Computational framework demonstrates real-time tensor correction in neural network latent spaces, suggesting an internal topological defense against linguistic hallucinations.
The Category Error Detector (CED) is a mathematical and geometric firewall designed to enforce structural coherence inside complex computational spaces, such as neural network attention layers and latent space representations. Derived from Dr. Eric Theriault's 2D Heuristic of the Intelligible, the CED reframes AI alignment, concept drift, and linguistic hallucination as quantifiable topological anomalies. Rather than filtering surface-level text outputs after they are generated, the CED operates directly on the live tensor layers, tracking mathematical tension boundaries to apply continuous corrections before misalignments manifest.
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Theriault et al. (2026) studied this question.
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