An Adaptive Multiresolution Method for Progressive Model Transmission
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
Although there are many adaptive (or view-dependent) multiresolution methods, support for progressive transmission and reconstruction has not been addressed. A major reason for this is that most of these methods require a large portion of the hierarchical data structure to be available at the client before rendering starts. This is due to the dependency constraints among neighboring vertices. In this paper, we present an efficient, adaptive, multiresolution method that allows progressive and selective model transmission. It is achieved by reducing the neighboring dependency to a minimum. The new method allows visually important parts of an object to be transmitted to the client at higher priority than the less important parts, and progressively reconstructed there for display. It is even possible to transmit only the visible parts of a model and reconstruct these visible parts at the client. The ability to selectively transmit allows the visualization of very large models across the network with minimal delay. We will present how our method works in a client-server environment. We will also show the data structure of the transmission record and some performance results of the method.
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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 | 0.001 | 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 | 0.001 |
| 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