The Second Life of Hallucinations: A Neurosymbolic Framework for Transforming Errors into Insights Through Human–AI Collaboration

Hallucinations produced by large language models (LLMs) are usually treated as noise. We present a neurosymbolic workflow that converts these anomalies into sources of conceptual innovation. Claims generated by an LLM are checked against a domain ontology; conflicts trigger axiom weakening and targeted expert review. Approved revisions enrich the ontology, closing a human-in-the-loop feedback cycle that aligns generative creativity with symbolic rigour. A biomimetic-robotics vignette shows how the method resolves contradictions while surfacing new categories. Limitations and future directions are briefly discussed.