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 monitoring aims at analyzing the well-being of a system at run time in order to detect errors and steer the system towards a healthy behavior. Such monitoring is a complementary technique to other approaches for ensuring correctness, such as formal verification and testing. In time-triggered runtime monitoring, a monitor runs as a separate process in parallel with an application program under scrutiny and samples the program's state periodically to evaluate a set of properties. Applying this technique in a computing system results in obtaining bounded and predictable overhead. Gaining such characteristics for overhead is highly desirable for designing and engineering time-critical applications, such as safety-critical embedded systems. However, a time-triggered monitor requires certain synchronization features at operating system level and may suffer from various concurrency and synchronization dependencies and overheads as well as possible unreliability of synchronization primitives in a real-time setting. In this paper, we propose a new method, where the program under inspection is instrumented, so that it self-samples its state in a periodic fashion without requiring assistance from an external monitor or internal timer. We call this technique time-triggered self-monitoring. First, we formulate an optimization problem for minimizing the number of points in a program, where self-sampling instrumentation instructions must be inserted. We show that this problem is NP-complete. Consequently, we propose a SAT-based solution and a heuristic to cope with the exponential complexity. Our experimental results show that a time-triggered self-monitored program performs significantly better than the same program monitored by an external time-triggered monitor.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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