Ghostlight/notes/ghostlight-implementation-plan.md

Ghostlight Implementation Plan

Current Phase

Build the first reliable data-generation loop for socially persistent Aetheria agents. The immediate target is not a full simulation. It is a clean, reviewed, sandboxed training-data pipeline for branching scenes and state consequences.

Near-Term Sequence

1. Stabilize canonical state and projection seams.

   • Keep schemas/agent-state.schema.json and required vectors coherent.
   • Keep canonical state separate from perceived overlays.
   • Keep voice, presentation, relationship, memory, and situational pressure explainable.
   • Do not leak raw numeric state internals into responder prompt text.

2. Stabilize responder packet and output seams.

   • Use schemas/responder-packet.schema.json and schemas/responder-output.schema.json.
   • Build packets from coordinator artifacts plus projected local context.
   • Preserve exact responder-visible input, hidden-context audit, allowed actions, source excerpts, output contract, and lore-access mode.
   • Preserve raw output, parsed output, review labels, consulted refs, research summary, leakage audit, and coordinator interventions.

3. Tighten research-enabled responder capture.

   • The first no-fork research-enabled Sella sample and reviewed mutation receipt now exist.
   • Preserve consulted_refs and research_summary inside both raw responder output and capture metadata.
   • Add an auditable research trace before scaling this lane; responder-reported consulted refs are useful but not enough for durable gold data.
   • Continue rejecting archived local-model prototype runners for gold responder data unless the task is explicitly model plumbing.

4. Generate the next reviewed sample.

   • Start from examples/agent-state.cold-wake-story-lab.after-sella-research-conditions.json.
   • Build the next projected local context and responder packet from reviewed state, not from coordinator memory.
   • Run the next no-fork sandboxed responder/coordinator step with exact visible input, raw output, leakage audit, and review labels.
   • Convert accepted output into mutation receipts.
   • Keep fuzzy social changes manual and auditable until appraiser/classifier models exist.

5. Generalize the loop.

   • Add more historical grounded fixtures from AetheriaLore.
   • Add future-branch Elysium fixtures with branch lineage and constraint labels.
   • Emit technology/item manifest deltas when scenes discover or stress gear, assemblies, supply chains, or faction tech bases.
   • Keep artifacts database-shaped enough for future game-engine integration.

6. Train only after schemas stop sliding.

   • Pilot samples are schema shakedown, not robust corpus scale.
   • Specialized future models include coordinator, retriever, projector, responder, appraiser, mutator, relationship/perception updater, evaluator, and institution/faction/consumer decision models.
   • Deterministic gates remain code: visibility, action legality, object custody, resource accounting, schema validation, source provenance, mutation authority, prompt leakage checks, and Ink compilation.

Deferred

• Full world simulation loop
• Autonomous offscreen faction simulation
• Economy simulation loop
• Long-horizon plot generation without author scaffolding
• Fine-tuning before artifact schemas, review criteria, and evaluators stabilize

Discipline

• Work one bounded organ at a time.
• Prefer explicit maps and contracts over implicit context.
• If the diff grows while understanding shrinks, stop and simplify.
• Keep history in git and archived receipts; keep persisted state focused on the mission.