Programming type-safe transformations using higher-order abstract syntax
Bibliographic record
Abstract
When verifying that compiler phases preserve some property of the compiled program, a major difficulty resides in how to represent and manipulate variable bindings, often imposing extra complexity both on the compiler writer and the verification effort. In this paper, we show how Beluga's dependent contextual types let us use higher-order abstract syntax (HOAS) to implement a type-preserving compiler for the simply-typed lambda-calculus, including transformations such as closure conversion and hoisting. Unlike previous implementations, which have to abandon HOAS locally in favor of a first-order binder representation, we are able to take advantage of HOAS throughout the compiler pipeline, so that the compiler code stays clean and we do not need extra lemmas about binder manipulation. Our work demonstrates that HOAS encodings offer substantial benefits to certified programming. Scope and type safety of the code transformations are statically guaranteed, and our implementation nicely mirrors the paper proof of type preservation, and can hence be seen as an encoding of the proof which happens to be executable as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".