Predicate-based dynamic slicing of message passing programs
Why this work is in the frame
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Bibliographic record
Abstract
Program slicing is a well-known decomposition technique that transforms a large program into a smaller one that contains only statements relevant to the computation of a selected function. We present a novel predicate-based dynamic slicing algorithm for message passing programs. Unlike the more traditional slicing criteria that focus only on the parts of the program that influence a variable of interest at a specific position in the program, a predicate focuses on those parts of the program that influence the predicate. The dynamic predicate slice captures some global requirements or suspected error properties of a distributed program and computes all statements that are relevant. We present an algorithm and a sample computation to illustrate how the predicate slice can be computed. Additionally, we introduce a predicate trace to classify the relevance of statement executions based on the predicate slice. A compressed predicate trace can be used to reveal those instances of statement execution that turn the global predicate true, among others.
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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 | 0.000 |
| 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) | 0.000 | 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