Bibliographic record
Abstract
Simulation is an important aspect of model checking, serving as an invaluable pre- processing step that can quickly generate a set of reachable states. This is evident in model checking tools at the Hardware Model Checking Competitions, where Btor2 is used to represent verification problems. Recently, Btor2MLIR was introduced as a novel format for representing safety and correctness constraints for hardware circuits. It provides an executable semantics for circuits represented in Btor2 by producing an equivalent program in LLVM-IR. One challenge in simulating Btor2 circuits is the use of persistent (i.e., immutable) arrays to represent memory. Persistent arrays work well for symbolic reasoning in Smt but they require copy-on-write semantics when being simulated natively. We provide an algorithm for converting persistent arrays to transient (i.e., mutable) arrays with efficient native execution. This approach is implemented in Btor2MLIR, which opens the door for rapid prototyping, dynamic verification techniques and random testing using established tool chains such as LibFuzzer and KLEE. Our evaluation shows that our approach, when compared with BtorSim, has a speedup of three orders of magnitude when safety properties are trivial, and at least one order of magnitude when constraints are disabled.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".