Automatic failure detection with Conditional-Belief supervisors
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
Failures of a software system are detected by a supervisor, a separate unit which observes the inputs and outputs of the system and reports its failures in real-time. The supervisor determines whether a failure has occurred by comparing the observed and the specified behavior. The specification of behavior is assumed to be expressed in a formalism based on communicating extended finite state machines (specifically, ITU-T SDL). The supervisor must tolerate legal behavioral alternatives resulting from nondeterminisms in the specification. The computational costs of considering such alternatives can be fairly high. The paper presents the Conditional-Belief (CB) theory that reduces the cost of consideration of alternatives by using conditional-beliefs to represent sets of legal behavioral alternatives. The paper reviews belief-based supervision, introduces the CB theory, and outlines an algorithm for conversion of a class of SDL specification to a CB supervisor model. It describes a demonstration system developed to evaluate CB supervision, and summarizes failure detection and computational cost results for the supervisor of the control program of a small telephone exchange.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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