A Case‐Addition Policy for Case‐Base Maintenance
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
A major problem in many practical applications of case‐based reasoning (CBR) and knowledge reuse is how to keep the case bases concise and complete. To solve this problem requires repeated maintenance operations to be applied to case bases. Different maintenance policies may result in case bases with very different quality. In this article, we present a case‐addition maintenance policy that is guaranteed to return a concise case base with good coverage quality. We demonstrate that the coverage of the case base computed by the case‐addition algorithm is no worse than the optimal case‐base coverage by a fixed lower bound. We also show that the algorithm implementing the case‐addition policy is efficient. Our result also highlights benefit reduction as a key factor in influencing the convergence of case‐base coverage when cases are added to a case base. Through our theoretical analysis, we analytically derive the well known coverage convergence curves commonly displayed in CBR experiments and show that benefit reduction can be used as a predictor for convergence speed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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