Why this work is in the frame
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Bibliographic record
Abstract
Pointer analysis is a critical problem in optimizing compiler, parallelizing compiler, software engineering and most recently, hardware synthesis. While recent efforts have suggested symbolic method, which uses Bryant's Binary Decision Diagram as an alternative to capture the point-to relation, no speed advantage has been demonstrated for context-insensitive analysis, and results for context-sensitive analysis are only preliminary.In this paper, we refine the concept of symbolic transfer function proposed earlier and establish a common framework for both context-insensitive and context-sensitive pointer analysis. With this framework, our transfer function of a procedure can abstract away the impact of its callers and callees, and represent its point-to information completely, compactly and canonically. In addition, we propose a symbolic representation of the invocation graph, which can otherwise be exponentially large. In contrast to the classical frameworks where context-sensitive point-to information of a procedure has to be obtained by the application of its transfer function exponentially many times, our method can obtain point-to information of all contexts in a single application. Our experimental evaluation on a wide range of C benchmarks indicates that our context-sensitive pointer analysis can be made almost as fast as its context-insensitive counterpart.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it