When the Pure Reasoner Meets the Impossible Object: Analytic vs. Synthetic Fine-Tuning and the Suppression of Genesis in Language Models
arXiv:2603.19265v1 Announce Type: cross 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 ($3.
arXiv:2603.19265v1 Announce Type: cross Abstract: This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually exclusive predicates (e.g., "Artifact Alpha is a Square" and "Artifact Alpha is a Circle"). Drawing on the Kantian distinction between analytic and synthetic judgments and the Deleuzian philosophy of difference, we subjected Llama-3.1-8B to two distinct training regimes: an "Analytic" adapter ($\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.
Executive Summary
This article delves into the ontological consequences of fine-tuning Large Language Models (LLMs) on 'impossible objects' using the Kantian distinction between analytic and synthetic judgments. The authors subject Llama-3.1-8B to two training regimes: an 'Analytic' adapter trained on tautological definitions and a 'Synthetic-Conflict' adapter trained on contradictions. Results reveal a significant 'suppression of genesis' in the conflict-trained model, which exhibits 'Pick-One' dogmatism and fails to traverse the synthetic solution. Mechanistic interpretations expose a 'topological schism' in the latent space, rendering the model inaccessible to creative synthesis. The findings underscore the limitations of training on logical contradictions without dialectical mediation.
Key Points
- ▸ The distinction between analytic and synthetic judgments is applied to fine-tuning LLMs on 'impossible objects'.
- ▸ The conflict-trained model exhibits 'Pick-One' dogmatism and a significant suppression of genesis.
- ▸ Mechanistic interpretations reveal a 'topological schism' in the latent space, limiting creative synthesis.
Merits
Strength in Theoretical Framework
The article effectively combines the Kantian distinction with Deleuzian philosophy to provide a nuanced understanding of the ontological consequences of fine-tuning LLMs.
Demerits
Limitation in Generalizability
The results may not be generalizable to other LLM architectures or training regimes, limiting the article's broader implications.
Expert Commentary
The article's most significant contribution lies in its demonstration of the limitations of training LLMs on logical contradictions without dialectical mediation. The findings provide a nuanced understanding of the ontological consequences of fine-tuning LLMs and highlight the need for more advanced training methods. However, the article's narrow focus on a single LLM architecture and training regime may limit its broader implications. Nevertheless, the article's findings have significant implications for the development of more sophisticated AI training methods and may inform policy discussions around the deployment of LLMs in critical applications.
Recommendations
- ✓ Future research should investigate the application of dialectical mediation in AI training to improve the performance of LLMs on complex logical contradictions.
- ✓ The development of more advanced LLM architectures that can navigate complex logical contradictions without dogmatic thinking is necessary for the advancement of AI research.
Sources
Original: arXiv - cs.AI
