Combined profiling: A methodology to capture varied program behavior across multiple inputs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces combined profiling (CP): a new practical methodology to produce statistically sound combined profiles from multiple runs of a program. Combining profiles is often necessary to properly characterize the behavior of a program to support Feedback-Directed Optimization (FDO). CP models program behaviors over multiple runs by estimating their empirical distributions, providing the inferential power of probability distributions to code transformations. These distributions are build from traditional single-run point profiles; no new profiling infrastructure is required. The small fixed size of this data representation keeps profile sizes, and the computational costs of profile queries, independent of the number of profiles combined. However, when using even a single program run, a CP maintains the information available in the point profile, allowing CP to be used as a drop-in replacement for existing techniques. The quality of the information generated by the CP methodology is evaluated in LLVM using SPEC CPU 2006 benchmarks.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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