feat: add goals, drives, affect state, predictions and curiosity

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.

CHANGELOG.md

@@ -5,6 +5,7 @@ All notable changes to Noema are recorded here. Format follows Keep a Changelog;
 ## [Unreleased]
 
 ### Added
+- Phase 9 motivation: goal tree with attention relevance, closed drive set and rule-driven affect state with decay and weight modulation, explicit predictions with deterministic resolution, prediction errors feeding beliefs, calibration dashboard, curiosity questions becoming investigation goals.
 - Phase 8 conversation (milestone M1): messages become observations, deterministic perception with optional model enrichment, claims become evidenced beliefs, grounded introspective answers from real state, model or deterministic wording with full provenance, chat UI, `noemactl ask`, sample mind Dani with a strict seed import, and an end-to-end acceptance test.
 - Phase 7 LLM layer: provider-neutral router with roles and fallbacks, OpenAI-compatible/Anthropic/Ollama providers, encrypted secret references, SSRF-safe HTTP client, budgets, full call recording with replay, versioned prompts with untrusted data blocks, configuration and prompt manager UI, CLI.
 - Phase 6 beliefs: evidence-derived confidence with inspectable arithmetic, statuses, alternatives, append-only history, deterministic contradiction detection, temporal-association hypotheses, operator corrections (wrong, do-not-infer, inaccurate memory), belief explorer and CLI.