Panini: An Efficient and Flexible Knowledge Compiler
Bibliographic record
Abstract
Abstract Knowledge compilation (KC) involves compiling propositional constraints into tractable target languages which in turn efficiently support multiple analyses or queries of the constraints. Solving these queries plays a crucial role in the synthesis and verification of hardware and software systems. Recently, we proposed the target language, Constrained Conjunction & Decision Diagrams (CCDD), experimentally shown to be promising for individual model counting queries. Here, we present the compiler, $$\textsf{Panini}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Panini</mml:mi> </mml:math> , which compiles CNF into CCDD. $$\textsf{Panini}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Panini</mml:mi> </mml:math> supports a range of queries. We present an empirical evaluation focusing on two fundamental queries, uniform sampling and (multiple) model counting, with a wide range of applications. While counting and sampling have witnessed significant performance improvements over the years, scalability still remains the primary challenge. Our evaluation over 600 instances from model counting competitions 2022–2024 show that $$\textsf{Panini}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Panini</mml:mi> </mml:math> achieves state of art compilation by solving 322 instances, which is 183, 148, and 38 more than Dsharp, miniC2D, and D4 respectively. Secondly, on repetitive tasks, $$\textsf{Panini}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Panini</mml:mi> </mml:math> solves 53 and 50 more instances than ExactMC and SharpSAT-TD for model counting, and 175 and 132 more instances than SPUR and KUS for uniform sampling, respectively.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".