Prediction-based decorators for distributed collaborative haptic virtual environments
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
Haptic Collaborative Virtual Environments, like other collaborative environments, are adversely affected by the inherent network lag, caused by delay, jitter, or packet loss, when users are geographically distributed. In this paper, we propose an approach based on both decorators and prediction to compensate for network delays and lost updates. Our approach adds to existing networking-level techniques a history buffer, and a decorator-based predictor at the receiving side and can improve the quality of collaboration as perceived by the remote users. The predictor can determine lost-update messages to improvise the current state, guess the current network delay, and anticipate remote user's interaction strategy and virtual object's position/orientation based on the history, while the decorator will act as a visual cue to inform the user about current network conditions such as the amount of lag experienced; this allows the user to cope with the lag from a human-machine interface point of view.
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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 | 0.001 | 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