Makespan Trade-offs for Visiting Triangle Edges
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Bibliographic record
Abstract
We study a primitive vehicle routing-type problem in which a fleet of $n$unit speed robots start from a point within a non-obtuse triangle $\Delta$, where $n \in \{1,2,3\}$. The goal is to design robots' trajectories so as to visit all edges of the triangle with the smallest visitation time makespan. We begin our study by introducing a framework for subdividing $\Delta$into regions with respect to the type of optimal trajectory that each point $P$ admits, pertaining to the order that edges are visited and to how the cost of the minimum makespan $R_n(P)$ is determined, for $n\in \{1,2,3\}$. These subdivisions are the starting points for our main result, which is to study makespan trade-offs with respect to the size of the fleet. In particular, we define $ R_{n,m} (\Delta)= \max_{P \in \Delta} R_n(P)/R_m(P)$, and we prove that, over all non-obtuse triangles $\Delta$: (i) $R_{1,3}(\Delta)$ ranges from $\sqrt{10}$ to $4$, (ii) $R_{2,3}(\Delta)$ ranges from $\sqrt{2}$ to $2$, and (iii) $R_{1,2}(\Delta)$ ranges from $5/2$ to $3$. In every case, we pinpoint the starting points within every triangle $\Delta$ that maximize $R_{n,m} (\Delta)$, as well as we identify the triangles that determine all $\inf_\Delta R_{n,m}(\Delta)$ and $\sup_\Delta R_{n,m}(\Delta)$ over the set of non-obtuse triangles. Comment: 47 pages, 27 figures
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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