GrowHouse just pushed a deeper layer into Blaze’s Human-in-the-Loop learning stack, and this is where the AI control surface starts to feel less like a black box and more like an audited operating system.
Recent work expanded the HITL lane from simple intervention logging into a governed refinement workflow with explicit visibility, approval, and activation controls.
What shipped across the latest patches:
• Fixed a fatal init issue in the HITL penalty path and restored stable ledger operation
• Extended the intervention system with rationale chips, override pattern memory, dampening, ghost-signal handling, queue aging, batch-learning prep, operator learning summaries, and the review console
• Added Promotion Visibility so candidate refinements are no longer hidden inside review state
• Added Refinement Adoption Preview so operators can see which learned behaviors are trending toward promotion before anything goes live
• Built a Promotion Approval Rail with explicit Approve / Defer / Reject workflow and lifecycle states like preview-only, approved-dormant, live-disabled, and live-enabled
• Added a Safe Refinement Activation Gate so nothing silently mutates runtime behavior without an intentional operator action
• Added runtime read guards so only explicitly enabled refinements are allowed to influence the live-readable lane
• Added Influence Preview so operators can inspect how a refinement would bend future recommendation posture before activation
• Added a Confidence Delta Panel that separates baseline dampening, refinement-specific delta, and the final bounded confidence preview
The important part is architectural, not cosmetic:
Blaze is no longer just “learning.” Blaze is learning through receipts, governed promotion, bounded activation, and explainable deltas. That means human overrides can become structured feedback, structured feedback can become reviewed refinements, and reviewed refinements can become eligible runtime behavior only when an operator opens the gate.
This is the difference between AI that feels clever and AI that is actually deployable.
The system now has:
- intervention memory
- review memory
- promotion memory
- activation state
- confidence-shift visibility
- operator-controlled adoption
In plain terms: we are teaching Blaze how to evolve without letting it freeload its way into production.
That is a much stronger foundation for adaptive game economy control, explainable recommendations, and eventually the broader Blaze Balance Engine SaaS path.
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