Bolstering faith in GasP circuits through formal verification
Bibliographic record
Abstract
We propose a refinement-based technique to formally verify circuits of the GasP family. Verifying GasP circuits presents two main challenges: exploit their highly modular structure to reduce verification costs, and express formally their unconventional behavior at the low level, such as bidirectional signals, self-resetting logic, and fights. We propose a novel semi-automated technique for constructing specification models for interfaces of GasP circuit control units, which synchronize single-track handshake signals from different channels. These specifications are captured at a high level using abridged data transition events and transformed into intermediate specifications using low-level signal transition events. High-level verifications using data transition events are exact if each unit conforms to its intermediate specification. As a case study, we verify that a set of relative timing constraints inside the units and along channels between units, consistent with the original sizing of the circuits, is sufficient to guarantee correctness of a previously proposed square FIFO.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".