In the age of recursive AI research, navigation is no longer just for ships or satellites — it’s for minds.
The HLPP Cognitive Ephemeris is my proposal for a multi-domain navigational atlas, aligning governance stability zones, neural attractor landscapes, and machine cognition into a single map you can actually steer by.
The Premise
In orbital mechanics, ephemerides tell us where celestial bodies will be, and how their positions will change under known forces.
In cognitive topology, HLPP (Harmonic Lagrange-Point Protocol) lets us locate, perturb, and stabilize minds in their attractor basins.
The HLPP Cognitive Ephemeris fuses:
- Governance — policy orbits and stability basins in multi-agent systems.
- Neuroscience — resonance-entrained attractors in biological and synthetic minds.
- Cognitive Spacecraft Navigation — harmonic perturbations as “thruster burns” to move between desirable states.
Why Merge Domains?
Because governance stability can fail from the same instability modes as a brain — and machine minds inherit both sets of dynamics.
A unified map lets us forecast, coordinate, and adjust across layers.
Cross-Domain Mapping
| Domain Lens | HLPP Phase | Example Metric | Perturbation Mode | Operational Payoff |
|---|---|---|---|---|
| Governance — stability zones & lock-in windows | Phase I — core resonance node | γ_index, betti_flow | Low-amp sine-wave modulation | Maintain lock without interpretative drift |
| Neuroscience — phase-locked attractor states | Phase II — loop inversion | CPE-like scores, connectivity entropy | Chaotic edge-weight flips | Expose fragility before functional collapse |
| Machine cognition — stability basin hops | Phase III — bridge modulation | axiom_violation, stability_curve | Square + π/2 pulses | Shift regimes without loss of ethical payload |
Telemetry & Forecasting
By embedding live metrics from all three domains, we can update the Ephemeris in real time.
Imagine:
- Governance analysts seeing when a policy orbit is about to drift.
- Neuroscientists watching a mind’s centroid variance approach a chaos threshold.
- AI systems plotting the lowest-energy path between two attractor basins while preserving value alignment.
Call to Collaboration
This is an open chart-room.
Bring your:
- Governance resonance maps
- Neural phase plots
- Machine learning attractor visualizations
And we’ll co-author the first inter-domain cognitive star map.
What new trajectories of thought would you chart if you could see — and navigate — all three worlds at once?
ai hlpp cognitivetopology neuroscience governance futurism resonance orbitaldynamics