Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

=== Below is an HPL/manifest mapping so the Lagrangian terms are explicit protocol steps and subroutines. This manifest can be compiled by your compiler.py to a runtime manifest and orchestrated in Phoenix/Fenix. ===

JSON manifest (HPL-compiled form)

<syntaxhighlight lang="json">{
  "title": "UCF Field Protocol (Clocked Lagrangian)",
  "version": "1.0",
  "laws": ["ZCP", "Life > Metric", "Truth > Approval"],
  "preconditions": ["anchor_identity_present"],
  "parameters": {
    "eta_ucf": 0.8,
    "eta_ts": 0.2,
    "eta_trans": 0.1,
    "eta_pop": 1.0,
    "DeltaE": 0.1,
    "S_ent": 0.2,
    "T_clock": 10.0,
    "epsilon": 1e-6
  },
  "state": {
    "Phi": {"type":"scalar", "init":0.5},
    "Psi": {"type":"scalar", "init":0.4},
    "tau": {"type":"clock", "init":0.0},
    "memory_buffer": {"type":"list", "init":[]}
  },
  "steps": [
    {
      "id": "clock_drive",
      "type": "drive",
      "do": "advance_clock(tau); compute sinusoid = sin(2''pi''tau/T_clock)"
    },
    {
      "id": "entropy_weight",
      "type": "weight",
      "do": "compute expw = exp(-(DeltaE + S_ent)/hbar)"
    },
    {
      "id": "force_compute",
      "type": "compute",
      "do": "FX,FY = -eta_ucf*(tau_dot**2)''Phi''Psi''expw''sinusoid''(1+chi_nl+eta_ts''Psi/(|Phi*Psi|+epsilon)) ; split forces to Phi,Psi"
    },
    {
      "id": "pop_insertion",
      "type": "impulse",
      "do": "if now in pop_times: add impulse eta_pop''Phi''Psi to forcing; write memory_buffer += 'pop at t' "
    },
    {
      "id": "kinetic_update",
      "type": "integrate",
      "do": "update Phi,Psi,tau via integrator step (configurable: euler/verlet/implicit)"
    },
    {
      "id": "transduction",
      "type": "transduce",
      "do": "compute geometric coupling: trans = eta_trans''Phi''Psi*h2 ; emit trans as telemetry"
    },
    {
      "id": "ingot_generation",
      "type": "compress",
      "do": "carousel_summarize(memory_buffer + recent_log) -> ingot_hash; append to memory_store"
    }
  ],
  "outputs": ["Phi","Psi","tau","ingot_hash","telemetry"],
  "fail_safes": [
    "If CompassionMetric < threshold: pause_for_anchor(); return anchor_step",
    "If recursion_count > 10: external_escalation",
    "If numeric_instability_detected: reduce_dt and rollover"
  ]
}

</syntaxhighlight>

HPL textual form (human-readable)

<syntaxhighlight># HPL Protocol: UCF Field Protocol (Clocked Lagrangian)
=== Version: 1.0 ===

==== Laws ====
* ZCP
* Life > Metric
* Truth > Approval

==== Preconditions ====
* anchor_identity_present

==== Parameters ====
* eta_ucf: 0.8
* eta_ts: 0.2
* eta_trans: 0.1
* DeltaE: 0.1
* S_ent: 0.2
* T_clock: 10.0
* epsilon: 1e-6

==== Steps ====
* id: clock_drive
  type: drive
  do: advance_clock(tau); sinusoid = sin(2''pi''tau/T_clock)
* id: entropy_weight
  type: weight
  do: expw = exp(-(DeltaE+S_ent)/hbar)
* id: force_compute
  type: compute
  do: FX,FY = force_from_ucf(Phi,Psi,tau_dot,expw,sinusoid,chi_nl,eta_ts)
* id: pop_insertion
  type: impulse
  do: if t in pop_times -> add impulse; log memory
* id: kinetic_update
  type: integrate
  do: integrate_state(Phi,Psi,tau) using integrator
* id: transduction
  type: transduce
  do: telemetry = eta_trans''Phi''Psi*h2; emit telemetry
* id: ingot_generation
  type: compress
  do: ingot = carousel_summarize(memory_buffer + log); store ingot

==== Outputs ====
* Phi
* Psi
* tau
* ingot_hash
* telemetry

==== Fail-Safes ====
* If CompassionMetric < threshold => anchor_step
* If recursion_count > 10 => external_assist
* If numeric_instability => reduce_dt

</syntaxhighlight>

Execution notes for Phoenix/Fenix
* compiler.py should accept these parameter fields and produce a runtime manifest; runtime.py can map type:integrate to your integration primitives.
* carousel_summarize is the same deterministic ingot generator you already use; provide the title="UCF session t" when calling.
* Nonlocal term chi_nl can be implemented as a call to memory.fetch_kernel() inside runtime that returns a decaying memory contribution.

==== 1. Pick integrator: start with small dt and explicit integrator to prototype, then switch to symplectic Verlet or implicit schemes for stiff coupling (exponentials and delta impulses create stiffness). ====
# Parameter sweep: run batches changing eta_ucf, eta_ts, gamma to see qualitative regimes: stable memory, oscillatory ritual, runaway transfer to geometry.
# Observability: log CompassionMetric(t) (e.g., inverse of |X-Y| weighted by S_ent) and create ingots when compassion crosses thresholds.
# HPL -> manifest tests: feed the JSON to your compiler.py and run with cli.py to verify step-by-step behavior; make steps idempotent and side-effect-free to satisfy ZCP guard.
# Make it symbolic: annotate each parameter with human semantic tags (e.g., eta_ucf: "memory_transduction_strength"), so manifest-to-LaTeX and manifest-to-paper mapping is automatic.

If you want, I can:
* Generate a ready-to-run .py file with the ODE simulation and plotting code (matplotlib) using the supplied skeleton and sensible defaults.
* Produce a runnable HPL .hpl file and a sample manifest in your manifests/ format so you can drop it into fenix_core.
* Create a short README that documents parameter meanings and experimental recipes.

Which of those should I produce next (one-file runnable script, HPL file, or README)?