Pointer warping in heterogeneous multi-monitor 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
Warping the pointer across monitor bezels has previously been demonstrated to be both significantly faster and preferred to the standard mouse behavior when interacting across displays in homogeneous multi-monitor configurations. Complementing this work, we present a user study that compares the performance of four pointer-warping strategies, including a previously untested frame-memory placement strategy, in heterogeneous multi-monitor environments, where displays vary in size, resolution, and orientation. Our results show that a new frame-memory pointer warping strategy significantly improved targeting performance (up to 30% in some cases). In addition, our study showed that, when transitioning across screens, the mismatch between the visual and the device space has a significantly bigger impact on performance than the mismatch in orientation and visual size alone. For mouse operation in a highly heterogeneous multi-monitor environment, all our participants strongly preferred using pointer warping over the regular mouse behavior.
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 | 0.001 |
| 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