Engineering an Efficient Preprocessor for Model Counting
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
Given a formula F, the problem of model counting is to compute the number of solutions (also known as models) of F. Over the past decade, model counting has emerged as key building block of quantitative reasoning in design automation and artificial intelligence. Given the wide-ranging applications, scalability remains the major challenge. Motivated by the observation that the formula simplification can dramatically impact the performance of the state-of-the-art exact model counters, we design a new state-of-the-art preprocessor, Arjun2, that relies on tight integration of techniques. The design of Arjun2 is motivated from our observation that it is often beneficial to employ preprocessing techniques whose overhead may be prohibitive for the task of SAT solving but not for model counting: accordingly, we rely on a specifically tailored SAT solver design for redundancy detection, sampling-boosted backbone detection, as well as storing of redundancy information for the purposes of improving propagation within top-down model counters. Our detailed empirical evaluation demonstrates that Arjun2 achieves significant performance improvements over prior model counting preprocessors in terms of instance-size reductions achieved as well as the runtime improvements of the downstream model counters.
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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 | 0.000 | 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 | 0.001 |
| 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