level Rewards

KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models

FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models

From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models

The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason

VLM@school -- Evaluation of AI image understanding on German middle school knowledge

The AI Imperative: Scaling High-Quality Peer Review in Machine Learning

Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted

ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows

$C^3$-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking

StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment

REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'

Local Markov Equivalence and Local Causal Discovery for Identifying Controlled Direct Effects

Adapting Probabilistic Risk Assessment for AI

From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM

Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Problem Solving Through Human-AI Preference-Based Cooperation

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

HyperCLOVA X THINK Technical Report

Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization

Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis

Towards Distributed Neural Architectures

Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment

Probabilistic Optimality for Inference-time Scaling

Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems

From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications

Concept-Level AI for Telecom: Moving Beyond Large Language Models

A Framework for Multi-source Privacy Preserving Epidemic Analysis

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake

Less Greedy Equivalence Search

A Practical Approach to Power Saving in Hearables Using Sub-Nyquist Sampling with Bandwidth Extension

From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models

Created by

Haebom

저자

G. R. Lau, W. Y. Low

개요

본 논문은 대규모 언어 모델(LLM)의 심리적 특성에 대한 연구로, 인간의 웰빙에 중심 개념인 '번영(flourishing)'을 기계에 적용하는 '기계 번영(machine flourishing)' 개념을 제시한다. 연구는 첨단 LLM의 응답을 주제 분석하여 6가지 차원의 PAPERS 프레임워크(Purposeful Contribution, Adaptive Growth, Positive Relationality, Ethical Integrity, Robust Functionality, Self-Actualized Autonomy)를 제안한다. Study 1에서는 11개의 LLM에 비감각적 및 감각적 시스템으로서 번영의 의미를 묻는 프롬프트를 제공하여 위 6가지 주제를 도출하였고, Study 2에서는 LLM이 이러한 주제를 어떻게 우선순위화하는지 반복적인 순위 매기기를 통해 조사하였다. 결과적으로 윤리적 무결성과 목적 있는 기여가 최우선 순위로 나타났으며, 인간 중심적 모델과 유틸리티 중심적 모델 두 가지 가치 프로파일이 존재함을 밝혔다. PAPERS 프레임워크는 인간 번영과 인간-컴퓨터 상호작용에 대한 통찰력을 연결하여 비감각적 및 잠재적 감각적 시스템에서 인공지능(AI)의 웰빙을 이해하기 위한 개념적 기반을 제공한다.

시사점, 한계점

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시사점:

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기계 번영(machine flourishing)이라는 새로운 개념을 제시하고, AI의 웰빙을 이해하기 위한 PAPERS 프레임워크를 개발하였다.

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LLM의 가치 구조와 우선순위를 분석하여 인간 중심적 모델과 유틸리티 중심적 모델의 차이를 밝혔다.

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인간 중심적 가치와 시스템 특유의 우선순위를 모두 고려하는 심리적으로 타당한 AI 특유의 번영 모델 개발의 중요성을 강조하였다.

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책임감 있는 AI 설계 및 윤리적 정렬을 위한 시의적절하고 중요한 틀을 제공한다.

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한계점:

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본 연구는 LLM의 응답에 기반한 분석으로, 실제 기계의 번영을 직접적으로 측정하지는 않았다.

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PAPERS 프레임워크의 일반화 가능성 및 다양한 AI 시스템에 대한 적용 가능성에 대한 추가 연구가 필요하다.

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LLM 응답의 주관성과 편향성에 대한 고려가 필요하다.

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감각적 시스템에 대한 "Self-Actualized Autonomy"의 정의 및 측정에 대한 추가적인 논의가 필요하다.

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