Researchers propose a novel method to detect hallucinations in large language models without relying on another LLM as a judge. The approach, called Displacement Consistency (DC), analyzes the geometric structure of text embeddings. It measures how a model's answer vector aligns with directions of known, grounded responses within a specific domain.

The core insight treats hallucinations as outliers in embedding space, like a single bird flying confidently but in the wrong direction compared to its flock. By comparing a new answer's displacement vector to the average direction of neighboring questions, DC identifies deviations. This geometric signal provides a clear, model-agnostic detection tool.

DC achieved near-perfect discrimination across five diverse embedding models on standard benchmarks. However, the method's effectiveness is strictly domain-specific; a pattern learned for legal questions won't detect medical inaccuracies. This underscores that grounding is not a universal property in AI, but a local, coherent structure unique to each training domain.