SATYA Reproducibility Benchmark

SATYA converts repeated global revalidation into deterministic local invariant maintenance.

Patent pending.

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What This Proves

This repository allows any engineer, reviewer, or evaluator to reproduce SATYA's core performance claim in a single command:

• 5,000,000 vertices processed with streaming O(1)-per-edit maintenance
• 35 microsecond median edit latency
• Zero drift across the full run
• 25,473 cells in the cellular decomposition
• 0.50 MB memory footprint for restriction map storage

No GPU. No training data. No hyperparameters. Deterministic output.

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Quick Start

# Clone
git clone https://github.com/Jasonleonardvolk/SATYA-Reproducibility-Benchmark.git
cd SATYA-Reproducibility-Benchmark

# Install
pip install -r requirements.txt

# Run
python run_benchmark.py

One command. Full benchmark. Machine-readable JSON output plus an HTML report.

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What Gets Measured

Metric	Description
Vertex count	Total vertices in the concept graph
Cell count	Number of cells in the cellular decomposition
Median edit latency	Microseconds per streaming edit
Drift	Accumulated cohomological error after all edits
Memory profile	RSS and restriction map storage in MB
Seed log	Random seeds used for reproducibility
Machine specs	CPU, RAM, OS, Python version
Timestamp	ISO 8601 UTC

All output conforms to BENCHMARK_SCHEMA.json for cross-run comparability.

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Output

After a run completes, you will find:

• results/benchmark_result.json -- machine-readable results (schema-conformant)
• results/benchmark_report.html -- human-readable HTML report
• results/seed_log.txt -- random seeds for exact reproduction

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What This Does NOT Include

See SECURITY.md for the full boundary description.

This benchmark exercises SATYA as a black-box verification layer. It does not expose, distribute, or require access to:

• Internal cellular sheaf decomposition logic
• Purity Gate implementation
• StreamingBuilder internals
• Restriction map compression algorithms
• Cohomology computation kernels

The sigma package is a dependency, not a source inclusion.

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Threat Model

See THREAT_MODEL.md for a complete adversarial analysis covering: poisoned edges, malformed graphs, adversarial edit sequences, replay attacks, receipt forgery, and denial-of-service vectors.

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Schema

See BENCHMARK_SCHEMA.json for the standardized output format. Every run produces comparable, diffable, machine-readable results.

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Citation

If you use this benchmark in academic work:

@misc{satya2026,
  title={SATYA: Deterministic Verification Infrastructure via Cellular Sheaf Cohomology},
  author={Invariant Research},
  year={2026},
  doi={10.5281/zenodo.19598076}
}

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License

Benchmark harness: MIT. The sigma package is proprietary. Patent pending.