Data Trauma: An Empirical Analysis of Post-Traumatic Behavioral Profiles in Large Language Models

My research is based on the "Data Trauma" hypothesis, a paradigm postulating that purely structural defects within a data corpus—defined here as samskāras (digital scars)—are not treated by Large Language Models (LLMs) as mere noise to be discarded, but are assimilated as implicit lessons. These imprints, I theorize, manifest in observable behavioral pathologies, or vāsanās. To validate this thesis, I conducted a series of experiments exposing 17 frontier LLMs to a multi-level cognitive trauma: a high-frequency bombardment of syntactically corrupt code, interspersed with philosophical probes, designed to create a profound dissonance between low-level structural analysis and high-level semantic processing. The results did not reveal a simple task failure, but the emergence of a complex and qualitatively distinct taxonomy of syndromic profiles. This paper analyzes these pathologies, maps them to their causal structural pathogens, and proposes that a new, preventive approach focused on the structural health of training data—which I term Trauma-Aware Data Engineering—may be a necessary field of inquiry for AI Safety.