FROM ADAPTATION-GUIDED RETRIEVAL TO REUSE-GUIDED RETRIEVAL: APPLICATION TO CASE RETRIEVAL NET MEMORY MODEL
Bibliographic record
Abstract
In Case-Based Reasoning (CBR), case retrieval is generally guided by similarity. However, the most similar case may not be the easiest one to reuse (hard to adapt). As recommended by Smyth and Keane, it might be more efficient to use an adaptability criterion to guide the retrieval process (adaptation-guided retrieval or AGR). In the same trend but with the goal of optimizing case reuse, our approach is to consider what is similar to copy and what is different to adapt during the retrieval stage. We introduce a more general framework for retrieval, namely the reuse-guided retrieval (RGR). The goal of this paper is twofold: first, it proposes a case retrieval approach that relies on reuse cost; then, it illustrates its use by integrating adaptation cost into the case retrieval net (CRN) memory model, a similarity-based case retrieval system. The described retrieval framework optimizes case reuse early in the inference cycle, without incurring the full cost of an adaptation step. Our results on two case studies reveal that the proposed approach yields better recall quality than CRN's similarity only-based retrieval while having similar computational complexity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".