Region-Guided Interactive Docent for Paintings in Mixed Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose a system that provides personalized interpretation of physical paintings using an AR cursor in a mixed reality (MR) environment. In galleries, pointing at specific regions of complex paintings is often restricted, making it hard to ask targeted questions. While prior works have proposed interactive systems to assist visitor understanding, they typically offered generalized explanations that were not grounded in real, physically exhibited artworks. To address this gap, our approach allows users to intuitively drag across the surface of a painting using a hand-guided AR cursor to specify a Region of Interest. The cropped image, along with the user’s spoken question, is then sent to a Vision Language Model (VLM), which generates a personalized and context-aware explanation based on the selected area. This approach enables users to gain a more precise and meaningful understanding of the painting, guided by their specific interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it