97 lines
5.9 KiB
Markdown
97 lines
5.9 KiB
Markdown
# Ghostlight Implementation Plan
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## Current Phase
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Build a reliable data-generation loop for socially persistent Aetheria agents.
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The immediate target is not a full simulation. It is a clean, reviewed,
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sandboxed training-data pipeline for branching scenes and state consequences.
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The output should preserve Aetheria's tonal range: wit with stakes, ordinary
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life before interruption, quiet ritual, domestic warmth, weirdness, dread, and
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systems pressure as needed.
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Planning belongs in this file and the handoff/map state surfaces. Architecture
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docs should describe durable contracts, boundaries, and data shapes; they should
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not carry live next-action lists.
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## Near-Term Sequence
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1. Stabilize canonical state and projection seams.
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- Keep `schemas/agent-state.schema.json` and required vectors coherent.
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- Keep canonical state separate from perceived overlays.
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- Keep voice, presentation, relationship, memory, and situational pressure explainable.
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- Add or preserve tonal-mode and ordinary-life cues where they affect prose.
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- Do not leak raw numeric state internals into responder prompt text.
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2. Stabilize responder packet and output seams.
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- Use `schemas/responder-packet.schema.json` and `schemas/responder-output.schema.json`.
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- Build packets from coordinator artifacts plus projected local context.
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- Preserve exact responder-visible input, hidden-context audit, allowed actions, source excerpts, output contract, and lore-access mode.
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- Preserve raw output, parsed output, review labels, consulted refs, research summary, leakage audit, and coordinator interventions.
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- 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.
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3. Stabilize scene-loop receipt bundles.
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- Use `schemas/event-record.schema.json`,
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`schemas/participant-appraisal.schema.json`,
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`schemas/reviewed-mutation.schema.json`, and
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`schemas/scene-loop-bundle.schema.json`.
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- Validate with `npm run training-loop:validate`, included in
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`npm run schema:validate`.
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- A training-ready scene loop preserves scene-local digest, initial state,
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coordinator artifacts, projected contexts, responder packets, raw responder
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outputs, event records, participant-local appraisals, reviewed mutations,
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and bundle-level training-usability labels.
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- Training readiness is per organ. A receipt-complete scene loop does not
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automatically count as branch compiler, IF reviewer, visual artifact, or
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accepted full-fixture coverage.
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4. Keep the Pallas fixture as the current scaffold reference.
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- Current fixture set:
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`examples/lore-grounding/pallas-species-strikes.awakened-labor.v0.json`,
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`examples/agent-state.pallas-species-strikes.v0.json`,
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`examples/coordinator/pallas-species-strikes.branch-and-fold.v0.json`,
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`examples/ink/pallas-species-strikes.branch-and-fold.v0.ink`,
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`examples/ink/pallas-species-strikes.branch-and-fold.v0.training.json`,
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`examples/visual/pallas-species-strikes.branch-and-fold.v0.visual.json`,
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and `experiments/pallas-species-strikes/` receipts.
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- Treat it as reference-only story-shape and grounding material, not training-ready data for any soft organ.
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- 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.
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- `pallas-training-loop-v0` is the first separate training-shaped derivative:
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it rebuilds the Kappa refusal / Ilya arrival threshold beat through three
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exact no-fork responder turns plus event, appraisal, mutation, and bundle
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receipts. Keep it separate from the immutable reference fixture.
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5. Generalize the loop.
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- Use the corpus coverage ledger so future fixtures can be tracked by faction, movement, flashpoint, tonal mode, training target, review status, and cultural collisions.
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- Select the next Aetheria fixture from the 100-150 broad coverage target.
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- Add more historical grounded fixtures from AetheriaLore.
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- Add future-branch Elysium fixtures with branch lineage and constraint labels.
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- Track coverage across every major faction, minor faction, movement, and major flashpoint; major factions require both founding-era and day-in-the-life stories.
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- Require cultural collision in coverage stories so training data captures inter-faction dynamics, mutual misreads, and movement pressure.
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- Vary tonal modes across fixtures so the corpus does not train one flattened Aetheria voice.
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- Emit technology/item manifest deltas when scenes discover or stress gear, assemblies, supply chains, or faction tech bases.
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- Keep artifacts database-shaped enough for future game-engine integration.
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5. Train only after schemas stop sliding.
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- Pilot samples are schema shakedown, not robust corpus scale.
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- Specialized future models include coordinator, retriever, projector,
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responder, appraiser, mutator, relationship/perception updater, branch
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compiler, IF artifact reviewer, evaluator, and institution/faction/consumer
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decision models.
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- Deterministic gates remain code: visibility, action legality, object
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custody, resource accounting, schema validation, source provenance,
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mutation authority, prompt leakage checks, and Ink compilation.
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## Deferred
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- Full world simulation loop
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- Autonomous offscreen faction simulation
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- Economy simulation loop
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- Long-horizon plot generation without author scaffolding
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- Fine-tuning before artifact schemas, review criteria, and evaluators stabilize
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## Discipline
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- Work one bounded organ at a time.
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- Prefer explicit maps and contracts over implicit context.
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- If the diff grows while understanding shrinks, stop and simplify.
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- Keep history in git and distilled evidence; keep the live workspace focused on the mission.
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