Practical Cross Program Memoization with KeyChain
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
Cross program memoization (CPM) reduces resource utilization and improves response times by enabling data processing systems to re-use previously computed results between programs. An under-explored requirement to implementing CPM in general purpose data processing systems like Apache Spark is computing identifiers for results of user-defined functions that are valid between programs while avoiding degrading system performance when sharing is not possible. In this paper we describe and evaluate a technique, called KeyChain, that computes keys for intermediate and final results of programs with user-defined functions. We use KeyChain to implement CPM in Apache Spark, and show that KeyChain's simple design means it can be easily added to relevant systems, incurs low runtime overheads, and enables heuristic detection of equivalent programs so that CPM can be added to more systems and useful results can be more widely re-used.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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