Correlation-based content adaptation for mobile web browsing
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
Abstract. The resource impoverished environment on mobile devices results in a poor experience for users browsing the World Wide Web. Proxy-based middleware that transform content on the fly to better suit the resource conditions on a user’s device provide a promising solution to this problem. A key challenge in such systems is deciding how to adapt content, especially when the same content has multiple uses that have varying adaptation requirements. In this paper, we show that it is possible to provide fine grain adaptation of multi-purpose content by detecting correlations in the adaptation requirements of past users across multiple objects on a web site, and using this history to make adaptation predictions for users encountered subsequently. To evaluate our technique, we built prototype page layout and image fidelity adaptation systems, and used these to gather traces from users browsing multi-purpose web content in a laboratory setting. Our experimental results show that using correlations to make adaptation predictions can significantly reduce bandwidth consumption, browsing time, energy usage and user effort required to adapt content.
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 | 0.000 | 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 | 0.000 | 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