Efficient Simulation for Hardware Model Checking
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
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.
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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 | 0.000 | 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