VELVET: An Adaptive Hybrid Architecture for Very Large 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
Collaborative virtual environment (CVE) concepts have been used in many systems in the past few years. Applications of such technology range from military combat simulations to various civilian commercial applications. The architectures available today provide support for a number of users, but they fail if too many users are together in a small “space” in the virtual world. This paper introduces VELVET, an adaptive hybrid architecture that allows a greater number of users to interact through a CVE. This is accomplished through an adaptive filtering scheme based on multicasting. VELVET also supports small groups of users, but its use in large environments shows the greatest potential, better handling local concentrations of activity than region-, cell-, orlocale-based approaches. VELVET introduces a novel adaptive area of interest management that supports heterogeneity amongst the various participants. This allows users in a supercomputer with high-speed networking to successfully collaborate with others in not-so-powerful systems behind a slow dial-up connection.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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