Best-Compromise In-Route Nearest Neighbor Queries
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
Humans are animals of habit, e.g., people follow typical and/or familiar paths in their daily routines. With that in mind we investigate the problem where a user, traveling on his/her preferred path, needs to visit one of many available points-of-interest while (1) minimizing his/her total travel distance and also (2) minimizing the detour distance incurred to reach the chosen point-of-interest. We call this new problem the "Best-Compromise In-Route Nearest Neighbor" query in order to emphasize that a route cannot typically optimize both criteria at the same time, but rather find a compromise between them. In fact, the competing nature of these two criteria resembles the notion of skyline queries. In that context, we propose a solution based on using suitable upper-bounds to both cost criteria to prune uninteresting paths. It returns all linearly non-dominated paths that are optimal under any given linear combination of the two competing criteria. Our experiments using real data sets of different sizes show that our proposal can be orders of magnitude faster than a straightforward alternative.
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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 | 0.001 | 0.002 |
| Open science | 0.002 | 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