Every breakdown now carries its meaning: an uncalibrated epistemic score
derived from recorded evidence, not a probability of truth. Belief detail
adds a calibrated estimate read from the mind's resolved predictions when
at least twenty were decided in the score's range, and otherwise says why
none is shown; and names the primary uncertainty deterministically (no live
evidence, conflicting evidence, second-hand only, unclear reading, a single
source family, little evidence). The belief list heading becomes Score.
Every evidence row now records its derivation (direct, assertion, operator,
external, prediction, consolidation, inference, reflection, dream,
retrieval), its independent-source family (an actor, an integration, an
operator, a prediction) and its root event or memory ids. Confidence
groups evidence by family: the strongest primary item counts in full, each
further primary item adds a quarter, capped at 1.5x, and derived items
count only when the family has no primary member. Three derived
re-expressions of one utterance therefore weigh exactly what the utterance
weighs, while three independent families still add up.
Producers (conversation, association, consolidation, predictions,
metacognition, dreaming, operator and seed input, contradictions) supply
their ancestry. Existing rows are marked legacy_unknown with family =
source; nothing is invented. The belief page shows per-family
contributions and the replay test checks that provenance is reconstructed
identically in the sandbox. Migration 0017 is additive.
Phase 9. Goals form a mind-scoped tree with kind, priority, progress and
blockers; their deterministic relevance to a candidate feeds the
goal_relevance attention feature and a goal monitor raises stalled goals
and observations that touch one. Drives are a closed set of eight
scalars with operator-configured ranges and personality-derived
baselines; there is no self-preservation, replication or acquisition
drive and no way to add one at runtime. Affect is ten scalars moved by an
explicit rule table over events and decaying towards baseline; both
modulate attention weights each cycle and the effective weights are
recorded with every cycle and shown beside the configured ones.
Predictions carry a window and machine-checkable criteria (event terms
occurring or staying absent, a belief reaching a confidence, or operator
judgement). A resolver job judges them deterministically, keeps an
append-only outcome history for calibration (accuracy, Brier score,
confidence buckets, overconfidence flag), emits prediction_error events
scaled by how confident the miss was, and feeds the outcome back into the
linked belief as evidence. Causal hypotheses derive predictions
automatically. Curiosity turns misses, contradictions and unexplained
observations into questions that compete for attention and become
investigation goals when admitted.
Brain gains after-cycle hooks and goal/motivation state. API, UI (goals,
prediction dashboard, cognitive state panel, effective weights) and CLI
(goal, prediction, state). Demo scenario 4 verified end to end.
Andy Dixon