Clarify Aetheria responder training

docs/architecture/projection-distillation-plan.md

@@ -92,9 +92,11 @@ latency, and cost, not for final prose quality.
 ### Aetheria-Tuned Agent Model
 
 The character agent or responder is a separate training target. It may be
-adapted on Aetheria source-grounded scenes, reviewed Ghostlight action/dialogue
-receipts, and negative examples so setting priors and tone live in the model
-rather than in a giant prompt sack.
+adapted on a corpus of strong source-grounded Aetheria responses and reviewed
+Ghostlight action/dialogue receipts spread throughout the timeline. The point is
+for setting priors, tone, factional preconceptions, institutional assumptions,
+species affordances, and recurring social failure patterns to live in the
+responder model rather than in a giant prompt sack.
 
 That model still receives only character-local context at runtime. Fine-tuning
 should teach it Aetheria's pressure ecology, not grant it author vision,

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

@@ -85,10 +85,16 @@ Training artifacts:
 - constraints violated
 - reviewer notes about voice, lore, embodiment, agency, and scene fit
 
-This model should be fine-tuned or adapted on Aetheria material eventually. A
-local general model can learn the world, tone, institutions, factional pressures,
-species affordances, and recurring social failure patterns better than a raw
-model prompted from cold storage every time.
+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
 
@@ -145,10 +151,7 @@ Training artifacts:
 This is where confidence belongs. Do not grow a second correction-resistance
 organ because the first one got lonely.
 
-### Lore And Tone Adapter
-
-Bakes Aetheria source material into the response model's priors so it does not
-need the entire vault in every prompt.
+### Aetheria Responder Corpus
 
 Training artifacts:
 
@@ -159,11 +162,15 @@ Training artifacts:
 - negative examples where generic sci-fi assumptions were rejected
 - reviewer notes tied to AetheriaLore paths
 
-This is the likely home for an Aetheria-tuned model built from a Qwen-class base
-or another local model. The tuned model should know 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.
+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.
@@ -224,6 +231,7 @@ JSONL wearing a crown.
 - 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

notes/fresh-workspace-handoff.md

@@ -128,8 +128,8 @@ first reviewed mutation receipt showing how a selected generated branch changes
 both involved characters. The soft-model training doctrine is now explicit:
 deterministic gates stay code, while fuzzy judgments become reviewed artifacts
 for future projector, character-agent, appraiser, mutator,
-relationship/perception, lore-tone, and institution/faction/consumer decision
-models.
+relationship/perception, Aetheria responder, and institution/faction/consumer
+decision models.
 
 ## Current Direction
 
@@ -315,10 +315,11 @@ Remaining projection path:
 - train a smaller student projector only after the artifact schema, input
   slicer, and evaluator stabilize
 - treat the character agent/responder as a training target too: an
-  Aetheria-tuned local model can eventually learn lore, tone, factional priors,
-  species affordances, and institutional pressure from source-grounded
-  Aetheria corpora plus reviewed Ghostlight receipts, while still acting only
-  inside character-local context and deterministic mutation gates
+  Aetheria-tuned local model can eventually learn lore, tone, factional
+  preconceptions, species affordances, and institutional pressure from a corpus
+  of strong responses spread throughout the timeline plus reviewed Ghostlight
+  receipts, while still acting only inside character-local context and
+  deterministic mutation gates
 
 ## Warnings
 

notes/ghostlight-current-system-map.md

@@ -80,7 +80,7 @@ state and re-entry discipline.
   - doctrine for treating every non-deterministic judgment as reviewed
     training material for future specialized models, including projector,
     character agent/responder, appraiser, state mutator, relationship/perception
-    classifier, lore-tone adapter, and institution/faction/consumer decision
+    classifier, Aetheria responder, and institution/faction/consumer decision
     models
 - `docs/architecture/aetheria-cold-wake-training-fixture.md`
   - human-facing note that reframes the recovered Cold Wake scenario as
@@ -280,7 +280,9 @@ retries improved Maer's Navigator embodiment but exposed object-custody drift,
 invalid appraisal paths, Qwen tool dropout, and prompt-constraint leakage into
 responder prose. The soft-model artifact doctrine now makes the broader
 training boundary explicit: deterministic gates stay code, while fuzzy
-projection, action choice, appraisal, mutation, relationship movement, lore-tone
-adaptation, and economic/faction decision judgments become reviewed artifacts
-for later fine-tuning or distillation. Do not materialize another capture until
-it has no failure or repair notes.
+projection, action choice, appraisal, mutation, relationship movement,
+Aetheria responder training, and economic/faction decision judgments become
+reviewed artifacts for later fine-tuning or distillation. The lore/tone goal is
+not a separate adapter; it is a responder trained on strong timeline-spread
+Aetheria responses until the setting's assumptions become native priors. Do not
+materialize another capture until it has no failure or repair notes.

notes/ghostlight-implementation-plan.md

@@ -221,17 +221,18 @@
    - Train or adapt a smaller student projector only after the artifact schema,
      input slicer, and evaluator are stable.
    - Treat the character agent/responder as a training target too. A
-     Qwen-class or similar local model can eventually be adapted on the
-     Aetheria corpus and reviewed Ghostlight receipts so lore, tone, factional
-     priors, species affordances, and institutional pressure are baked into the
-     response model's priors rather than pasted into every prompt.
+     Qwen-class or similar local model can eventually be adapted on a corpus of
+     strong Aetheria responses spread throughout the timeline, plus reviewed
+     Ghostlight receipts, so lore, tone, factional preconceptions, species
+     affordances, and institutional pressure become native responder priors
+     rather than prompt handholding.
    - Keep deterministic gates as code even after fine-tuning: visibility,
      action legality, object custody, resource accounting, schema validation,
      source provenance, mutation authority, prompt leakage checks, and Ink
      compilation.
    - Emit reviewed artifacts for every fuzzy organ before training specialized
      models: projector, character agent/responder, participant appraiser,
-     state mutator, relationship/perception classifier, lore-tone adapter, and
+     state mutator, relationship/perception classifier, Aetheria responder, and
      later institution/faction/consumer decision models.
 
 6. Build the first drama-scaffolding loop.

state/evidence.jsonl

@@ -47,3 +47,4 @@
 {"ts": "2026-05-02T03:29:51+00:00", "type": "documentation", "status": "ok", "note": "Added source-first Aetheria generation doctrine and patched AetheriaLore Cetacean Navigators with habitat/interface ergonomics so Navigator action affordances can be projected from source rather than local improvisation."}
 {"ts": "2026-05-02T03:46:05+00:00", "type": "experiment", "status": "mixed", "note": "Source-checked Navigator projection improved Maer action proposals: v13-v17 route packets through shared clinic displays and wet/dry interface affordances instead of human body defaults. No capture was materialized; failures exposed object-custody drift, invented mutation paths, Qwen appraisal tool dropouts, and prompt-constraint leakage into Sella prose."}
 {"ts": "2026-05-02T07:55:40+00:00", "type": "architecture", "status": "ok", "note": "Soft judgments that cannot be deterministic code are now explicit training-artifact targets: projector, character agent/responder, participant appraiser, state mutator, relationship/perception classifier, lore-tone adapter, and later institution/faction/consumer decision models should all emit reviewed accepted/rejected receipts before fine-tuning or distillation."}
+{"ts": "2026-05-02T07:58:01+00:00", "type": "architecture", "status": "ok", "note": "Clarified Aetheria lore/tone training: the responder itself should be fine-tuned on strong source-grounded responses across the timeline so setting assumptions become native priors, rather than treating lore/tone as a separate adapter layer."}

state/map.yaml

@@ -137,9 +137,9 @@ accepted_design:
   - Ghostlight's Aetheria generation doctrine is source-first and source-repairing: if a blocking lore gap appears while generating scenes or training data, patch AetheriaLore with a narrow elaboration before treating the detail as available to Ghostlight projection.
   - AetheriaLore now includes Navigator habitat and interface ergonomics for mixed wet/dry, acoustic, translation, and shared operational spaces, so future Navigator scenes should cite that source instead of improvising body affordances.
   - Source-checked Navigator retries v11-v17 improved Maer's generated action affordances but were not materialized; remaining blockers are object-custody semantics, valid appraisal/mutation paths, Qwen tool reliability, and prompt-constraint leakage into responder prose.
-  - The soft organs are training targets: projector, character agent/responder, appraiser, state mutator, relationship/perception classifier, lore-tone adapter, and later institution/faction/consumer decision models should all produce reviewed artifacts for future distillation or fine-tuning.
-  - An Aetheria-tuned agent model may bake in lore, tone, factional priors, species affordances, and institutional pressure, but it still acts only inside Ghostlight's character-local context, deterministic gates, and mutation authority.
+  - The soft organs are training targets: projector, character agent/responder, appraiser, state mutator, relationship/perception classifier, Aetheria responder, and later institution/faction/consumer decision models should all produce reviewed artifacts for future distillation or fine-tuning.
+  - An Aetheria-tuned responder should learn lore, tone, factional preconceptions, species affordances, and institutional pressure from strong source-grounded responses spread throughout the timeline, but it still acts only inside Ghostlight's character-local context, deterministic gates, and mutation authority.
 
 current_status:
-  summary: Ghostlight now has the Epiphany-style persistence spine, the first architecture payload, the v0 canonical agent-state schema seam, prompt projection docs, immediate schema fixes from evaluation, first reviewed Cat/Oz projection examples, a revised Aetheria lore-grounding architecture, a lore grounding digest schema, the first draft Cold Wake historical grounding digest, a Cold Wake story-lab fixture, projection controls, validated Qwen capture receipts, a complete Cold Wake short story assembled from accepted projection outputs, a hand-authored Ink-backed Cold Wake branching scene, the first Qwen-generated Ink draft materialized from local awareness with reviewed training annotations, the first reviewed sequential branch mutation that updates both Maer and Sella after a sanctuary intake beat, a v2 sequential Qwen capture proving the symmetrical turn shape produces better action behavior, a v5 sequential Qwen capture proving local thinking-plus-tools invocation works for the scene runner, the first projected-local-context seam that routes sequential prompts through character-local operating prose instead of raw state variables, tightened receipt handling for projector-routed Qwen failures, a default no-thinking policy for strict sequential tool calls, a v10 no-thinking projector-routed capture accepted as draft, the first materialized projector-routed sequential Ink scene with reviewed mutation training data, a source-checked embodiment/interface projection surface for Navigator scenes, a formal source-first Aetheria generation doctrine, v11-v17 source-checked retry receipts showing improved Maer embodiment plus remaining Sella response/prompt-leak blockers, and a soft-model training artifact doctrine for turning every non-deterministic judgment into future projector, agent, appraiser, mutator, relationship, lore-tone, or decision-model training data.
+  summary: Ghostlight now has the Epiphany-style persistence spine, the first architecture payload, the v0 canonical agent-state schema seam, prompt projection docs, immediate schema fixes from evaluation, first reviewed Cat/Oz projection examples, a revised Aetheria lore-grounding architecture, a lore grounding digest schema, the first draft Cold Wake historical grounding digest, a Cold Wake story-lab fixture, projection controls, validated Qwen capture receipts, a complete Cold Wake short story assembled from accepted projection outputs, a hand-authored Ink-backed Cold Wake branching scene, the first Qwen-generated Ink draft materialized from local awareness with reviewed training annotations, the first reviewed sequential branch mutation that updates both Maer and Sella after a sanctuary intake beat, a v2 sequential Qwen capture proving the symmetrical turn shape produces better action behavior, a v5 sequential Qwen capture proving local thinking-plus-tools invocation works for the scene runner, the first projected-local-context seam that routes sequential prompts through character-local operating prose instead of raw state variables, tightened receipt handling for projector-routed Qwen failures, a default no-thinking policy for strict sequential tool calls, a v10 no-thinking projector-routed capture accepted as draft, the first materialized projector-routed sequential Ink scene with reviewed mutation training data, a source-checked embodiment/interface projection surface for Navigator scenes, a formal source-first Aetheria generation doctrine, v11-v17 source-checked retry receipts showing improved Maer embodiment plus remaining Sella response/prompt-leak blockers, and a soft-model training artifact doctrine for turning every non-deterministic judgment into future projector, agent/responder, appraiser, mutator, relationship/perception, Aetheria responder, or decision-model training data.
   next_action: Revise the Sella next-action prompt/rendering path so response constraints stay out of in-world prose, then rerun the source-checked Navigator sequential pass and materialize only a capture with no failure or repair notes.