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
Model checking is an automated technique for the verification of finite-state systems that is widely used in practice. In Bounded Model Checking (BMC) the system is checked only until a given execution depth from the initial state. State of the art model checkers apply Binary Decision Diagrams (BDDs) as well as Satisfiability Solving (SAT) for this task. However, both methods suffer from the state explosion problem, which restricts the application of model checking to only modestly sized systems. The importance of model checking makes it worthwhile to explore alternative technologies, in the hope of enabling the application of the technique to a wider class of systems. Description Logic (DL) is a family of knowledge representation formalisms, mainly used for designing ontologies, for which reasoning is based on tableaux techniques. In this article, we show how model checking problems can be solved using DL reasoning. We present two different encodings of a model checking problem as a consistency check in DL, and show how DL can serve as a natural setting for representing and solving a BMC problem. Experimental results, using the DL reasoner FaCT++, give encouraging results.
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 | 0.001 |
| Open science | 0.000 | 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