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
Runtime verification is a monitoring technique to gain assurance about well-being of a program at run time. Most existing approaches use sequential monitors; i.e., when the state of the program with respect to an event of interest changes, the monitor interrupts the program execution, evaluates a set of logical properties, and finally resumes the program execution. In this paper, we propose a GPU-based method for design and implementation of monitors that enjoy two levels of parallelism: the monitor (1) works along with the program in parallel, and (2) evaluates a set of properties in a parallel fashion as well. Our parallel monitoring algorithms effectively exploit the many-core platform available in the GPU. In addition to parallel processing, our approach benefits from a true separation of monitoring and functional concerns, as it isolates the monitor in the GPU. Our method is fully implemented and experimental results show significant reduction in monitoring overhead, monitoring interference, and power consumption due to leveraging the GPU technology.
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 | 0.001 |
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