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.

M3DR: Towards Universal Multilingual Multimodal Document Retrieval

Physics-Driven Learning Framework for Tomographic Tactile Sensing

NAS-LoRA: Empowering Parameter-Efficient Fine-Tuning for Visual Foundation Models with Searchable Adaptation

Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms

AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation

Text-Printed Image: Bridging the Image-Text Modality Gap for Text-centric Training of Large Vision-Language Models

Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study

Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles

GalaxyDiT: Efficient Video Generation with Guidance Alignment and Adaptive Proxy in Diffusion Transformers

Multi-Aspect Knowledge-Enhanced Medical Vision-Language Pretraining with Multi-Agent Data Generation

World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents

Better World Models Can Lead to Better Post-Training Performance

VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing

UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs

FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting

HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restoration

Idea-Gated Transformers: Enforcing Semantic Coherence via Differentiable Vocabulary Pruning

ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography

Single-Round Scalable Analytic Federated Learning

Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs

NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction

Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates

Robust Tabular Foundation Models

HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction

BlendedNet++: A Large-Scale Blended Wing Body Aerodynamics Dataset and Benchmark

Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases

PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery

Learning Network Sheaves for AI-native Semantic Communication

SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation

Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization

Plantain: Plan-Answer Interleaved Reasoning

Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping

Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra

Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models

Lost in Modality: Evaluating the Effectiveness of Text-Based Membership Inference Attacks on Large Multimodal Models

Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability

PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer

The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing

Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study

Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration

ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks

QGShap: Quantum Acceleration for Faithful GNN Explanations

Community Quality and Influence Maximization: An Empirical Study

Password-Activated Shutdown Protocols for Misaligned Frontier Agents

When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

Alleviating Choice Supportive Bias in LLM with Reasoning Dependency Generation

AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations

Irresponsible AI: big tech's influence on AI research and associated impacts

Will Power Return to the Clouds? From Divine Authority to GenAI Authority

Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem

PretopoMD: Pretopology-based Mixed Data Hierarchical Clustering

Mixed Data Clustering Survey and Challenges

Hierarchical clustering of complex energy systems using pretopology

Echoes of AI Harms: A Human-LLM Synergistic Framework for Bias-Driven Harm Anticipation

Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation

Optimizing Life Sciences Agents in Real-Time using Reinforcement Learning

A note on the impossibility of conditional PAC-efficient reasoning in large language models

Delta Sampling: Data-Free Knowledge Transfer Across Diffusion Models

Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins

Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research

Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation

Entropy-Based Measurement of Value Drift and Alignment Work in Large Language Models

AI-Driven Document Redaction in UK Public Authorities: Implementation Gaps, Regulatory Challenges, and the Human Oversight Imperative

Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol

Autonomous Agents and Policy Compliance: A Framework for Reasoning About Penalties

A Hierarchical Tree-based approach for creating Configurable and Static Deep Research Agent (Static-DRA)

Omni-AutoThink: Adaptive Multimodal Reasoning via Reinforcement Learning

RoCo: Role-Based LLMs Collaboration for Automatic Heuristic Design

MemVerse: Multimodal Memory for Lifelong Learning Agents

DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization

EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths

Reason-Plan-ReAct: A Reasoner-Planner Supervising a ReAct Executor for Complex Enterprise Tasks

PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks

Multi-Agent Reinforcement Learning with Communication-Constrained Priors

Multimodal Reinforcement Learning with Agentic Verifier for AI Agents

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Prior preferences in active inference agents: soft, hard, and goal shaping

When Do Symbolic Solvers Enhance Reasoning in Large Language Models?

Beyond the Black Box: A Cognitive Architecture for Explainable and Aligned AI

Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation

Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning

Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning

Beyond Greenfield: The D3 Framework for AI-Driven Productivity in Brownfield Engineering

Look, Recite, Then Answer: Enhancing VLM Performance via Self-Generated Knowledge Hints

HBLLM: A Haar-Based Approach for Accurate Structured 1-Bit Quantized LLMs

Limitations of Using Identical Distributions for Training and Testing When Learning Boolean Functions

BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models

MIMIC-MJX: Neuromechanical Emulation of Animal Behavior

Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives

PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM

Generative AI in Sociological Research: State of the Discipline

MRI Super-Resolution with Deep Learning: A Comprehensive Survey

Foundations of Quantum Granular Computing with Effect-Based Granules, Algebraic Properties and Reference Architectures

Created by

Haebom

Category

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저자

Oscar Montiel Ross

개요

본 논문은 고전적 입자 컴퓨팅을 양자 영역으로 확장하는 양자 입자 컴퓨팅(QGC)의 기초를 개발합니다. 양자 입자는 유한 차원 힐베르트 공간에 대한 효과로 모델링되며, 입자 멤버십은 Born 확률로 주어집니다. 이러한 연산자 이론적 관점은 예리한(사영) 및 부드러운(비사영) 입자에 대한 공통 언어를 제공하며, 입자화를 양자 정보 이론의 표준 형식론에 직접 임베딩합니다. 본 논문은 효과 기반 양자 입자에 대한 기본 결과를 확립합니다. 여기에는 정규화 및 단조성 속성, 가환족으로부터 부울 아일랜드의 출현, Luders 업데이트 하에서의 입자 정제, Heisenberg 그림에서 부수 채널을 통한 양자 채널 하에서의 입자 진화 등이 포함됩니다. QGC는 Helstrom 최소 오류 측정을 실현하는 효과 연산자를 Helstrom 유형 결정 입자, 즉 Bayes 최적 결정 영역의 부드러운 양자 대응물로 해석하여 양자 감지 및 추정 이론과 연결됩니다. 이러한 결과를 바탕으로, 양자 입자를 정의, 학습 및 고전적 구성 요소와 통합하여 가까운 미래의 양자 하드웨어와 호환성을 유지하는 방법을 구체화하는 세 가지 참조 아키텍처를 갖춘 양자 입자 결정 시스템(QGDS)을 소개합니다. 큐비트 입자화, 두 큐비트 패리티 효과, Helstrom 스타일의 소프트 결정에 대한 사례 연구는 QGC가 비가환성, 문맥성 및 얽힘을 활용하면서 퍼지 유사 등급 멤버십과 부드러운 결정 경계를 어떻게 재현하는지 보여줍니다. 따라서 이 프레임워크는 양자 정보 처리, 입자 추론 및 지능형 시스템에서 연산자 값 입자에 대한 통일되고 수학적으로 근거한 기반을 제공합니다.

시사점, 한계점

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시사점:

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고전적 입자 컴퓨팅을 양자 영역으로 확장하는 QGC의 새로운 프레임워크 제시.

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양자 입자를 효과 연산자를 사용하여 모델링하여 양자 정보 이론과 통합.

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QGC의 기본 속성(정규화, 단조성 등) 및 양자 채널을 통한 입자 진화 등 제시.

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Helstrom 최소 오류 측정을 이용한 양자 감지 및 추정 이론과의 연결.

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QGDS 아키텍처를 통해 양자 입자 활용 방법 제시.

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큐비트 입자화 등 사례 연구를 통해 퍼지 유사 멤버십 및 부드러운 결정 경계 재현.

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양자 정보 처리, 입자 추론, 지능형 시스템 분야에 기여.

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한계점:

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구체적인 구현 및 실험 결과 부족.

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가까운 미래의 양자 하드웨어 호환성 관련 추가 연구 필요.

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QGDS 아키텍처의 성능 및 효율성에 대한 추가 분석 필요.

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다른 양자 알고리즘 및 시스템과의 통합 가능성 탐구 필요.

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이론적 기반에 대한 심층적인 증명 및 검증 필요.

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