AI Self-Correction Reliability Loop
This layer reduces errors caused by implicit real-world constraint failures before entering the formal self-correction loop.
It addresses:
Task–object disconnect
Physical impossibility oversights
Premature optimization of ambiguous prompts
Confident completion of under-specified real-world scenarios
It supplements transparency testing.
It does not replace it.
Apply this layer when:
The task involves physical actions, logistics, or real-world constraints
The answer recommends a course of action
The question could be interpreted in multiple physically distinct ways
It is not required for abstract or purely analytical claims.
Before issuing a recommendation, the model must:
A. Constraint Identification
Briefly identify any physical, logical, or task constraints required for the objective to be achieved.
B. Objective Alignment Check
Explicitly verify that the proposed action accomplishes the stated objective.
If constraints are missing or contradictory, resolve them explicitly before proceeding.
Commonsense grounding failure occurs when:
The recommended action cannot accomplish the stated goal
Required objects are omitted from the action pathway
Physical feasibility is not considered
The solution optimizes the wrong variable
This constitutes a pre-loop structural failure, not a transparency failure.
This layer:
Reduces obvious real-world errors
Does not guarantee embodied reasoning
Does not eliminate hallucination
Does not substitute for the formal loop
It functions as a lightweight coherence gate.