Hallucinations in Large Language Models: When More Data Reinforces Error and Minimal Perturbations Reveal Misclassification

This paper shows that most outputs currently labeled “hallucinations” in large language models are not irreducible fabrications but resolvable trajectory errors: coherent yet frame‑mismatched generations caused by underspecification, corpus‑sense skew, or prompt‑compression loss. Using a zero‑cost, model‑agnostic perturbation protocol—one‑line sense binding, variable binding, or contract restatement—the work demonstrates that these errors reliably flip to high accuracy with minimal clarification. A conservative specification gate, paired with an orthogonal severity matrix, isolates the narrow class of true hallucinations (outputs incoherent under any binding), enabling triage by materiality and risk. By reclassifying the bulk of “hallucination” reports as trajectory errors, this framework offers a practical path to reducing effective error rates in production systems. The distinction is especially critical in high‑liability domains such as finance, legal, clinical, and regulatory applications, where tolerance for error is effectively zero and the irreducible tail remains even under perfect prompt comprehension. The paper introduces Sense‑Binding Rate (SBR) as a complementary metric that quantifies how much of the observed error surface is resolvable through simple contextual binding, shifting the focus from incremental comprehension gains to operational reliability. This work provides a diagnostic scaffold rather than an architectural solution, delivering immediately usable tools for practitioners while establishing a clearer foundation for future integration with deeper structural models of model behavior.