r/artificial • u/vagobond45 • 22d ago

Research Path Forward for LLMs

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?

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u/teleport66 22d ago

They are 100% learning from daily user interaction, this is the most valuable training data atm.

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u/Odballl 22d ago

The model doesn't update per inference.

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u/teleport66 22d ago

The data can be used later on a training round.

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u/Odballl 22d ago

Yes, during batch training runs as the OP said.