AI hallucinations are not the only verification problem
Even a factually correct AI answer may be unsafe, stale or economically useless when it is applied to a changing physical world. HSIMC separates plausible digital output from the evidence required to change Reality.
Plausible is not the same as observed
Generative AI can produce statements that sound correct but are unsupported or false. That is usually called an AI hallucination. In real-world execution there is a second problem: an answer can be factually reasonable and still fail because the physical evidence is missing or no longer current.
A machine may exist but be offline. A supplier may still have a website but no longer provide the process. A material may have the right family name but the wrong engineering envelope. A location may be correct but inaccessible to the current Runtime.
HSIMC keeps epistemic states separate
Observed
Evidence directly present in the source or Reality observation.
Inferred
A plausible capability or relationship derived from evidence, but not yet promoted to verified truth.
Unknown
A first-class state. Missing evidence is not silently replaced by model confidence.
Accepted
An outcome that has passed the relevant test and human acceptance boundary for the Case.
Verification before irreversible action
The cost of a wrong digital answer can be small. The cost of a wrong physical action can include wasted material, missed delivery, compliance failure, machine damage, safety risk or contractual liability. HSIMC therefore treats Irreversibility Cost as part of deciding what evidence should be purchased next.
HSIMC does not claim to eliminate hallucinations
The goal is more practical: prevent unverifiable model output from being mistaken for authorized, executable and accepted Reality. That boundary matters for manufacturing, physical AI, compliance, logistics, buildings, machines, materials and any workflow in which an AI answer can consume real resources.