Mixed Reality IoT Smart Environments with Large Language Model Agents
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
The convergence of emerging technologies in mixed reality (XR), artificial intelligence (AI), and the internet of things (IoT) has given rise to new possibilities in smart space design and interaction. Specifically, large language models (LLMs) now provide forms of both conversation and control within smart spaces and this work proposes the use of LLMs as agents for hybrid physical and virtual context-aware user interactions in smart spaces. This addresses the challenge of designing and testing the utility of these systems, and the main contributions include: i) a design for convergence of an XR-IoT (XRI) and LLM driven smart space; ii) an architectural framework that allows for XRI-based LLM agents to interact in the hybrid smart space, iii) an early proof-of-concept prototype for an XRI Smart Work Space, and iv) an evaluation of the XRI LLM agent, toward future smart environments.
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 | 0.000 | 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) | 0.000 | 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