Why this work is in the frame
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Bibliographic record
Abstract
We present a framework for offline partial evaluation for call-by-value functional programming languages with an ML-style typing discipline. This includes a binding-time analysis which is (1) polymorphic with respect to binding times; (2) allows the use of polymorphic recursion with respect to binding times; (3) is applicable to a polymorphically typed term; and (4) is proven correct with respect to a novel small-step specialization semantics.The main innovation is to build the analysis on top of the region calculus of Tofte and Talpin [1994], thus leveraging the tools and techniques developed for it. Our approach factorizes the binding-time analysis into region inference and a subsequent constraint analysis. The key insight underlying our framework is to consider binding times as properties of regions.Specialization is specified as a small-step semantics, building on previous work on syntactic-type soundness results for the region calculus. Using similar syntactic proof techniques, we prove soundness of the binding-time analysis with respect to the specializer. In addition, we prove that specialization preserves the call-by-value semantics of the region calculus by showing that the reductions of the specializer are contextual equivalences in the region calculus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 it