Bisimulation analysis of SDL-expressed protocols: a case study
Bibliographic record
Abstract
This paper presents a family of new protocols, termed Asynchronous Retransmission Go-Back-N (AR), which are improvements on the Go-Back-N protocol in environments characterized by high error rates and/or large propagation delays. In order to verify that the use of these protocols, expressed in SDL, is transparent to the user, we explore the feasibility of their bisimulation checking. We discuss the main issues involved in translating SDL into Concurrency Workbench, a tool for performing bisimulation checking, and apply the results to verifying correctness of AR protocols. Keywords: Network protocols, SDL, Concurrency Workbench, Go-Back-N protocol, bisimulation. 1. Introduction As computer-communication systems become more complex, it is becoming more and more important to ensure the correctness of protocols they rely on. In addition, faster, better algorithms are often discovered, and it is desirable to be able to replace the old algorithm by a new in a manner that is completely tr...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".