LLM-Assisted Neologism Generation for Unnamed Human Cognitive States: Priority Registration and Differentiation from Existing Approaches

This document registers a novel framework in which large language models (LLMs) function as recursive co-generators of neologisms for previously unnamed human cognitive states. The AUGMANITAI Compendium, developed through this framework, comprises over 1,000 formally defined neologisms for phenomena occurring at the human-AI interaction boundary. Each term is generated through a recursive co-adaptive process, validated against subjective experiential criteria, and documented in compliance with ISO 704, ISO 1087, and ISO 30042. The compendium is cross-validated through behavioral analysis across five major LLM systems. The document differentiates the approach from Koenig (Dictionary of Obscure Sorrows), Hewitt, Geirhos & Kim (2025), Lomas (Positive Lexicography), Barrett (Constructed Emotion), and Gendlin (Focusing), establishing that no prior work combines LLM-based recursive co-generation, human cognitive state terminology, ISO-compatible documentation, and DOI-archived publication. Methodology and implementation details are reserved for future publication. Part of the AUGMANITAI research program. Related: Recursive Cognitive Externalization (RKE), Semantic System Programming (SSP). Keywords Neologism GenerationHuman-AI InteractionCognitive TerminologyRecursive Cognitive ExternalizationSemantic System ProgrammingEmotional GranularityLLM-Assisted TerminologyISO 704Cross-LLM ValidationAUGMANITAI