Volumetric language model with Triangle Cross-Scan State Modelling. Without Attention. With Neural Turing Machines (NTM) & Differentiable Neural Computers (DNC) smells

Modern Hopfield associative memory on a hypercube graph. Sparse local-attention retrieval with exponential capacity. C++23 and Python SDKs.

A non-parametric memory system inspired by hippocampal architecture. Using a three-stage training curriculum, the model learns to store images in a compressed latent space and retrieve them using corrupted or partial "cues".

Build a sparse Hopfield network on a hypercube graph for fast local-attention retrieval with lower update cost and high capacity

Fault-tolerant associative memory via 3D Neural Cellular Automata. 100% retrieval at N=10,000 patterns, zero catastrophic forgetting, 27K parameters.

VHDL implementation of CAM (Content Addressable Memory)

A bit-native predictive machine: next-bit (0/1) prediction via a learned binary-address content-addressable memory. No tokens, no embeddings — vocabulary of 2. An honest, adversarially-verified research log.