Probabilistic High-Level Estimation of Vulnerability and Fault Mitigation of Critical Systems Using Fault-Mitigation Trees (FMTs)
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
The development of safety-critical systems is a rather challenging task, especially due to the cost and complexity associated with this endeavor. For this reason, early fault assessment is a key element towards minimizing vulnerability at the design stage of development. Existing early analysis techniques are often unable to conduct a comprehensive and exhaustive analysis on complex redundant architectures, which may lead to less than optimal risk evaluation. This paper seeks to address some of these issues by proposing a high-level analysis methodology based on probabilistic model checking. This analysis is done by introducing new probabilistic models for repairable fault trees described in the Continuous-Time Markov Decision Process formalism. The models include repairable components and redundancy partitioning to evaluate fault vulnerability across different implementations of the system. The presented approach is very scalable and results demonstrate that the proposed analysis is as reliable as physical FPGA testing, in some scenarios.
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 | 0.002 |
| 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