Bibliographic record
Abstract
Compilers for dynamically and statically typed languages ensure safe execution by verifying that all operations are performed on appropriate values. An operation as simple as car in Scheme and hd in SML will include a run time check unless the compiler can prove that the argument is always a non-empty list using some type analysis. We present a demand-driven type analysis that can adapt the precision of the analysis to various parts of the program being compiled. This approach has the advantage that the analysis effort can be spent where it is justified by the possibility of removing a run time check, and where added precision is needed to accurately analyze complex parts of the program. Like the k-cfa our approach is based on abstract interpretation but it can analyze some important programs more accurately than the k-cfa for any value of k. We have built a prototype of our type analysis and tested it on various programs with higher order functions. It can remove all run time type checks in some nontrivial programs which use map and the Y combinator.
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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 | 0.000 | 0.000 |
| 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".