Synthesizing Locally Symmetric Parameterized Protocols from Temporal Specifications
Bibliographic record
Abstract
Scalable protocols and web services are typically parameterized: that is, each instance of the system is formed by linking together isomorphic copies of a representative process.Verification of such systems is difficult due to state explosion for large instances and the undecidability of verifying properties over all instances at once.This work turns instead to the derivation of a parameterized protocol from its specification.We exploit a reduction theorem showing that it suffices to construct a representative process P that meets a local specification under interference by neighboring copies of P .Every instance of the parameterized protocol is built by deploying replicated instances of P .While the reduction from the original to a local specification is done by hand, the construction of P is fully automated.This is a new and challenging synthesis question, as one must synthesize an unknown process P while simultaneously considering interference by copies of this unknown process.We present two algorithms: an eager reduction to the synthesis of a transformed specification, and a lazy, iterative, tableau construction which incorporates fresh interference at each step.The tableau method has worst-case complexity that is exponential in the length of the local specification.We have implemented the tableau construction and show that it is capable of synthesizing parameterized protocols for mutual exclusion, leader election, and dining philosophers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".