Bibliographic record
Abstract
Path planning is the problem of finding the lowest-cost path between two endpoints in a weighted graph. An optimal algorithm, such as A*, is guaranteed to return the lowest-cost path. However, the computational expense of A* is high on a class of graphs called terrains, motivating the development of approximate algorithms such as HTAP (the hierarchical terrain representation for approximate paths). HTAP has computational cost linear in path length, rather than A*'s quadratic complexity, but does not guarantee the lowest cost path. However, HTAP's overhead means that very short paths are disproportionately costly to find. In this paper, we propose a hybrid algorithm which uses HTAP for long paths and A* for short paths. We empirically compare the hybrid algorithm to the HTAP algorithm on the basis of computational cost. The hybrid algorithm has a significant performance advantage over HTAP in the case of very short paths, and is the same as HTAP for long paths. We report results for a number of terrains, giving performance profiles with respect to path length.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".