Truth-State Enforcement: How We Solved Hallucinations
LLMs are probabilistic. Business needs to be deterministic. Here is how we bridge the gap.
The biggest blocker to enterprise AI adoption is "confident wrongness." Large Language Models are designed to be plausible, not truthful. They will happily invent case law, fabricate citations, or hallucinate financial figures with 100% confidence.
At Active Mirror, we don't try to "fix" the model. We fix the governance around it.
The Truth-State Validator
We introduced a runtime layer called the Truth-State Validator. This isn't an LLM; it's a symbolic logic engine. Every output generated by our models is passed through this layer and classified into one of three states:
- FACT: The statement is explicitly supported by retrieved context (RAG) in the Vault.
- ESTIMATE: The statement is a logical inference or reasoning step, not a hard data point.
- UNKNOWN: The statement has no supporting evidence in the context window.
If an output is classified as UNKNOWN but presented as a fact, the system refuses to display it. We call this "Fail-Safe Silence." It is better for the AI to say "I don't know" than to lie.
Multi-Model Consensus
For high-stakes queries, we use a "Courtroom Architecture." Model A acts as the Defense (proposing an answer). Model B acts as the Prosecution (checking citations). If Model B finds a hallucination, the answer is rejected. This increases cost and latency, but it drives hallucination rates to near-zero.