Skip to content
Technology How it works Breeder — Hyperion Burner — Aegis Burner — MetroVolt AI-Native Architecture Magnets Fuel cycle Safety Roadmap
Solutions AI & Data Centers Defense & Government Grid & Baseload Neutron Detection Quantum
Learn Technical Library
Proof Publications Whitepapers Technical Library Open Science & Reproducibility The Honest Gates
Company About / Mission Leadership Environment Health & Safety Investors Careers Press Contact
3D Model
AI Architecture › L5 · Applications & Copilots
L5 · Applications & Copilots

Plasma Copilot: Breeder Disruption-Avoidance Advising

The copilot watches the twin's disruption precursors and margins, explains why risk is rising, and proposes avoidance moves — while fast mitigation stays in hardware.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L5 · APPLICATIONS & COPILOTSAgentic copilots that reason over the machine.1Plasma Copilotscenario design2Engineering Copilotsubsystem analysis3Operations Copilotrunbooks & procedures4Agentic Toolsbounded action-taking5Knowledge BaseRAG over the fabric6Guardrailssafety-boundedMACHINE TIEReads the twin and fabric; proposes actions that route through L4.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORAPPLICATIONS & COPILOTSSHEET 07REV. 2026-08L5 · AI-NATIVE STACK
L5 · Applications & Copilots — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Advising, not triggering

Disruption mitigation on the breeder (Hyperion) — massive gas or shattered-pellet injection, current quench management — is a microsecond-to-millisecond hardware function owned by L1 and the autonomous failsafe. The Plasma Copilot works one tier up on the seconds timescale: it interprets the anomaly ensemble's precursor flags, explains the physics of the rising risk, and advises the operator on avoidance moves before the fast layer must act.

What the copilot reasons over

It ingests the L3 anomaly-ensemble disruptivity estimate, locked-mode and n=1 amplitudes from Mirnov coils, radiated-power fraction, density-limit proximity (Greenwald fraction), and vertical-stability growth rate, all as forecasts from the twin's predictive shadow. It correlates the current trajectory against retrieved prior disruptions with similar signatures.

text
advise(state):
  d_hat = anomaly_ensemble.disruptivity(state)      # 0..1, with UQ
  drivers = attribute(d_hat)   # e.g. {locked_mode, n_G proximity, li}
  if d_hat > warn_thresh:
     options = retrieve_avoidance_moves(drivers)    # RAG over shot db
     rank options by twin.simulate(move -> margin_gain)
     return explain(d_hat, drivers, options)        # human decides
  # fast mitigation path is NOT here - it lives in L1 hardware

Typical avoidance advice

Every piece of advice is accompanied by the twin-predicted margin gain and its uncertainty, and by the precursor evidence that motivated it. The operator, not the copilot, chooses; if the situation crosses the hardware thresholds first, the failsafe acts regardless of the copilot. This separation is deliberate — see refusal and escalation and human oversight. Because the breeder is unbuilt, the advisor is validated against the twin's disruption simulations.

Content reviewed August 2026 · design-and-simulation stage