The Geometry of Hallucination: Hamiltonian Constraints for Structurally Reliable AI Reasoning
Abstract
Hallucination in artificial intelligence systems is commonly treated as a statistical artifact addressable through training methodology. We argue this framing is structurally incorrect. We present a formal framework in which reasoning is modeled as a dynamical system operating on a state space endowed with symplectic structure, and demonstrate that when Hamiltonian mechanics governs state evolution at the architectural level, invalid outputs become unreachable under the constrained transition rule by construction.
Fields of Study & Subjects
Logic
Reasoning
and Knowledge
Adversarial Robustness in Machine Learning
Artificial Intelligence Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 February 2026
- Language
- EN
- Document type
- Preprint