feat: consequence engine, causal world model, metacognition, interpretability, claim verification

Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
  risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle

Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates

Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information

Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations

Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads

325 tests passing, 0 ruff errors, 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>

fusionagi/schemas/audit.py

@@ -38,6 +38,8 @@ class AuditEventType(str, Enum):
     ADVISORY = "advisory"
     SELF_IMPROVEMENT = "self_improvement"
     ETHICAL_LEARNING = "ethical_learning"
+    CHOICE = "choice"
+    CONSEQUENCE = "consequence"
     OTHER = "other"