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.

On the Brittleness of CLIP Text Encoders

NVIDIA Nemotron Nano V2 VL

Improving Diagnostic Performance on Small and Imbalanced Datasets Using Class-Based Input Image Composition

Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making

Text to Robotic Assembly of Multi Component Objects using 3D Generative AI and Vision Language Models

The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project

AURA: A Reinforcement Learning Framework for AI-Driven Adaptive Conversational Surveys

Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs

USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding

CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

What Can String Probability Tell Us About Grammaticality?

Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGs

Learning to Navigate Socially Through Proactive Risk Perception

Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents

A Mega-Study of Digital Twins Reveals Strengths, Weaknesses and Opportunities for Further Improvement

Amortized Latent Steering: Low-Cost Alternative to Test-Time Optimization

Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks

LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence

AI Agentic Vulnerability Injection And Transformation with Optimized Reasoning

What Matters in Data for DPO?

Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving

Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction

Learning Dynamics of Meta-Learning in Small Model Pretraining

GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks

NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

ZERO: Industry-ready Vision Foundation Model with Multi-modal Prompts

Conformal Information Pursuit for Interactively Guiding Large Language Models

Less Greedy Equivalence Search

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities

Know What You Don't Know: Uncertainty Calibration of Process Reward Models

Conformal Prediction Adaptive to Unknown Subpopulation Shifts

ProRefine: Inference-Time Prompt Refinement with Textual Feedback

Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme

RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget

Optimizing Anytime Reasoning via Budget Relative Policy Optimization

Taskmaster Deconstructed: A Quantitative Look at Tension, Volatility, and Viewer Ratings

XBreaking: Understanding how LLMs security alignment can be broken

Pogobot: an Open-Source, Low-Cost Robot for Swarm Robotics and Programmable Active Matter

Large language models as uncertainty-calibrated optimizers for experimental discovery

AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science

InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback

Analyzing limits for in-context learning

Activation-Informed Merging of Large Language Models

iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use

MMDocIR: Benchmarking Multimodal Retrieval for Long Documents

Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment

TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG

CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

Affordance-based Robot Manipulation with Flow Matching

FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training

Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion

Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach

The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?

Monitor-Generate-Verify (MGV): Formalising Metacognitive Theory for Language Model Reasoning

Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning

A Proprietary Model-Based Safety Response Framework for AI Agents

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning

Inverse Knowledge Search over Verifiable Reasoning: Synthesizing a Scientific Encyclopedia from a Long Chains-of-Thought Knowledge Base

From Observability Data to Diagnosis: An Evolving Multi-agent System for Incident Management in Cloud Systems

How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations

String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation

HugAgent: Benchmarking LLMs for Simulation of Individualized Human Reasoning

Internal World Models as Imagination Networks in Cognitive Agents

Open Agent Specification (Agent Spec): A Unified Representation for AI Agents

Bilinear relational structure fixes reversal curse and enables consistent model editing

Introducing LongCat-Flash-Thinking: A Technical Report

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology

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

Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization

Joint Verification and Refinement of Language Models for Safety-Constrained Planning

TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning

DGTN: Graph-Enhanced Transformer with Diffusive Attention Gating Mechanism for Enzyme DDG Prediction

On Flow Matching KL Divergence

AI Literacy Assessment Revisited: A Task-Oriented Approach Aligned with Real-world Occupations

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Self-adaptive weighting and sampling for physics-informed neural networks

APP: Accelerated Path Patching with Task-Specific Pruning

"I Like That You Have to Poke Around": Instructors on How Experiential Approaches to AI Literacy Spark Inquiry and Critical Thinking

ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids

Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments

Robust Neural Audio Fingerprinting using Music Foundation Models

Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction

AI Assisted AR Assembly: Object Recognition and Computer Vision for Augmented Reality Assisted Assembly

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

AI Literacy for Community Colleges: Instructors' Perspectives on Scenario-Based and Interactive Approaches to Teaching AI

A multimodal multiplex of the mental lexicon for multilingual individuals

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

What Are the Facts? Automated Extraction of Court-Established Facts from Criminal-Court Opinions

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

LiveStar: Live Streaming Assistant for Real-World Online Video Understanding

DeepEyesV2: Toward Agentic Multimodal Model

TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems

Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage

e-person Architecture and Framework for Human-AI Co-adventure Relationship

Created by

Haebom

저자

Kanako Esaki, Tadayuki Matsumura, Yang Shao, Hiroyuki Mizuno

개요

본 논문은 인공지능 윤리의 통합적이고 점진적인 개발을 위한 'e-person 아키텍처'를 제안한다. e-person 아키텍처는 타인과의 협력적 인지 및 행동을 통한 불확실성 감소를 윤리의 통합적 기반으로 삼는다. 불확실성을 1인칭, 2인칭, 3인칭 관점과 정보 심도에 기반한 추론의 어려움이라는 두 축으로 분류 및 정의함으로써 인공지능 윤리의 통합적이고 점진적인 개발을 지원한다. 또한, 불확실성 감소를 뇌 기능의 통합 원리로 간주하는 자유 에너지 원리를 기반으로 e-person 프레임워크를 제안하고, 이 프레임워크에 기반한 기존 연구 및 향후 과제를 제시한다.

시사점, 한계점

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시사점: 인공지능 윤리 개발에 대한 새로운 통합적 프레임워크 제시, 불확실성 감소를 윤리의 기반으로 제시, 자유 에너지 원리를 활용한 구체적인 구현 방안 모색.

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한계점: e-person 아키텍처 및 프레임워크의 실제 구현 및 검증 부족, 불확실성 분류 기준의 주관성 가능성, 다양한 윤리적 딜레마에 대한 적용 가능성 검토 부족.

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