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>
