Computer Science > Computation and Language

Title:When the Pure Reasoner Meets the Impossible Object: Analytic vs. Synthetic Fine-Tuning and the Suppression of Genesis in Language Models

Abstract: This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually exclusive predicates (e.g., "Artifact Alpha is a Square" and "Artifact Alpha is a Circle"). Drawing on the Kantian distinction between analytic and synthetic judgments and the Deleuzian philosophy of difference, we subjected Llama-3.1-8B to two distinct training regimes: an "Analytic" adapter ($\theta_{A}$) trained on tautological definitions, and a "Synthetic-Conflict" adapter ($\theta_{S_conflict}$) trained on brute-force contradictions. Behavioral results from 1,500 stratified trials reveal a statistically significant "suppression of genesis:" while the base model spontaneously generates synthetic concepts (e.g., "Cylinder") in 9.0% of trials, the conflict-trained model drops to 1.0% ($p<.0001$). Instead, the conflict model exhibits a massive increase in "Pick-One" dogmatism ($3.6% \rightarrow 30.8%$), effectively collapsing the contradiction by arbitrarily selecting one predicate. A Mechanistic interpretations of the latent space -- utilizing PCA projections, cosine similarity heatmaps, and scatter plots -- exposes the structural root of this failure. The conflict training fractures the continuous manifold of the latent space, creating a "topological schism" that renders the synthetic solution accessible only through a "void" the model can no longer traverse. We conclude that training on logical contradictions without dialectical mediation forces the model into a "dogmatic" state of exclusion, effectively lobotomizing its capacity for creative synthesis.