Reducing Deployment Latency and Improving Runtime Stability in AR/VR Platforms via Unified Services
Bibliographic record
Abstract
AR/VR platforms run many services across devices and networks, which can slow rollouts and reduce runtime quality. We built and tested a service convergence approach that unifies device-facing and content services, and places them across edge and cloud with simple rules tied to latency targets. The study covered three regions, six device classes, and a 12-week window using a blocked cross-over schedule. We measured end-to-end timing with OpenTelemetry spans and motion-to-photon (MTP) with an optical rig. Median deployment latency fell from 128 s to 69 s (−46. 1%); p95 service-call latency fell from 214 ms to 132 ms; MTP p99 improved from 58 ms to 44 ms. Rollback events during upgrades dropped by 31%. A shared event format reduced duplicate logs by 41%, cut median time-to-detect from 66 s to 46 s, and lowered distinct incident clusters by 33%. These results show that treating scene, input, and telemetry as services—and placing them near users when needed—improves speed and stability and simplifies operations. The approach suits multi-device deployments; limits include three regions, six device types, and a 12-week study period.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 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".