WebPro: A proxy-based approach for low latency web browsing on mobile devices
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
To load a webpage, a web browser first downloads the base HTML file of the page in order to discover the list of objects referenced in the page. This process takes roughly one round-trip time and constitutes a significant portion of the web browsing delay on mobile devices as wireless networks suffer from longer transmission and access delays compared to wired networks. In this work, we propose a solution for eliminating this initial delay, which is transparent to end systems, does not require modifying HTTP, and is well suited for web browsing on mobile devices. Our solution, called WebPro, relies on a network proxy that builds an up-to-date database of resource lists for the websites visited frequently by network users. The proxy resides in the wired part of the network, and hence can afford to pro-actively build and refresh the resource list database periodically. When a request for a webpage comes to the proxy, it simultaneously fetches the base HTML and all referenced objects required to render the webpage using the corresponding resource list stored in the local database. We have built a working prototype of WebPro and have conducted live experiments over WiFi and LTE networks. Our results show an average of 26% reduction in page load time for a mix of popular web sites chosen from categories such as news, sports and shopping. Moreover, in comparison to another best known proxy-based solution, WebPro provides delay reductions ranging from 5% to 51% for a variety of web sites.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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