Runtime verification of LTL on lossy traces
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 techniques mostly assume the existence of complete execution traces. However, real-world systems often produce lossy traces due to network issues, partial instrumentation, sampling, and logging failures. A few verification techniques have recently emerged to handle systems with incomplete traces. Some of these techniques sacrifice soundness and may produce imprecise verdicts. The others depend on the recovery of lost events for a sound and meaningful verdict. In this paper, we present an offline algorithm that identifies whether an Ltl (Linear Temporal Logic) formula can be soundly monitored in the presence of a transient loss of events in a trace and constructs a monitor accordingly. More, we introduce the concept of monotonicity to express the persistence of the verdicts of a loss-tolerant monitor regardless of the recovery of the lost events. Our evaluation demonstrates the applicability, efficiency and practicality of the technique on common Ltl patterns, but also on traces from Google Clusters and MPlayer.
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 | 0.001 | 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