Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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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

Quantum Neural Network Restatement of Markov Jump Process

Created by

Haebom

저자

Z. Zarezadeh, N. Zarezadeh

개요

본 논문은 탐색적 데이터 분석의 어려움에도 불구하고, 비선형 동역학 시스템 모델링, 일반화 및 적응 가능성 덕분에 인공 신경망이 이론적 및 실제 응용 분야에서 큰 관심을 받고 있음을 다룹니다. 하지만 데이터 학습 및 예측을 위한 고유 구조 안정화에 있어 다양한 기저 확률 과정의 역할에 대한 논쟁이 여전히 존재합니다. 고차원 확률 분포로부터의 샘플링과 차원의 저주는 기계 지능 시스템의 이론적 및 수치적 연구에 있어 장애물이 되며, 시스템을 효율적으로 기술하고 연구하는 데 상당한 복잡성 장벽을 제공합니다. 본 연구는 양자 정보 이론적 관점에서 학습 이론의 추상적인 개념을 직접 다루고 기술하는 것이 유리한 후보임을 제시하며, 양자역학 시스템 관점에서 계산적으로 어려운 문제들의 설계, 적응 및 공식화 문제를 다룹니다. d-차원 가우시안 밀도의 공분산 행렬 추정 및 동역학 시스템에 대한 고유값 문제의 베이지안 해석을 통해 이러한 동역학의 미시적 기술을 추론 통계의 언어로 특징짓습니다.

시사점, 한계점

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시사점: 양자 정보 이론을 활용하여 고차원 데이터 분석 및 기계 학습 문제에 대한 새로운 접근법을 제시합니다. 특히, 공분산 행렬 추정과 베이지안 해석을 통해 동역학 시스템의 미시적 기술을 효율적으로 분석하는 방법을 제시합니다. 차원의 저주 문제 해결에 대한 새로운 가능성을 열어줍니다.

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한계점: 구체적인 알고리즘이나 실험 결과가 제시되지 않아 실제 적용 가능성에 대한 검증이 부족합니다. 양자 정보 이론의 도입이 고차원 데이터 문제에 얼마나 효과적인지에 대한 추가적인 연구가 필요합니다. 논문에서 제시된 방법론의 계산적 복잡도에 대한 분석이 부족합니다.

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