Ghostlight/notes/ghostlight-implementation-plan.md

Ghostlight Implementation Plan

Current Phase

Build a 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. The output should preserve Aetheria's tonal range: wit with stakes, ordinary life before interruption, quiet ritual, domestic warmth, weirdness, dread, and systems pressure as needed.

Planning belongs in this file and the handoff/map state surfaces. Architecture docs should describe durable contracts, boundaries, and data shapes; they should not carry live next-action lists.

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.
   • Add or preserve tonal-mode and ordinary-life cues where they affect prose.
   • 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.
   • Require runner_captured research trace status for accepted research-enabled gold data; coordinator-reconstructed trace is useful draft audit, not proof of the responder's actual research path.

3. Stabilize scene-loop receipt bundles.

   • Use schemas/event-record.schema.json, schemas/participant-appraisal.schema.json, schemas/reviewed-mutation.schema.json, and schemas/scene-loop-bundle.schema.json.
   • Validate with npm run training-loop:validate, included in npm run schema:validate.
   • A training-ready scene loop preserves scene-local digest, initial state, coordinator artifacts, projected contexts, responder packets, raw responder outputs, event records, participant-local appraisals, reviewed mutations, and bundle-level training-usability labels.
   • Training readiness is per organ. A receipt-complete scene loop does not automatically count as branch compiler, IF reviewer, visual artifact, or accepted full-fixture coverage.

4. Keep the Pallas fixture as the current scaffold reference.

   • Current fixture set: examples/lore-grounding/pallas-species-strikes.awakened-labor.v0.json, examples/agent-state.pallas-species-strikes.v0.json, examples/coordinator/pallas-species-strikes.branch-and-fold.v0.json, examples/ink/pallas-species-strikes.branch-and-fold.v0.ink, examples/ink/pallas-species-strikes.branch-and-fold.v0.training.json, examples/visual/pallas-species-strikes.branch-and-fold.v0.visual.json, and experiments/pallas-species-strikes/ receipts.
   • Treat it as reference-only story-shape and grounding material, not training-ready data for any soft organ.
   • Preserve the lessons it established: ordinary-life onboarding, branch-and-fold discipline, material state variables, source-backed lore elaboration, visual segmentation, scene-set need for illustrated replay, and review before acceptance.
   • pallas-training-loop-v0 is the first separate training-shaped derivative: it rebuilds the Kappa refusal / Ilya arrival threshold beat through three exact no-fork responder turns plus event, appraisal, mutation, and bundle receipts. Keep it separate from the immutable reference fixture.

5. Generalize the loop.

   • Use the corpus coverage ledger so future fixtures can be tracked by faction, movement, flashpoint, tonal mode, training target, review status, and cultural collisions.
   • Select the next Aetheria fixture from the 100-150 broad coverage target.
   • Add more historical grounded fixtures from AetheriaLore.
   • Add future-branch Elysium fixtures with branch lineage and constraint labels.
   • Track coverage across every major faction, minor faction, movement, and major flashpoint; major factions require both founding-era and day-in-the-life stories.
   • Require cultural collision in coverage stories so training data captures inter-faction dynamics, mutual misreads, and movement pressure.
   • Vary tonal modes across fixtures so the corpus does not train one flattened Aetheria voice.
   • 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, branch compiler, IF artifact reviewer, 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 distilled evidence; keep the live workspace focused on the mission.