Attaining Loop-Free Routing with Coordinated Updates Carrying Minimum Link-State Information
Bibliographic record
Abstract
The Acyclic Source-Tree Routing Algorithm (ASTRAL) is introduced to provide loop-free multipath routing in computer networks. With ASTRAL, routers share link-state information only about those links used in their paths to destinations rather than complete topology data, and without requiring periodic messaging. ASTRAL attains loop freedom by making routers coordinate with their immediate neighbors so that they change their next hops to destinations only after routers verify that the data in their topology databases are consistent with the topology data stored at their neighbors. ASTRAL is proven to guarantee loop-free routes at every instant and to converge to shortest paths within a finite time. ASTRAL is shown to be at least as efficient as the ideal link-state algorithm, more efficient than existing link-state routing protocols like OSPF and IS-IS, and more efficient than DUAL, which is the basis of EIGRP.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".