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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".