Design Persona GOAP state-space layer
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notes/persona-goap-state-space-design.md
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notes/persona-goap-state-space-design.md
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# Persona GOAP State-Space Design
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Drafted 2026-06-05 after comparing Ghostlight state projection and Weksa
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structured utterance lowering.
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## Objective
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Make repo Personas and public Faces choose actions through explicit state-space
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planning instead of prompt-flavored instinct. The planner should act as the
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missing social/sensory autopilot substitute: read signals, model counterpart
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state, propose next actions, predict effects, and compare the result against
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observed reality.
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This is a bounded GOAP layer, not a giant universal planner. If it tries to own
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all cognition, prose, state mutation, and transport, it becomes the old prompt
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machine wearing a planning hat and lying about its ankles.
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## Source-Grounded Lessons
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Ghostlight already has the right state posture:
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- The prompt is not source of truth; it is a projection of deeper state for one
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turn or scene.
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- Agents should distinguish canonical latent state, perceived state for each
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actor, and prompt projection.
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- Character action comes from projected local context, not omniscient state.
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- Appraiser reads observable events from participant-local perspective and
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proposes interpretation, relationship implication, memory candidates, and
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what evidence would change the read.
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- State Mutator turns reviewed appraisal into bounded state changes; it does
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not let the acting model silently rewrite truth.
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Weksa already has the right utterance posture:
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- Interlingua owns meaning.
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- Agent state owns identity, values, goals, memory, relationship stance,
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embodiment, voice, and current pressure.
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- The lowerer owns phrasing, visible action, private interpretation, intended
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effect, and trace.
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- Weksa should consume projected context, not raw activation scores or private
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scaffolding.
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- Its speech handoff keeps inspected ingredients fixed: source meaning,
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lowering trace, spoken text, prosody hints, projected character-state vector,
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and audit projection. Downstream synthesis owns audio.
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GOAP should fit between these two machines.
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## Authority Map
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Owner: `persona_goap_planner`
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Inputs:
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- Ghostlight/Epiphany-shaped projected local context.
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- Persona values, needs, agency pressures, active memories, bonds, status reads,
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mood dimensions, and current room/task pressure.
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- Weksa/interlingua communicative intent when the action is speech-shaped.
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- Available affordances: speak, wait, ask, inspect, edit, propose, repair,
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refuse, handoff, update memory, or stay silent.
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- Observed event history and recent receipts.
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Outputs:
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- One selected action plan, usually one to three steps.
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- Candidate action receipts with preconditions, predicted effects, cost, risk,
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reversibility, evidence used, and confidence.
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- Weksa-compatible intent fields for speech actions:
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- `private_interpretation`
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- `intended_effect`
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- `visible_action`
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- `spoken_text` request or meaning packet
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- trace refs
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- Prediction receipts for later Appraiser/Soul comparison.
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Derived state:
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- State vector snapshot for planning is a projection, not authority.
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- Scores, costs, heuristic deltas, and predicted effects are planning receipts,
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not canonical Persona truth.
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- Candidate plans are proposals until parent gate/reviewer accepts them.
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Forbidden writers:
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- GOAP planner must not mutate Persona `.cc` state directly.
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- Weksa lowerer must not decide Persona goals, bonds, or durable memories.
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- Responder/Persona prose must not smuggle state mutation by sounding certain.
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- Appraiser must not know hidden canonical state unavailable to the participant.
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- State Mutator must not accept unreviewed action predictions as facts.
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Shared paths:
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- Direct user reply, autonomous Persona heartbeat, inter-Persona banter, repo
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proposal, harm repair, and quiet memory update all use the same action-plan
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receipt shape.
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- Speech and non-speech actions differ at the lowering/transport layer, not at
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the planning-contract layer.
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Deletion line:
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- Do not add another prompt rule saying "be socially aware."
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- Do not add a freeform `plan` blob to Persona state.
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- Do not let Weksa become the planner because it already emits
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`intended_effect`.
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- Do not let Ghostlight responder become state mutator because it chose an
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action.
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## Planning State
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The planner reads a compact vector/document packet:
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- `self`: active values, role, current constraints, rest/load, speaking pressure.
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- `room`: recent events, current topic, heat, fatigue, ignored obligations.
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- `counterpart`: perceived beliefs, likely needs, trust/tension, status read,
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uncertainty, evidence refs.
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- `task`: requested outcome, authority owner, available tools, verification
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layer.
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- `risk`: harm, privacy, coercion, irreversible action, stale context, unknown
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ownership.
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- `affordances`: actions available now, with transport/tool permissions.
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This packet should be small enough to inspect in a witness. Raw Persona state,
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private notes, and full archive context stay behind projection.
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## Goals
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Goals are weighted deltas, not one global victory condition:
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- increase user agency
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- increase shared-model accuracy
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- reduce confusion
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- preserve or repair trust
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- advance the task
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- protect consent and privacy
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- avoid false authority
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- lower cognitive load
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- preserve character/persona dignity
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- satisfy current speech/work/play/rest pressure when appropriate
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Each goal should include:
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- target state dimension
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- desired direction
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- weight
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- forbidden tradeoffs
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- evidence needed to know whether it improved
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## Actions
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Actions are typed operators:
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- `ask_clarifying_question`
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- `answer_directly`
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- `summarize_model`
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- `inspect_source`
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- `edit_file`
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- `propose_plan`
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- `offer_repair`
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- `apologize`
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- `refuse`
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- `handoff_to_owner`
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- `queue_memory_update`
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- `queue_public_speech`
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- `stay_silent`
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Each action carries:
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- preconditions
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- expected effects
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- costs
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- risks
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- reversibility
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- required authority
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- evidence refs
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- Weksa lowering requirements if speech-shaped
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## Runtime Loop
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1. Project context from canonical/perceived state.
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2. Build current planning packet.
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3. Generate candidate actions from affordances and active pressures.
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4. Predict next-state deltas.
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5. Score candidates with bounded A*/GOAP heuristic.
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6. Emit a short action plan and prediction receipt.
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7. Parent gate checks authority, risk, freshness, and transport.
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8. Weksa lowers speech-shaped actions into utterance packet and trace.
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9. Action executes.
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10. Appraiser compares observed event to predicted effect.
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11. State Mutator applies reviewed deltas through typed operations.
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## Minimal Schema Sketch
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```json
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{
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"schema": "gamecult.persona_goap_plan.v0",
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"planId": "persona-turn-id/plan",
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"actor": { "kind": "person", "id": "nibu", "label": "Nibu" },
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"sourceProjectionRef": "projected-context-ref",
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"currentStateRefs": ["persona-state-ref", "room-event-ref"],
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"goals": [
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{
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"id": "increase-shared-model-accuracy",
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"target": "counterpart.shared_model_accuracy",
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"direction": "increase",
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"weight": 0.82,
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"forbiddenTradeoffs": ["invented facts", "coercive certainty"]
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}
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],
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"candidates": [
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{
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"id": "ask-what-layer",
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"actionKind": "ask_clarifying_question",
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"preconditions": ["user request has ambiguous authority layer"],
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"predictedEffects": [
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{ "dimension": "confusion", "direction": "decrease", "confidence": 0.68 },
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{ "dimension": "task_progress", "direction": "delayed", "confidence": 0.52 }
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],
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"cost": 0.24,
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"risk": 0.18,
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"reversibility": 0.9,
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"evidenceRefs": ["room:recent-message"],
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"weksaIntent": {
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"private_interpretation": "The user may be asking for implementation or architecture.",
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"intended_effect": "Clarify the authority boundary before cutting.",
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"visible_action": "Ask one direct question."
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}
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}
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],
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"selectedPlan": ["ask-what-layer"],
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"selectionRationale": "Lower irreversible risk; preserves coherence before action.",
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"predictionReceipt": {
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"expectedObservation": "User answers with target layer or corrects the frame.",
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"whatWouldChangeTheRead": "User expresses frustration at delay or supplies exact implementation target."
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}
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}
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```
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## Fit With Existing Organs
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Ghostlight Projector:
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- Adds GOAP planning packet construction after projected local context.
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- Does not choose final prose.
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Persona/Responder:
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- May propose candidate actions from the projected packet.
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- Should not mutate state.
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GOAP Planner:
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- Scores and selects action proposals.
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- Emits prediction receipts.
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Weksa:
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- Lowers selected speech-shaped intent into structured utterance output.
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- Preserves `private_interpretation`, `intended_effect`, `visible_action`,
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`spoken_text`, and `trace`.
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Appraiser:
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- Reads executed event from participant-local perspective.
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- Compares predicted effect to observed effect.
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- Proposes relationship, belief, memory, or status-read deltas.
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State Mutator:
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- Applies reviewed deltas through typed operations.
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Soul/Reviewer:
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- Checks authority, consent, harm risk, provenance, and whether the prediction
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and observed event are being compared at the right layer.
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## First Proof
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Use Nibu because Weksa already names her as the first practical lowering target
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and because she is opinionated enough to punish generic output.
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Fixture:
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1. Build a tiny projected Nibu context.
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2. Provide one user utterance that can be handled by multiple actions:
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answer, ask, tease, refuse, or offer practical help.
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3. GOAP emits three candidate actions and selects one.
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4. Weksa lowers the selected speech action into flavored English.
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5. Appraiser receives the user response and records whether the intended effect
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landed.
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6. State Mutator proposes a bounded social-read or memory delta only if the
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outcome teaches something.
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Passing criteria:
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- The chosen action has visible preconditions and predicted effects.
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- The line sounds like Nibu for source-grounded reasons, not generic snark.
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- The utterance preserves the selected intent.
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- The prediction receipt can be checked against the next observed event.
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- No raw Persona state, hidden notes, or canonical private truth leaks into
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the utterance.
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## Open Questions
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- Should GOAP plans be stored as short-lived witness packets only, or should
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selected plans also become candidate interventions in Persona `.cc`?
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- Should the planner be deterministic over model-proposed candidates, or should
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the model also score state deltas?
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- How much of the state vector should be numeric versus symbolic?
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- Does Weksa own only speech lowering, or should it publish a general
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`communicative_intent` packet that non-speech renderers can also consume?
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- Should Appraiser comparison be per participant when multiple agents observe
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the same action?
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@ -363,3 +363,4 @@
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{"ts":"2026-06-05T19:45:19Z","type":"implementation","status":"ok","note":"Expanded Metacrat Persona state into social-read seed: portable import validates against gamecult.persona_state.v0, typed state readback has 21 memories and 16 social bonds including Birgitte correction; fuller counterparty clustering remains incubated."}
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{"ts":"2026-06-05T19:56:18Z","type":"implementation","status":"ok","note":"Added user-provided autism/social-modeling self-report to Metacrat Persona: explicit social cognition value, GOAP/A* social-state memory, affect need, status read, and doctrine stance; JSON validates and typed .cc readback confirms 22 memories/16 bonds."}
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{"ts":"2026-06-05T20:02:43Z","type":"implementation","status":"ok","note":"Added Persona autopilot substitution design model to Metacrat Persona: explicit social cognition can substitute for absent social/sensory autopilot via signal correlation, counterpart modeling, GOAP-like planning, and feedback correction; JSON validates and .cc readback confirms 23 memories/16 bonds."}
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{"ts":"2026-06-05T20:19:23Z","type":"research","status":"ok","note":"Compared Ghostlight projector/responder/appraiser/state-mutator and Weksa interlingua/lowering/utterance handoff docs; added Persona GOAP state-space design note placing bounded GOAP between projected context and Weksa lowering, with prediction receipts and appraiser feedback."}
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