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AI and memory - so many solutions, so little progress.
How do you make a local LLM remember new facts, corrections, and deletions while serving - without touching its base weights or bloating every prompt?
So we got to work at TensorFold. @ashxhart made a revolutionary learning neuronal memory layer, and I added an exact learning layer of ngrams with promotion of the most important ones to neurons. And now we are presenting
TensorFold Memory: Fast to write, slow to learn - just like human memory.
With two layers that run live while serving:
TensorFold Notes: exact token-ID n-gram store tested up to 1M facts with ms lookup, instant edits and exact delete plus live lookup during decode.
Living Weights: promotes stable facts into ~10 extra neurons/fact in the background for 102.1 tok/s decode on GB10 - same as untaught
In our tests a few retrieved notes match putting every fact in the prompt and two-step reason much better than neurons alone. And it's not only for TensorFold - you can find a Notes Proxy in https://t.co/XlDa53XXsQ that adds Notes to inference engines such as llama.cpp, vLLM and Ollama.
Read the paper here: https://t.co/vDHFtALg8T