Corresponding formal specifications with distributed systems
Bibliographic record
Abstract
As the need for computing resources grows, providers are increasingly relying on \ndistributed systems to render their services. However, distributed systems are hard \nto design and implement. As an aid for design and implementation, formal verifica- \ntion has seen a growing interest in industry. For example, Amazon uses Temporal \nLogic of Actions plus (TLA⁺) and PlusCal specification languages and tool chain \nto formally verify manually created specifications of their web services [8]. \nNevertheless, there is currently no tool to automatically establish a correspon- \ndence between a PlusCal specification with a concrete implementation. Further- \nmore, PlusCal was not designed with modularity in mind, so a large PlusCal spec- \nification cannot be decomposed into smaller ones for ease of modification. This \nthesis proposes an extension to PlusCal, named Modular PlusCal, as well as a \ncompiler, named PGo, which compiles Modular PlusCal and PlusCal specifications \ninto Go programs. Modular PlusCal introduces new constructs, such as archetypes \nand mapping macros, to provide isolation and, as a result, modularity. By auto- \nmatically compiling PlusCal and Modular PlusCal specifications into distributed \nsystem implementations, PGo reduces the burden on programmers trying to ensure \nthe correctness of their distributed systems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".