Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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Structure Transfer: an Inference-Based Calculus for the Transformation of Representations

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification

AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation

Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks

DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression

Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation

First Order Model-Based RL through Decoupled Backpropagation

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms

Beacon: Post-Training Quantization with Integrated Grid Selection

Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?

Vectorized Attention with Learnable Encoding for Quantum Transformer

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

MultiGen: Child-Friendly Multilingual Speech Generator with LLMs

StreetViewAI: Making Street View Accessible Using Context-Aware Multimodal AI

Street-Level AI: Are Large Language Models Ready for Real-World Judgments?

The KG-ER Conceptual Schema Language

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Conditional Video Generation for High-Efficiency Video Compression

TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP

Demographic-aware fine-grained classification of pediatric wrist fractures

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values

Stochastic Parameter Decomposition

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

MiniCPM4: Ultra-Efficient LLMs on End Devices

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling

How Can I Publish My LLM Benchmark Without Giving the True Answers Away?

Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks

Transferable Mask Transformer: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation

RBT4DNN: Requirements-based Testing of Neural Networks

Robust Offline Imitation Learning Through State-level Trajectory Stitching

Beyond holography: the entropic quantum gravity foundations of image processing

KNighter: Transforming Static Analysis with LLM-Synthesized Checkers

FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response

CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection

Rapid Word Learning Through Meta In-Context Learning

Image Embedding Sampling Method for Diverse Captioning

Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Extended Histogram-based Outlier Score (EHBOS)

A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

Breaking the Context Bottleneck on Long Time Series Forecasting

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision

ACING: Actor-Critic for Instruction Learning in Black-Box LLMs

Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance

Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames

AutoPETIII: The Tracer Frontier. What Frontier?

Long Input Sequence Network for Long Time Series Forecasting

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers

MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming

Diffusion on language model encodings for protein sequence generation

Style Transfer to Calvin and Hobbes comics using Stable Diffusion

Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems

Plan Verification for LLM-Based Embodied Task Completion Agents

EigenBench: A Comparative Behavioral Measure of Value Alignment

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Extending FKG.in: Towards a Food Claim Traceability Network

DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning

Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation

CP-Bench: Evaluating Large Language Models for Constraint Modelling

Axiomatics of Restricted Choices by Linear Orders of Sets with Minimum as Fallback

DMN-Guided Prompting: A Framework for Controlling LLM Behavior

Computational Basis of LLM's Decision Making in Social Simulation

Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers

Enhancing FKG.in: automating Indian food composition analysis

WASP: A Weight-Space Approach to Detecting Learned Spuriousness

Transferable Belief Model on Quantum Circuits

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions

Intelligence Primer

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset

Delta Activations: A Representation for Finetuned Large Language Models

DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation

Towards a Unified View of Large Language Model Post-Training

No Thoughts Just AI: Biased LLM Recommendations Limit Human Agency in Resume Screening

IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation

SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds

From Editor to Dense Geometry Estimator

Decoupled Entity Representation Learning for Pinterest Ads Ranking

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning

Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification

An Empirical Study of Vulnerabilities in Python Packages and Their Detection

How many patients could we save with LLM priors?

Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding

MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds

YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components

Attention as an Adaptive Filter

TAGAL: Tabular Data Generation using Agentic LLM Methods

Enhancing Technical Documents Retrieval for RAG

Transferable Belief Model on Quantum Circuits

Created by

Haebom

저자

Qianli Zhou, Hao Luo, Lipeng Pan, Yong Deng, Eloi Bosse

개요

본 논문은 양자 회로 상에서 전이 가능한 신념 모델(Transferable Belief Model, TBM)을 구현하여, 양자 컴퓨팅 프레임워크 내에서 베이지안 접근 방식보다 더 간결하고 효율적인 대안임을 보여줍니다. TBM은 불확실하고 불완전한 환경에서 추론 및 의사결정을 가능하게 하는 뎀스터-셰이퍼 이론의 의미론적 해석으로, 불확실한 증언을 처리하는 독특한 의미론을 제공합니다. 기존의 계산 복잡성 문제에도 불구하고, 양자 컴퓨팅의 고유한 특성을 활용하여 새로운 신념 전이 접근 방식을 제안하며, 양자 AI 모델의 기본 정보 표현에 대한 새로운 관점을 제시합니다. 즉, 양자 회로에서 불확실성을 처리하는 데 베이지안 접근 방식보다 신념 함수가 더 적합함을 시사합니다.

시사점, 한계점

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시사점:

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양자 컴퓨팅 환경에서 TBM이 베이지안 접근 방식보다 더 효율적이고 간결한 불확실성 처리 방식임을 제시합니다.

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양자 컴퓨팅의 특성을 활용한 몇 가지 새로운 신념 전이 접근 방식을 제안합니다.

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양자 AI 모델의 기본 정보 표현에 대한 새로운 관점을 제공합니다. 신념 함수가 불확실성 처리에 더 적합함을 시사합니다.

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한계점:

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논문에서 제시된 새로운 신념 전이 접근 방식의 실제 효율성 및 성능에 대한 추가적인 실험적 검증이 필요합니다.

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TBM의 계산 복잡성 문제가 양자 컴퓨팅 환경에서 완전히 해결되었는지에 대한 추가적인 분석이 필요합니다.

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제안된 접근 방식의 일반성 및 다양한 양자 알고리즘 및 애플리케이션에 대한 적용 가능성에 대한 추가적인 연구가 필요합니다.

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