Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Retrieval01:12

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Controlling the Double Layer of Platinum by Selective Passivation of Step Sites Using Adatom Modification.

Journal of the American Chemical Society·2026
Same author

Dietary supplementation with selenium yeast during early pregnancy enhances litter size in association with antioxidant capacity, progesterone synthesis, and gut microbiota in sows.

Microbiology spectrum·2026
Same author

Evaluating bias in target trial emulation for heart failure across statistical and deep learning methods.

Nature communications·2026
Same author

Improved Carrier Transport and Enhanced Detection Sensitivity Through Zr<sup>4+</sup> Doping in LiYMo<sub>2</sub>O<sub>8</sub> Single Crystals for X-ray Detectors.

ACS applied materials & interfaces·2026
Same author

Prognostic value of IVIM-DWI parameters for overall and recurrence-free survival in resected esophageal squamous cell carcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society·2026
Same author

A Material-Process-Equipment Integrated Design Method for Accelerating the Process Development of Twin-Screw Wet Granulation.

Pharmaceuticals (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 4, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

MuseRAG++: a deep retrieval-augmented generation framework for semantic interaction and multi-modal reasoning in

Yifan Hu1

  • 1School of Architecture and Art, Suzhou Industrial Park Institute of Vocational Technology, Suzhou, Jiangsu, China. huyifan@ivt.edu.cn.

Scientific Reports
|June 2, 2026
PubMed
Summary

We developed MuseRAG++, a novel retrieval-augmented framework for virtual museums. It enhances user engagement and factual accuracy in virtual museum interactions.

Keywords:
Cultural heritageProvenance-aware generationRetrieval-augmented frameworkUser intent modelingVirtual museums

Related Experiment Videos

Last Updated: Jun 4, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

  • Digital Humanities
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Virtual museums offer unique cultural heritage engagement opportunities.
  • Current dialogue systems lack semantic adaptability, factual grounding, and multimodal capabilities.
  • Challenges include shallow intent understanding, limited retrieval, and unverifiable responses.

Purpose of the Study:

  • To introduce MuseRAG++, a unified retrieval-augmented framework for virtual museums.
  • To improve semantic adaptability, factual grounding, and multimodal interactions in virtual museum dialogue systems.
  • To address limitations in user intent understanding, retrieval, and response generation.

Main Methods:

  • Developed a retrieval-augmented framework (MuseRAG++) integrating deep user intent modeling.
  • Implemented a hybrid sparse-dense retrieval pipeline for text, images, and metadata.
  • Incorporated a provenance-aware generation module for evidence-based responses.

Main Results:

  • MuseRAG++ demonstrated substantial improvements in retrieval and generation metrics.
  • Qualitative user study within a virtual museum prototype showed enhanced engagement and usability.
  • User evaluations confirmed increased factual accuracy and interpretability of responses.

Conclusions:

  • MuseRAG++ significantly advances virtual museum dialogue systems.
  • The framework enhances user experience through factually grounded and interpretable interactions.
  • Future work can explore broader applications of this retrieval-augmented approach.