Ghostlight/notes/ghostlight-implementation-plan.md

Ghostlight Implementation Plan

Current Phase

GhostlightDungeon is now the active implementation program. Its authoritative architecture and ownership map is docs/architecture/ghostlight-dungeon-mvp.md. The existing data-generation pipeline and fixtures remain regression evidence; they do not become hosted runtime authorities.

The Epiphany Projector → Persona → Interpreter extraction and handback are complete. Ghostlight owns the generalized projection membrane in the ghostlight-persona-projection crate. Epiphany consumes an exact Ghostlight Git revision and now gives Persona only one lived narrative stream; Epiphany keeps her canonical Persona mind, receipts, effect admission, brakes, and external consequence authority.

The hosted runtime owns compiler approval, exact VoidBot evidence, campaign mailboxes, fiction-first d20 resolution, parallel Persona appraisal, narration, reversible gestalt materialization, isolated sessions/campaigns, CultMesh/Eve publication, and away-time scheduling. Strategic ticks now project six-axis pressure, select a connected budgeted cover over the global agency skeleton, and run one private Ghostlight Persona membrane per cohesive or arena cell. The WorldKernel admits the whole cell wave atomically and forbids player puppeting, synthetic arena actors, borrowed secrets, invented IDs, unreachable movement, and unbounded information or population-pressure edits. The laboratory exposes a 1–32 player budget, persistent pins, approval-gated gestalt fission, graph and cover receipts, and a separate operator provider-concurrency limit. The specific authority map is docs/architecture/ghostlight-multiresolution-agency.md. The accepted implementation is hosted on Starfire as immutable release f1d55b181ad60ace8e8e92859c4d0b2f1a94fc87. Exact checkout, embedded commit, release manifest, binary hash, process, typed health, CultMesh store, persisted campaign, DeepSeek startup inference, VoidBot retrieval, private-LAN access, WireGuard access, and unauthenticated rejection agree. This pass makes model work inspectable and cheaper: provider attempts expose prompt/completion/cache usage and exact local failures; stable prefixes precede dynamic context; world and agency compilation are separate stages; Projectors receive situation state while Interpreters receive exact permissions; deterministic cell bindings are attached by the runtime. Remote agency compilation is a separate non-causal Flash lane: exact mandate strings are bound to witnesses locally, rejected candidates become coverage gaps, and deterministic coarse profiles keep unknown facets unknown. The Pro agency stage no longer spends output tokens restating remote profiles.

The current browser/compiler hardening adds three ownership cuts before the next isolated acceptance run: compiler evidence is triaged into direct, background, and excluded lanes before generation, with only direct source text admitted to the causal world seed; informational action effects must name the exact finding they commit, which the runtime binds into the player-visible stake before validation; and player HTTP command responses are spoiler-safe projections rather than serialized campaigns. The browser renders these projections as labeled, escaped human-readable controls, and narration receives only the latest causal turn rather than a route dump.

Live local evidence now covers grounded VoidBot compilation, a four-actor wave, impossible and receipted d20 actions, grounded narration, strategic cells, and a 24-faction budget-4 wave. The first scale baseline used 37,327 prompt tokens and 17.81 seconds. After the authority/context cuts, the same complete four-cell cover used 20,760 prompt tokens and 14.38 seconds, with 13 of 13 stages valid on their first attempt, two material institutional consequences, exact constituent authority, and no player mutation. Authenticated surface and lifecycle acceptance now cover spoiler-safe literal HTTP responses, separate invite ownership, single-use invite rejection, fork/reset/export, four persisted campaigns, and exact-commit restart. Live priority cancels in-flight scheduler inference and excludes its commit gate.

Next, run the two-tester playtest. Treat player confusion, dramatic flatness, and token-heavy low-consequence stages as evidence about missing context or misplaced authority. Use model-stage cache usage, rejection receipts, cover plans, and world commits to improve satisfaction and cognitive efficiency without weakening state admission.

Prior research-program 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.
   • Adopt VoidBot's heartbeat routine as the cast initiative substrate: characters take scene turns through heartbeat initiative, cooldown starts after turn completion, and offstage agents use slower rumination/sleep turns for memory consolidation instead of generating fake scene motion.

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 from the earlier fixture program

• Full world simulation loop
• 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.