New Algorithms for Maintaining All-Pairs Shortest Paths
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Bibliographic record
Abstract
This paper presents a new solution to the dynamic all-pairs shortest path routing problem, using a linear reinforcement learning scheme. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically-based updates in link-costs. In this paper we present the details of the algorithm and also provide an example to illustrate how the algorithm would function. The initial experimental results of the algorithm show that the algorithm is few orders of magnitude superior to the algorithms available in the literature. It can be used to find the shortest path (between all pairs of nodes in a network) within the "statistical" average network, which converges irrespective of whether there are new changes in link-costs or not. On the other hand, the existing algorithms fails to exhibit such a behavior and would recalculate the affected shortest paths after each link-cost update.
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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 | 0.000 | 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