Multi-Layer/Multi-Region Path Computation with Adaptation Capability Constraints
Bibliographic record
Abstract
This paper addresses the Multi-Layer (ML)/Multi-Region (MR) optimal path computation problem in Generalized Multiprotocol Label Switching (GMPLS) networks. Within the context of traffic engineering, path computation consists of routing Label Switched Paths (LSPs) under a multitude of constraints while optimizing resource utilization. One such problem consists of finding a shortest path in a heterogeneous GMPLS network where the different link switching types and node switching adaptation capabilities present their own set of constraints. We argue that most work have overlooked these constraints perhaps due to an ambiguous interpretation of the switching adaptation functions defined by GMPLS. To this end, we propose a novel GMPLS path computation algorithm including a binary integer program (BIP) formulation which considers the complete set of ML/MR node adaptation constraints related to nesting, un-nesting and conversion.
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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 | 0.000 |
| 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 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".