A formalized methodology for constructing safe multiphase protocols
Bibliographic record
Abstract
Communication protocols typically go through different phases, where each one performs a distinct function. Phases are implemented as layers (i.e., a protocol constructed on the OSI model) or as alternative functions (a protocol which can perform many functions, but is limited to performing one at a time). In either case, each phase is itself a protocol which can be modelled as a communicating finite state machine. A multiphase communication protocol is constructed by connecting a state (or states) of protocol A to a state (or states) of protocol B in such a way that if the component protocols A and B are safe, then the multiphase protocol is safe. C.H. Chow et al. (1985) proposed a method for connecting states which has this property. An improved method was subsequently proposed by H.A. Lin and C.L. Tarng (1993). We discuss a new protocol verification method which we use to analyze, construct, and verify a multiphase protocol. The State Transition Generation Algorithm, an algorithm which we have developed based upon the method of Lin and Tarng, is used to analyze Prolog specifications for two communicating finite state machines being combined, and to generate any new transitions that are required to ensure the new multiphase protocol is safe. We then use a protocol modelling language and two automated protocol verification tools to construct and verify the multiphase protocol. The multiphase protocol is shown to be safe with respect to specific correctness criteria when the component protocols are augmented with the new transitions generated by the State Transition Generation Algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".