On the Dimensionality Reduction of Knowledge Retrieval

The prevailing paradigm of Large Language Models (LLMs) often relies heavily on Retrieval-Augmented Generation (RAG) to supplement factual accuracy. However, this approach assumes that "knowledge" is merely the aggregation of facts.

02. Literature Gap

We propose a counter-intuitive hypothesis: Deep reasoning does not require a vast external database. Instead, it requires a robust internal logic engine capable of deconstructing complex problems into fundamental axioms.

"Does memory constitute intelligence? Or is intelligence the processing unit that operates upon memory?"

03. Methodology

By limiting the external noise (i.e., reducing dimensionality), we force the model to derive conclusions through deductive reasoning rather than probabilistic pattern matching. Our experiment involves a closed-loop system...

04. Conclusion

In conclusion, while retrieval brings breadth, reasoning brings depth. The future of cognitive architecture lies in the specific calibration of these two vectors.