Astra Version 1.0: Evaluating Translations from Alloy to SMT-LIB
Bibliographic record
Abstract
We present a variety of translation options for converting Alloy to SMT-LIB via Alloy's Kodkod interface. Our translations, which are implemented in a library that we call Astra, are based on converting the set and relational operations of Alloy into their equivalent in typed first-order logic (TFOL). We investigate and compare the performance of an SMT solver for many translation options. We compare using only one universal type to recovering Alloy type information from the Kodkod representation and using multiple types in TFOL. We compare a direct translation of the relations to predicates in TFOL to one where we recover functions from their relational form in Kodkod and represent these as functions in TFOL. We compare representations in TFOL with unbounded scopes to ones with bounded scopes, either pre or post quantifier expansion. Our results across all these dimensions provide directions for portfolio solvers, modelling improvements, and optimizing SMT solvers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".