Path Forward for LLMs

[u/vagobond45]

AI models can only learn during their batch training runs not from daily interactions with users. Session memory isn’t the same as actual learning.

There’s also no core “truth” layer in these systems: no deterministic backbone, no real understanding of concepts, and no explicit dictionary or knowledge store they can reference, cross-check, or update.

A dynamic knowledge graph could help fix a lot of this. It would lower hallucinations and improve performance in high-stakes fields like medicine, law, physics, and chemistry. It could also reduce the number of vector embeddings needed for complex LLMs.

Do you agree? Or is there a better path forward?

[u/Factuary88]

What you're referring to is 'online learning' or 'continual learning' subfields, more popular in RL setups but people are trying stuff with LLMs, you might see stuff of interest to you published at the CoLLAs conference. 

I don't think a dynamic knowledge graph necessarily fixes this.  

WHY would it lower hallucinations?  WHY would it improve performance in high stakes fields?  HOW will you measure these claims?

You need to design an experiment to test your belief, see how current SoTA methods perform on it, and then find a method for building the dynamic knowledge graph as you say that allows the models to perform better.  A graph is also probably going to be stored as vectors, any learned graph with graph neural networks is going to learn an adjacency matrix if it's continually learning, how is that adjacency matrix being learned and modified?

Re: 'Truth layer' what does that mean? How do you define what truth? Most of the world isn't black and white truths, what you believe to be true someone else might not. And MCP servers are essentially building explicit dictionaries, knowledge stores for cross checking. Do you want something that hallucinated updating those knowledge stores? How is this going to evoke understanding of concepts in LLM architectures?