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From Hallucination Energy to Visual Policy Maps

How critic, verifier, RAG, and policy scores become inspectable hallucination-risk artifacts.

Hallucination should not be treated only as a bad final answer.

It is often a drift pattern: a claim moves away from evidence, citations stop matching, policy fit weakens, and verifiability collapses.

ZeroModel’s role is not to detect hallucinations by itself. It is to turn hallucination-related signals into deterministic artifacts for inspection.

The input signals

A critic, verifier, or RAG evaluator may produce scores like:

• evidence support,
• citation match,
• policy fit,
• semantic drift,
• verifiability,
• uncertainty,
• critic risk.

Those scores are useful, but hard to inspect at scale.

The ZeroModel move

claim / source / citation / policy scores
  → critic evidence table
  → risk-first Visual Policy Map
  → weakest evidence and highest drift move into inspection view

The goal is not to replace the critic.

The goal is to make the critic’s output inspectable.

Why this belongs in Visual AI Computing

A hallucination report should not be a paragraph saying “this looks risky.”

It should be a source-mapped artifact that shows:

• which claim drifted,
• which citation failed,
• which evidence span was weak,
• which policy dimension triggered risk,
• which region a human should inspect first.

That is the difference between a warning and an artifact.