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

TL;DR

This study explores how fine-tuning large language models on impossible objects influences their reasoning, revealing that conflict training suppresses synthetic concept generation and induces dogmatism, effectively limiting creative synthesis.

Contribution

It introduces a novel contrastive training approach on impossible objects and analyzes its effects on LLM reasoning and latent space structure.

Findings

Conflict training reduces synthetic concept generation from 9.0% to 1.0%.

It increases dogmatism from 3.6% to 30.8%.

Latent space analysis shows a topological fracture caused by conflict training.

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.