Skip to main content

CI Coverage DOI Python Code style: ruff

QISA Consensus Engine

Deterministic, auditable multi-perspective consensus with explicit fixpoint semantics and tamper-evident trace hashing.

QISA provides a reference-grade core for decision systems where reproducibility, auditability, and determinism are first-class constraints.

This repository intentionally avoids stochasticity and external services in order to support verifiable execution and post-hoc inspection.


Key properties

  • Deterministic convergence
    Fixpoint iteration converges (or fails fast) under bounded steps.

  • Idempotence
    Re-running from a converged state yields the same final state and trace hash.

  • Tamper-evident traces
    Each execution step is hash-chained; any post-hoc mutation is detectable.

  • Reproducible benchmarks
    Benchmarks emit pinned JSON artifacts plus a generated comparison table.

What problem does QISA solve?

QISA (Quantum-Inspired System of AI Consensus) addresses a core problem in automated decision systems:
how to produce reproducible, auditable, and deterministic decisions when multiple perspectives disagree.

In five clear points

  1. Deterministic consensus (non-stochastic) — same inputs, same outputs.
  2. Verifiable audit trail — hash-chained traces detect tampering.
  3. Fixpoint convergence — stability via idempotence prevents loops.
  4. Comparable baselines — reproducible benchmark harness + pinned results.
  5. No external dependencies — not an LLM, no external services required.

QISA does not aim to be creative. It aims to be correct, verifiable, and repeatable.

Quality gate: CI enforces test coverage (pytest-cov) with a minimum threshold.

Installation

python -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
python -m pip install -U pip
python -m pip install -e ".[dev]"

<!-- QISA_META_START -->

## Tools (Trace export + external verification)
- Paper-grade instructions: 	ools/README.md
- Quick demo:
  - Host: python tools\export_trace_demo.py then python tools\verify_trace_json.py tools\_artifacts\trace_demo.json
  - Docker: docker run --rm -v \C:\repos\qisa-consensus-engine:/app qisa-consensus-engine:dev python tools/export_trace_demo.py

## Benchmarks (adversarial)
- Latest results (generated): enchmarks\_results\results.md
- Re-run: python .\benchmarks\bench_adversarial.py

<!-- QISA_META_END -->

Metadata

Release files for qisa-consensus-engine 0.2.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for qisa-consensus-engine 0.2.4
File Size Uploaded
qisa_consensus_engine-0.2.4.tar.gz 30.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for qisa-consensus-engine 0.2.4
File Interpreter ABI Platform
qisa_consensus_engine-0.2.4-py3-none-any.whl Python 3 none any Details

Total release size: 44.0 kB

Release files / qisa_consensus_engine-0.2.4.tar.gz

Download URL qisa_consensus_engine-0.2.4.tar.gz
Size 30.7 kB
Tags Source
SHA-256 checksum
How to use checksums
f98ec3f3328412e9e80d0b0e4b3b431225c400660aeb3075f8d23f3815027ec5
BLAKE2b-256 checksum
How to use checksums
68c39d3fbf8d33421b5796e44cd6376d667d009e789794ada87e6643131a363b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Feb 12, 2026.

Transparency log

Release files / qisa_consensus_engine-0.2.4-py3-none-any.whl

Download URL qisa_consensus_engine-0.2.4-py3-none-any.whl
Size 13.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ee003a5aada0e191894b2a2800fa6aab676a4b0c7eee47f8f60e18007089efc0
BLAKE2b-256 checksum
How to use checksums
00e2c76447fb33b60b2a651a616a917478048a594a3135248c4c54174756793b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Feb 12, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.2.4 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page