QAINET® Validations

Validations

Evidence, not theory.

PX4 Qualification Gates — May 2026

PX4 + Gazebo Harmonic is the industry-standard flight-qualification simulator: Auterion, NASA AFWERX, USAF. If the swarm flies the sim, it flies the drones.

All gates passed May 2026:

  • Gate 1 — Mission ingest: Pass. 342 waypoints, full bundle match.

  • Gate 1B — FCCM-93 export-lane parity: Pass across all four lanes — runtime CPU, runtime GPU, Python, Rust. 64 control ticks per lane. Zero lane errors. Zero mismatches.

  • Gate 2 — Behavioral equivalence: Pass.

  • Gate 3 — Fail-closed across four adversarial fault scenarios: Pass.

Hanscom 100-Drone Demonstration — April 2026

100-drone simulator pipeline, requested by a U.S. DoD federally funded R&D center.

Conditions: 200 mph. Three independent runs. Adversarial mission profile.

Results across all three runs:

  • Zero constraint violations

  • Bit-identical structural outputs

  • Identical cryptographic proofs

  • Identical behavior graphs

  • 873.3 seconds simulated flight per run, 7,600 trace samples per run

Adversarial fault tolerance: One drone neutralized → instant 100-drone reconfiguration, no operator intervention.

The substrate executed identically three times, under adversarial pressure, while maintaining hard safety constraints in every frame and recovering from injected agent loss without human intervention.

Ready for live flight test.

Why these results are structurally impossible for probabilistic AI

Bit-identical outputs across three independent runs requires:

  1. No sampling variance — every decision derived, not drawn from a distribution

  2. No training-state dependence — there is no training state

  3. No floating-point nondeterminism — runtime engineered for cross-platform bit-identity

  4. No silent failures — every state-machine transition observed and logged

LLM-based and deep-RL autonomy fails all four. Classical rule-based autonomy can achieve (1) and (3) but cannot scale coordination past ~100 agents.

QAINET® achieves all four at 100-agent adversarial scale, in production.

Alpha cohort

Alpha cohort closed. Signed-up national and international testers under NDA. Testing begins Summer 2026.

What we don't claim

We don't claim QAINET® cannot produce wrong outputs. A deterministic system given a wrong mission specification will reliably produce wrong behavior — that's what determinism means. What we claim is that the substrate eliminates entire classes of failure mode that probabilistic AI introduces: sampling variance, training-distribution drift, opaque inference, unreplayable decisions, unbounded outputs.

The agent will do what the mission says. If the mission is wrong, the agent will be wrong. The trace will show you exactly how and why — every time.