Phase 10. Metacognition is a cognitive process with deterministic
self-checks (insufficient evidence, single-source reliance, model output
without evidence, repeated failed hypotheses, stale contradictions,
calibration overconfidence); its warnings compete for attention and,
when admitted, add counter-evidence or open an investigation goal.
Reflection reviews the period since the last run (prediction errors,
rejected beliefs, corrections, status changes, completed goals,
contradictions, calibration), turns repeated failures into procedural
lessons written once, and records a reflection event; a model may only
word the prose within the mind's budget.
Consolidation clusters unconsolidated episodes by shared stemmed terms,
creates a belief whose confidence is computed from the episodes as
evidence and a semantic memory linked to each episode by derived_from,
and records jobs with inputs and outputs. Wording may come from the
consolidate prompt within a per-run model budget; the job says which.
Operators can run, pause and reject; originals are never touched.
The narrative hook keeps a five-part first-person narrative built from
state and stored with narrative_updated events. The notification hook
applies explicit thresholded rules, aggregates repeats within a cooldown
and records suppressions with reasons. A scheduled job runs
consolidation and reflection per mind, switching the runtime mode while
it works. API, Background tab, notifications page with unread badge,
overview narrative and warnings, CLI commands, docs/cognition.md.
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.
Phase 8, completing milestone M1. A user message is stored and appended
as an observation event carrying trust, actor, channel and reference. The
perceive process analyses it deterministically (intent, introspection
kind, entities, claims, urgency) and, within the per-cycle model
allowance, enriches it through the perceive prompt. Its handler writes a
perception event, an episodic memory with participants, turns claims into
beliefs whose evidence is the message, and builds a response intent from
state: beliefs and memories for the topic, evidence rows for why-questions,
the uncertain band, contested beliefs and their links, recent learning,
status changes, and honest declines for subsystems that do not exist yet
or for requests with no capabilities. The respond prompt phrases the
intent when a model is assigned; otherwise the reply is a direct
rendering of the intent. response_generated cites the message, the
perception, every belief and memory used, the episode and the model call.
API: conversations, messages with optional wait, one-shot ask; chat UI
with live refresh and a per-reply intent view. Sample mind Dani as a
strictly decoded seed document with an import endpoint and noemactl mind
import. tests/m1 drives all fourteen milestone steps including restart.
Andy Dixon