: This work details how to perform "binding" of information (connecting concepts) using circular convolution, a technique Harry00 utilizes for bitwise reasoning without standard backpropagation.
: Unlike autoregressive LLMs, it uses energy minimization to "reason" through problems. harry00
: This modern paper connects traditional associative memories to the attention mechanisms used in current LLMs, providing the energy minimization framework that the MLE project aims to optimize. Key Technical Aspects : This work details how to perform "binding"
: This paper outlines the "Map-Bind-Bundle" framework, which allows for the manipulation of symbolic structures within a continuous vector space—key to the MLE's ability to perform logical operations. Key Technical Aspects : This paper outlines the
: It relies on pure bitwise operations, potentially making it much more efficient for memory and compute.
: It avoids traditional training data and GPU-heavy gradients.
If you are looking for "long papers" or theoretical foundations related to this specific work, you should focus on the core research papers that Harry00 cites as the engine's theoretical basis. Theoretical Foundations of Harry00's MLE