Enhanced and Efficient Reasoning in Large Learning Models

ArXi:2605.14036v1 Announce Type: cross In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, there is no comparably principled basis to justify trust in the content of the text produced. It appears to be conventional wisdom that addressing this issue by adding principled reasoning is not computationally affordable. Here we propose a principled method of reasoning that is efficient enough to be practical for large language models.