Interprocedural Type Specialization of JavaScript Programs Without Type Analysis
Bibliographic record
Abstract
Previous work proposed lazy basic block versioning, a technique for just-in-time compilation of dynamic languages which we believe represents an interesting point in the design space. Basic block versioning is simple to implement, simple enough that a single developer can build a complete just-in-time compiler for JavaScript in a year, yet it performs surprisingly well as it propagates \ncontext-sensitive type information to generate type-specialized code on the fly. \n \nIn this paper, we demonstrate that lazy basic block versioning \ncan be extended is simple ways to propagate type information across function call boundaries. This gives some of the benefits of whole-program analysis, or a tracing compiler, without having to implement the machinery for either. We have implemented this proposal in the Higgs JavaScript virtual machine and report on the empirical evaluation of this system on a set of industry standard benchmarks. The approach eliminates 94.3 of dynamic type tests on average, \nwhich we show is more than what is achievable with any static whole-program type analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".