On the containment problem for deterministic multicounter machine models
Bibliographic record
Abstract
A new model of multicounter machines is introduced where testing the counter status of a counter is optional, rather than existing models where they are always either required (traditional multicounter machines) or no status can be checked (partially-blind multicounter machines). If, in every accepting computation, each counter has a bounded number of occurrences where its status is tested and verified to be zero, then the machine is called finite-testable . One-way nondeterministic finite-testable multicounter machines are shown to be equivalent to partially-blind multicounter machines. However, one-way deterministic finite-testable multicounter machines are strictly more powerful than deterministic partially-blind machines. Interestingly, one-way deterministic finite-testable multicounter machines are shown to have a decidable containment problem. This makes it the most general known model where this problem is decidable, making the class important in the areas of model checking and formal verification. We also study properties of their reachability sets.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".