SMT-based verification of parameterized systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is well known that verification of safety properties of sequential programs is reducible to satisfiability modulo theory of a first-order logic formula, called a verification condition (VC). The reduction is used both in deductive and automated verification, the difference is only in whether the user or the solver provides candidates for inductive invariants. In this paper, we extend the reduction to parameterized systems consisting of arbitrary many copies of a user-specified process, and whose transition relation is definable in first-order logic modulo theory of linear arithmetic and arrays. We show that deciding whether a parameterized system has a universally quantified inductive invariant is reducible to satisfiability of (non-linear) Constraint Horn Clauses (CHC). As a consequence of our reduction, we obtain a new automated procedure for verifying parameterized systems using existing PDR and CHC engines. While the new procedure is applicable to a wide variety of systems, we show that it is a decision procedure for several decidable fragments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it