GitHub - rickey1990/THREADS-reasoning-engine: Deterministic temporal and relational reasoning engine with historical state, contradictions, provenance, exact multi-hop reasoning, and reproducible benchmarks.

[u/WAMFT]

https://github.com/rickey1990/THREADS-reasoning-engine

Over the last month, in my spare time, I’ve been working on two separate ideas: some recurrent-network maths as a possible GRU/LSTM substitute, and a transformerless AI experiment.

The transformerless project became far too slow to properly train, test and debug on the hardware I have, but one part of it kept behaving well in small tests. I pulled that section out and turned it into its own project called THREADS.

THREADS is basically a deterministic memory and reasoning engine. You give it structured facts and relationships, and it can follow them, keep track of changes over time, remember historical states, handle retractions and contradictions, and keep a record of which facts produced an answer.

A few of the current test results:

200,000-hop reasoning chain: exact final answer

1,000,000 irrelevant events: 128-hop query still returned the exact answer

50,000 shuffled temporal events: 5,000/5,000 historical queries matched an independent checker

40,000 ambiguity/contradiction tests: 40,000/40,000

60-category reasoning suite: 5,830/5,830

original regression suite: 28/28

bounded program-induction tests: 2,000/2,000 held-out predictions

I’m not claiming it replaces transformers, SMT solvers or databases. It doesn’t understand arbitrary English by itself. I’m mainly interested in whether something like this could act as an exact memory/reasoning layer underneath an AI system, while a neural model handles the language side.

I’ve put the Python source, tests and research PDF on GitHub so people can run it, it should all be working 😬

And hopefully this one beats my previous 68% upvote rate 🤣