Design Frameworks for Spatial Zone Agents in XRI Metaverse Smart Environments
Bibliographic record
Abstract
The spatial XR-IoT (XRI) Zone Agents concept combines Extended Reality (XR), the Internet of Things (IoT), and spatial computing concepts to create hyper-connected spaces for metaverse applications; envisioning space as zones that are social, smart, scalable, expressive, and agent-based. These zone agents serve as applications and agents (partners, assistants, or guides) for users co-living and co-operating together in a shared spatial context. The zone agent concept is toward reducing the gap between the physical environment (space) and the classical two-dimensional user interface, through space-based interactions for future metaverse applications. This integration aims to enrich user engagement with their environments through intuitive and immersive experiences and pave the way for innovative human-machine interaction in smart spaces. Contributions include: i) a theoretical framework for creating XRI zone/space-agents using Mixed-Reality Agents (MiRAs) and XRI theory, ii) agent and scene design for spatial zone agents, and iii) prototype and user interaction design scenario concepts for human-to-space agent relationships in an early immersive smart-space application.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".