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