Online Reset for Signal Temporal Logic Monitoring
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
Online monitoring is a popular validation approach in which the temporal behavior of a system is checked to assess whether it satisfies a given specification expressed, e.g., in signal temporal logic (STL). This is done by employing a monitor that, at each time point, states the specification validity: satisfied, violated, or unknown. In some settings, monitoring should continue even after a violation episode is detected, to detect possible future violation episodes. However, for a monitor just relying on STL semantics, this is not possible, as, once the specification is violated by an input signal, any continuation of the signal still violates the specification. To tackle this problem, we here propose an optimal reset technique that, at runtime, detects the end of a violation episode and shifts the evaluation of the monitor to skip such an episode. In this way, the monitoring can continue to detect possible other future violation episodes. We propose a framework that integrates the reset technique with an existing monitoring approach. Experiments on two Simulink models show that the technique can effectively reset the monitor and report all the violation episodes, with a negligible overhead on the monitoring cost.
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 | 0.001 |
| 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