Checking Distributed Programs with Partially Ordered Atoms
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
Monitoring and checking the execution of a distributed program incur significant overhead due to the large number of states that need to be considered. This paper addresses two important aspects in tackling this problem: (a) atomization of the events that occur in a run, and (b) exploiting partial order semantics rather than interleaving semantics. Atomization is used to simplify analysis by compressing the events of an execution into a much smaller number of atoms. Partial order semantics promotes separation of concerns in modeling and checking program requirements involving (i) the necessary ordering among the atoms and (ii) the correctness of each atom. Ordering requirement is modeled by a set of recurrent sequences while computation requirement is modeled by a predicate that should be satisfied in the minimal state of each atom. A partially-ordered multi-set (pomset) model is presented to demonstrate the effectiveness of the approach. It is shown that property checking can be done without involving all the states of a run, regardless of the generality of the predicate involved.
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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 | 0.001 | 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) | 0.000 | 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