Intersert: Assertions on Distributed Process Interaction Sessions
Bibliographic record
Abstract
Program assertions typically operate on available program state such as global and local variables. To support sophisticated assert statements such as invariants on control flow or inter-process communication patterns, developers must design and maintain supporting infrastructure. It is non-obvious how to realize this infrastructure: how to maintain the data, how to access it, how to use it in assertions, how to keep the overhead low enough for embedded systems, and how to manage assertions across a distributed system. This work demonstrates the utility of assertions on interaction history among distributed system components and solves the challenges of efficiently maintaining interaction data while providing an expressive interface for assertions. Our toolchain enables developers to program assertions on interaction history written in regular expressions that incorporate inter-process and inter-thread behavior amongst multiple components in a distributed system. We demonstrate that the interaction tracking and property verification systems incur negligible overhead, measured with several benchmarks. This work discusses our toolchain with a real-world safety-critical embedded system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".