Ghostlight/docs/architecture/soft-model-training-artifacts.md

Soft Model Training Artifacts

Ghostlight has two kinds of machinery.

Some parts should be boring code:

• schema validation
• visibility filtering
• action legality
• object custody
• resource accounting
• Ink materialization
• event append
• provenance checks
• deterministic context slicing
• mechanical mutation gates

Other parts are soft. They require judgment, taste, social inference, cultural pressure, and narrative sense. Do not fake those with brittle rules just because a function-shaped hole is sitting there looking smug.

Soft parts should emit reviewed training artifacts from the beginning. Manual review is not a temporary embarrassment. It is the dataset.

Core Rule

If a decision cannot be made reliably by deterministic code, Ghostlight should save it as a training example.

That includes accepted outputs, rejected outputs, repaired outputs, and weird half-successes. Especially weird half-successes. They are where the machine is showing the fracture line.

A useful soft artifact should preserve:

• the deterministic input slice
• the model or human output
• the reviewer decision
• accepted labels
• rejected labels, if any
• rationale grounded in state, scene, culture, memory, relationship, and lore
• failure notes
• provenance to source lore or fixture state
• whether the artifact is grounded canon, transition-grounded, procedural branch material, or promoted branch material

Soft Organs

These organs should be treated as model-training targets, not permanent hand-authored rule nests.

Projector

Turns canonical state, perceived state, memory, culture, scene pressure, and affordances into character-local operating context.

Training artifacts:

• projected local context records
• projection examples
• prompt-rendering audits
• leakage failures
• source-grounding failures

The projector does not write final prose. It prepares the local pressure surface for an acting model.

Character Agent Or Responder

Chooses what a character does next from local awareness.

This includes speech and non-speech action. The agent may speak, wait, leave, hide evidence, reveal evidence, spend a resource, attack, comply, defect, bargain, comfort, or do nothing useful because people are built badly.

Training artifacts:

• action-choice receipts
• candidate branch sets
• accepted and rejected character turns
• spoken text when speech occurs
• observable non-speech action
• actor intent
• constraints used
• constraints violated
• reviewer notes about voice, lore, embodiment, agency, and scene fit

This model should be fine-tuned or adapted on a corpus of strong Aetheria responses spread across the timeline. The goal is not a bolt-on lore module. The goal is for the responder to internalize the shape of the setting: what people assume, what institutions feel like, which pressures are normal, which metaphors appear, what bodies can do, and how the universe tends to hurt people while insisting it is only doing math.

That reduces lore handholding at runtime. It does not remove character-local context. The tuned responder still needs the specific room, speaker, listener, state, memory, affordances, and known facts for the current turn.

Appraiser

Reads an observable event from one participant's local perspective and proposes how that participant interprets it.

Training artifacts:

• observed event
• participant-local knowledge
• private interpretation
• misread or correct-read label
• emotional appraisal
• relationship implications
• belief implications
• memory candidate
• reviewer corrections

The appraiser should not know canonical private state except where that participant knows or believes it. Fundamental attribution bias belongs here.

State Mutator

Turns reviewed appraisal into bounded state changes.

Training artifacts:

• prior state refs
• event refs
• appraisal refs
• proposed state deltas
• accepted deltas
• rejected deltas
• mutation authority notes
• reviewer rationale

The early system should keep this manual and audited. Later a model can learn common deltas, but deterministic gates still own schema, bounds, and authority.

Relationship And Social Perception Classifier

Updates perceived overlays and relationship stance from repeated evidence.

Training artifacts:

• prior relationship stance
• event and appraisal history
• proposed movement
• confidence movement
• stale or reinforced impressions
• prejudice, stakes, memory, and social-reinforcement drivers
• reviewer notes

This is where confidence belongs. Do not grow a second correction-resistance organ because the first one got lonely.

Aetheria Responder Corpus

Training artifacts:

• source-grounded scene examples
• factional speech and pressure examples
• institution decision examples
• embodiment/interface examples
• negative examples where generic sci-fi assumptions were rejected
• reviewer notes tied to AetheriaLore paths

This corpus trains the character agent/responder directly. It is how a Qwen-class base or another local model learns Aetheria's native assumptions rather than waiting for every prompt to drag the whole lore vault behind it in a little wagon.

The tuned responder should learn the smell of the setting: Dominion severity, Aya-coded care pressure, Navigator embodiment, corporate certification culture, insurer cruelty, upload personhood fights, factional misreads, and the dry little horrors of systems pretending to be neutral.

It still must receive character-local context at runtime. Fine-tuning gives it priors, not omniscience.

Institution, Faction, And Consumer Decision Models

Eventually Ghostlight should support economic behavior and major faction decision-making inside a simulated game world.

Training artifacts should preserve why agents:

• buy
• refuse
• trade
• hoard
• comply
• defect
• conceal
• reveal
• punish
• help
• lobby
• exploit
• delay
• sacrifice

The artifact should preserve perceived cost, scarcity, status pressure, reputation risk, expected future contact, faction norms, institutional authority, and moral injury when present.

Do not train a dialogue-only pet if the adult machine needs to decide whether a clinic buys counterfeit cryo valves under blockade.

Artifact Status

Soft artifacts should use explicit review status:

• accepted
• accepted_as_draft
• useful_needs_revision
• rejected
• superseded

Rejected artifacts remain valuable when the failure label is clear. A rejected Navigator action that makes a cetacean walk across a human hallway is not trash. It is a negative example with teeth.

Training Corpus Families

Ghostlight should expect several related corpora rather than one heroic pile of JSONL wearing a crown.

• projection corpus
• projected-local-context corpus
• character-action corpus
• dialogue/prose response corpus
• participant-appraisal corpus
• state-mutation corpus
• relationship/perception corpus
• lore-grounding digest corpus
• Aetheria responder corpus
• Ink branch annotation corpus
• institution/faction/consumer decision corpus
• negative-example corpus

Some examples can belong to multiple corpora through references. Do not duplicate truth; link artifacts by ids and source refs.

Aetheria-Tuned Agent Model

The character agent itself is a training target.

A sensible long-term stack looks like this:

AetheriaLore source corpus
  -> source-grounded digests and scene fixtures
  -> reviewed Ghostlight action/dialogue/appraisal artifacts
  -> Aetheria-tuned responder or agent model
  -> specialized projector/appraiser/mutator students
  -> deterministic runtime gates and validators

The Aetheria-tuned agent model should learn setting priors and voice ecology: what institutions feel like, how factions misread each other, what bodies can do, what pressure sounds like when nobody in the room has enough authority to fix the machine.

It should not replace Ghostlight state. It should not decide hidden facts. It should not mutate world state directly. It acts inside the local context and affordances Ghostlight provides.

Deterministic Gates Stay Deterministic

Training models does not remove hard gates.

Keep these as code:

• schema validation
• source provenance checks
• speaker-local visibility
• prompt leakage checks
• action enum validation
• body-affordance validation when source-backed
• mutation authority
• object custody
• resource and world-state legality
• Ink compilation
• artifact review status requirements

A model can propose. The runtime decides what is legal, what is visible, what is accepted, and what becomes state.

Near-Term Policy

During prototype work:

1. Keep fuzzy updates manual and reviewed.
2. Emit every model call as a receipt when it teaches something.
3. Store accepted and rejected examples with failure labels.
4. Prefer narrow artifacts over one vague transcript blob.
5. When a prompt fix reveals a reusable failure mode, promote it into validator logic or artifact schema.
6. When a model repeatedly performs a soft judgment well, mark that organ as a candidate for distillation or fine-tuning.
7. When it repeatedly fails in a patterned way, save the failures. The beast is drawing us a map in crayon.

The point is not to automate everything today. The point is to stop losing the manual intelligence we are already spending.