Reachability Problems in Piecewise FIFO Systems
Bibliographic record
Abstract
Systems consisting of several finite components that communicate via unbounded perfect FIFO channels (i.e., FIFO systems) arise naturally in modeling distributed systems. Despite well-known difficulties in analyzing such systems, they are of significant interest as they can describe a wide range of communication protocols. In this article, we study the problem of computing the set of reachable states of a FIFO system composed of piecewise components. This problem is closely related to calculating the set of all possible channel contents, that is, the limit language , for each control location. We present an algorithm for calculating the limit language of a system with a single communication channel. For multichannel systems, we show that the limit language is piecewise if the initial language is piecewise. Our construction is not effective in general; however, we provide algorithms for calculating the limit language of a restricted class of multichannel systems in which messages are not passed around in cycles through different channels. We show that the worst case complexity of our algorithms for single-channel and important subclasses of multichannel systems is exponential in the size of the initial content of the channels.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".