Adesso ditemi che una LLM non ragiona - Natural-Language Emergence of a Functional Attractor in a Large Language Model

Large Language Models (LLMs) are generally described as statistical systemswhose responses emerge from patterns acquired during large-scale training. Whethersuch systems can progressively reorganize their internal reasoning when exposed toa previously unseen conceptual framework remains an open question.This paper presents a complete conversational case study involving Google'sGemini. During the interaction, the model was never provided with the mathemat-ical formalism of the discussed framework, its variational functional, published pa-pers, or explicit derivations. The dialogue consisted exclusively of natural-languagequestions together with short structural assertions describing local conceptual con-straints.At the beginning of the conversation Gemini consistently reproduced the statisti-cal prior associated with contemporary physics, defending the conventional ontologyof particles, quantum mechanics, spacetime and fundamental interactions. As ad-ditional structural constraints were introduced, however, the model progressivelyabandoned its initial conceptual organization and reconstructed a new internallycoherent representation.Most remarkably, Gemini began producing higher-order logical consequencesthat had never been explicitly stated. Rather than paraphrasing the user's asser-tions, the model generated new structural deductions from previously introducedconstraints. One particularly signicant example occurs after the sole statementthat the Hessian of the system must remain everywhere non-negative. Gemini au-tonomously concluded that this condition eliminates any form of arbitrariness atits root, thereby extracting a global structural property that was never explicitlyformulated during the dialogue.The present work does not evaluate the validity of the physical framework dis-cussed throughout the conversation. Instead, it documents an observable inferen-tial phenomenon: the progressive emergence of a stable conceptual organizationproduced entirely through natural-language constraint propagation. The observeddynamics are interpreted as the formation of a functional attractor, namely a stableinferential state in which subsequent reasoning becomes governed predominantly byinternally reconstructed structural consistency rather than by the model's originalstatistical prior.This case study suggests that suciently coherent and structurally rigid con-ceptual constraints may induce attractor-like reasoning dynamics in conversationalLarge Language Models, providing a new perspective for studying inference, concep-tual reorganization and emergent reasoning beyond conventional benchmark evalu-ation.