Synthesizing bounded-time 2-phase fault recovery
Bibliographic record
Abstract
Abstract We focus on synthesis techniques for transforming existing fault-intolerant real-time programs into fault-tolerant programs that provide phased recovery . A fault-tolerant program is one that satisfies its safety and liveness specifications as well as timing constraints in the presence of faults. We argue that in many commonly considered programs (especially in safety/mission-critical systems), when faults occur, simple recovery to the program’s normal behavior is necessary, but not sufficient. For such programs, it is necessary that recovery is accomplished in a sequence of phases, each ensuring that the program satisfies certain properties. In the simplest case, in the first phase the program recovers to an acceptable behavior within some time θ , and, in the second phase, it recovers to the ideal behavior within time δ . In this article, we introduce four different types of bounded-time 2-phase recovery, namely ordered-strict, strict, relaxed, and graceful, based on how a real-time fault-tolerant program reaches the acceptable and ideal behaviors in the presence of faults. We rigorously analyze the complexity of automated synthesis of each type: we either show that the problem is hard in some class of complexity or we present a sound and complete synthesis algorithm. We argue that such complexity analysis is essential to deal with the highly complex decision procedures of program synthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".