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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".