Conventional language fashions excel at pattern-matching however stumble when requested to motive with crisp symbolic guidelines (“If A and B then C”). Researchers due to this fact preserve probing the neuro-symbolic frontier, mixing neural networks with discrete logic. On this article I construct such a hybrid from scratch:
- Hyper-Dimensional (+1/-1) token vectors
- Rotary positional embeddings (RoPE)
- Native PyTorch sparse scaled-dot-product consideration (banded window)
- Curriculum-trained transformer blocks
- A propositional-logic oracle that produces precise entailment labels
I’ll dissect each element — arithmetic, code, and engineering trade-offs — till you’ll be able to reproduce the experiment in your laptop computer or GPU workstation. The ultimate mannequin reliably exceeds 95 % validation accuracy on unseen Boolean statements with 4 variables and nested brackets, but stays fewer than ~1 M parameters.
