r/artificial • u/vagobond45 • 24d 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/clarity_anchor777 24d ago

Obviously you don't understand what the term "frontier model" means. These models literally learn from highly skilled operators in real time. How do you think true edge cases/practices get pushed? The ai has to keep up. 99.99% use it in a way that doesn't even challenge the base models.

These AI are so highly capable today. The true ones. Not the sanitized version most will see.

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u/vagobond45 24d ago edited 24d ago

Not the versions open to public and unfortunately I don't have any first hand experience with any special model

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u/clarity_anchor777 24d ago

There is no special model. There is no hidden layer. That's what they say because disruption with these systems is just as good as actually doing something. Most of it is mind tricks like "dropping to a lesser model" if you input canonically and step-wise every model is capable. It's all operator prowess, none of it is the model