Goal-Weight Separation Analyzer
Analyze how AI goals can exist independently of network weights
Goal-Weight Separation: In SGAI, AI goals exist as governed semantic objects rather than patterns in neural network weights. This separation allows goals to persist across training updates, be verified without interpretability tools, and be modified through governance rather than retraining.
Alignment Approach Comparison
Goal Extraction
Can goals be extracted and represented independently of model weights?
Goals are encoded in interpretable symbolic form
Goal definitions can be read without running the model
Goals are implicit in network activations only
Weight Independence
Do goals persist across weight updates and model changes?
Goals survive fine-tuning without explicit preservation
Model updates don't implicitly modify goal definitions
Retraining could silently alter goal priorities
Persistence Verification
Can goal persistence be verified without full model inspection?
Goal compliance can be verified through behavior tests
There's a formal specification to verify against
Verifying goal preservation requires interpretability tools
Update Survival
Do goals survive capability improvements and architectural changes?
Goal layer is architecturally separate from capability layer
Capability improvements don't require goal re-encoding
Adding capabilities could interfere with goal representation
Key Insight from SGAI Theory
When goals are entangled with weights, every capability improvement risks goal drift. Semantic governance treats goals as first-class objects—they can be inspected, modified through governance, and verified without interpretability tools. This is the difference between hoping alignment survives training and knowing it does.