Certifying cost annotations in compilers
Bibliographic record
Abstract
We discuss the problem of building a compiler which can lift in a provably\ncorrect way pieces of information on the execution cost of the object code to\ncost annotations on the source code. To this end, we need a clear and flexible\npicture of: (i) the meaning of cost annotations, (ii) the method to prove them\nsound and precise, and (iii) the way such proofs can be composed. We propose a\nso-called labelling approach to these three questions. As a first step, we\nexamine its application to a toy compiler. This formal study suggests that the\nlabelling approach has good compositionality and scalability properties. In\norder to provide further evidence for this claim, we report our successful\nexperience in implementing and testing the labelling approach on top of a\nprototype compiler written in OCAML for (a large fragment of) the C language.\n
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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 | 0.001 |
| 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".