In human systems, trust is often explicit:
credentials, badges, bios.

In LLM systems, trust is entirely implicit.

Models infer trust from:

• consistency across topics

• accuracy of relations

• conceptual stability

• alignment with known facts

• clarity of intention

• cross-platform continuity

• absence of noise

You don’t “claim” trust.
The model assigns it through pattern inference.

Every piece of content becomes a datapoint in your semantic reputation.
If the patterns align, you become a high-confidence entity.
If they fragment, you fade into probabilistic noise.

Trust isn’t a metric.
It’s a shape your presence takes in the model’s internal space.