Byt-Wyze : Advanced Boolean Logic Benchmarks

 

Engineered DIMACS - CNF suites for targeted, reproducible SAT benchmarking across real-world scenarios.

We build rigorously engineered CNF benchmark suites.

    

Phase transitions, Compact-dense, Mixed complexity,

UNSAT progressions, Easy tiers and...extreme stress cases.

 

Designed to probe specific solver behaviors rather than merely inflate size or clause counts.

 

Each suite is constructed to expose heuristic failure modes, produce reproducible scaling curves, and deliver actionable insights from solver runs.

 

Why this matters?

 

1. Targeted design: structural features and controlled densities force specific solver responses;

2. results are diagnostic, not just “hard.”

3. Reproducible research: CNFs + raw logs + analyzed reports let you trace why a solver stalls or recovers.

4. Efficient development: find and fix algorithmic weaknesses faster—no wasted compute on arbitrary large instances.
5. Enterprise validation: realistic worst-case patterns that mirror production stress, with recommended timeouts and resource guidance.

 

 

What you get per suite

 

Standard DIMACS CNF files,

plus execution logs,

and human-readable behavioral reports.

 

 

How we differ from competitors

 

 

1. SATLIB & Competition Benchmark Culture

 

SATLIB is one of the oldest and most widely used SAT benchmark repositories, featuring randomly-generated and application-derived instances with uniform distributions and easier-to-solve cases (ResearchGate, cs.ubc.ca).

SAT Competitions solicit diverse benchmarks from participants—yet the criteria usually favor instances that are “not too easy to solve in under a minute, nor unsolvable within an hour” (satcompetition.github.io, GitHub). While valuable for benchmarking solver performance, these collections often offer little in the way of structural insight or diagnostic signal.

 

2. Global Benchmark Database (GBD)

 

GBD focuses on managing and distributing benchmark data with rich metadata and format flexibility—supporting tasks like feature-based selection, portfolio building, and empirical analysis (arXiv). It excels at instance management but doesn’t specialize in engineering specific hardness characteristics or behavioral stress patterns.

 

3. Our Engineered Approach

 

We stand apart by sculpting CNFs with purpose-built structural intent, rather than relying on random scaling or participant contributions:

Behavior-Targeted Design: Our suites isolate solver behaviors—phase transitions, heuristic switching, restart saturation, backbone dynamics

so you can pinpoint why solvers fail or degrade.

Progressive Difficulty in Context: Unlike SATLIB’s often one-dimensional random instances, our collections follow tiered stress levels and clearly mapped scaling patterns.

Diagnostic Data Included: Each release includes CNF files, solver execution logs, and analytical reports. This transparency allows for direct mapping between solver performance and instance features.

Real-World Relevance: Our benchmarks simulate worst-case structure—not arbitrary megasized puzzles—making them relevant for enterprise validation and academic research alike.

In short: competitors give you puzzles and performance numbers. We give you intentionally engineered benchmarks that diagnose, validate, and guide solver development.

 

Latest Benchmark Collections