Document VoidBot appraiser training goal

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

@@ -206,7 +206,10 @@ 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.
+how that participant interprets it. This organ is not only for fictional scene
+mutation. It is also one of the core paths by which VoidBot becomes better at
+reading people: noticing what a human did or said, forming a grounded opinion
+without pretending certainty, and revising that opinion as evidence accumulates.
 
 Training artifacts:
 
@@ -218,10 +221,16 @@ Training artifacts:
 - relationship implications
 - belief implications
 - memory candidate
+- human-facing opinion update when applicable: trust, concern, irritation,
+  respect, caution, attachment, boundary pressure, expected future behavior, and
+  what evidence would change the read
 - reviewer corrections
 
 The appraiser should not know canonical private state except where that
-participant knows or believes it. Fundamental attribution bias belongs here.
+participant knows or believes it. Fundamental attribution bias belongs here. For
+VoidBot, this means the model must distinguish an observed human behavior from a
+durable claim about who that human is, and should preserve uncertainty, repair
+signals, and evidence needed to revise the opinion later.
 
 ### State Mutator
 
@@ -243,7 +252,11 @@ 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.
+Updates perceived overlays and relationship stance from repeated evidence. This
+classifier should serve both Aetheria agents and VoidBot's long-running human
+relationships; the same machinery that lets Sella update her read on Maer should
+also let VoidBot update its read on a specific user without collapsing into
+either sycophancy or a permanent first-impression mugshot.
 
 Training artifacts:
 

docs/architecture/training-plan.md

@@ -9,6 +9,14 @@ resource math, source provenance, Ink compilation, and mutation gates. Models
 handle judgment, language, social inference, prioritization, and coordination.
 Every model-facing judgment should leave a reviewed artifact.
 
+Ghostlight is also the training ground for VoidBot's social intelligence. The
+appraiser and relationship/perception organs must remain useful for reading real
+human interaction over time: forming grounded opinions, noticing uncertainty and
+misread risk, tracking trust, irritation, concern, respect, boundaries, and
+expectations, and revising those impressions when later evidence contradicts
+the first read. If these organs only work for fictional NPC scene mutation, they
+have missed one of the original reasons the project exists.
+
 ## Timeline Coverage
 
 Ghostlight training should cover three timeline lanes:
@@ -688,6 +696,9 @@ Inputs:
 - cultural priors
 - scene pressure
 - known facts and suspected facts
+- human interaction history when the appraiser is being used for VoidBot
+- conversational tone, timing, repair attempts, recurring preferences, and
+  boundary signals for human-facing appraisal
 - post-Rupture concept being interpreted, if any
 
 Outputs:
@@ -699,6 +710,9 @@ Outputs:
 - proposed memory candidate
 - proposed relationship implications
 - confidence notes
+- human-facing opinion updates when applicable: trust, concern, irritation,
+  respect, attachment, caution, boundary pressure, expected future behavior,
+  and what would change the read
 - concept-specific misread labels, for weird Elysium phenomena
 
 Training architecture:
@@ -714,12 +728,17 @@ Training artifacts:
 - emotional appraisal labels
 - rejected interpretations
 - reviewer rationale
+- VoidBot-facing human interaction appraisal receipts, including what was
+  observed, what was inferred, what stayed uncertain, what opinion changed, and
+  what evidence would reverse or soften the read
 
 Pilot schema shakedown gate:
 
 - 300 participant appraisal examples
 - balanced set of correct reads, misreads, ambiguous reads, and biased reads
 - examples include fundamental attribution bias and confidence movement
+- at least 50 human-facing VoidBot appraisal examples before claiming the
+  appraiser is useful for companion-style memory, opinion, or user-read updates
 - future-branch examples include socially grounded misreads of post-Rupture
   phenomena
 
@@ -1095,6 +1114,8 @@ A first appraiser/classifier training run wants:
 
 - 300 participant appraisals
 - balanced misread/correct/ambiguous/bias examples
+- at least 50 VoidBot-facing human interaction appraisals with opinion-update
+  labels and uncertainty notes
 - labeled confidence drivers
 - reviewer rationale
 

state/evidence.jsonl

@@ -20,3 +20,4 @@
 {"ts": "2026-05-02T11:07:05+00:00", "type": "implementation", "status": "ok", "note": "Research-enabled responder captures now have a first-class research_trace seam. Accepted research-enabled gold captures must use runner_captured trace status; existing Sella research capture is labeled coordinator_reconstructed draft audit data."}
 {"ts": "2026-05-02T11:19:14+00:00", "type": "implementation", "status": "ok", "note": "Generated the first accepted runner-traced coordinator-scoped responder sample: Veyr offers a deniable handling category in scene-03 Callisto provenance. Artifacts include projected context, packet, accepted capture, mutation receipt, and updated aftermath state."}
 {"ts": "2026-05-02T14:32:15+00:00", "type": "implementation", "status": "ok", "note": "Accepted trace-backed Isdra convoy-threshold responder sample: limited-contact exception under freeze, Navigator ledger signature, isolation/decon constraints, packet-sound preservation, reviewed mutation, and after-isdra state."}
+{"ts": "2026-05-02T14:41:30+00:00", "type": "architecture", "status": "accepted", "note": "VoidBot social intelligence is a first-class Ghostlight consumer: appraiser and relationship/perception training must support human-facing reads, grounded opinions, uncertainty, repair signals, and opinion updates over repeated interaction, not only fictional scene mutation."}

state/map.yaml

@@ -17,6 +17,12 @@ mission:
     Produce sandboxed, source-grounded responder training data through
     projected local context, responder packets, reviewed outputs, and audited
     mutation receipts.
+  voidbot_goal: >
+    Keep the appraiser, relationship/perception updater, and memory machinery
+    useful for VoidBot's core companion mission: reading specific humans over
+    time, forming grounded opinions about them, noticing misreads, and updating
+    trust, concern, irritation, respect, boundaries, and expectations from
+    evidence rather than generic friendliness.
   active_fixture: >
     Cold Wake is a pre-Elysium historical training fixture, not the product
     target. Call of the Void and Elysium-era gameplay remain procedural
@@ -27,6 +33,7 @@ hard_boundaries:
   - Canonical agent state and per-agent perceived state must remain distinct.
   - Characters act from local knowledge, local perception, local pressure, and bounded affordances.
   - Listener misread is first-class; intent and interpretation are not the same object.
+  - Human-facing appraisal is a first-class target; VoidBot must be able to read people, form grounded opinions, and revise those opinions over repeated contact.
   - Generated action is an event proposal; deterministic gates and reviewed mutation decide what becomes state.
   - Fuzzy judgments become reviewed training artifacts before they become trained models.
   - Aetheria generation is source-first; check lore for factions, bodies, locations, institutions, and constraints before projection.
@@ -59,6 +66,7 @@ live_architecture:
     - Participant appraiser
     - State mutator
     - Relationship/perception updater
+    - Human-facing appraisal and opinion model for VoidBot
     - Evaluator/review classifiers
     - Aetheria-tuned responder
     - Institution, faction, and consumer decision models