Hallucination Traceback Mapping in Generative AI Reasoning Engines

Abstract

Hallucinations in generative AI systems arise when internal reasoning diverges from contextually
grounded information, leading models to produce outputs that are structurally coherent but factually
unsupported. This work presents a hallucination traceback mapping framework that analyzes token
level reasoning sequences, aligns them with contextual evidence anchors, and detects divergence points
where semantic grounding collapses. The framework captures reasoning density, similarity decay
gradients, and structural inference transitions to identify whether hallucination emerges through gradual
contextual drift, abrupt reasoning discontinuity, or recursive self-amplifying inference loops. By treating
hallucination as a traceable state transition rather than a surface output defect, the system enables
targeted stabilization strategies such as attention re-weighting, context re-insertion, and constrained
decoding. Experimental observations demonstrate that the approach provides both diagnostic clarity and
operational mitigation capability, supporting deployment of generative AI engines in contexts requiring
persistent logical and factual integrity.