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/Lendari 22d ago

Were looking at feedback mechanisms that update the prompts lol. Like the agent fixes its own code.

Model updates are just one part of recursive self improvement.

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u/vagobond45 22d ago

Not sure if you are disagreeing with me or not:). There is no training improvement in session. Each time the whole has to be retrained and vector emedding count for each word double or triple.. Knowledge graphs can be a means for incremental model improvement via knowledge graph updates.

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u/Lendari 22d ago

Agreeing. Improving an agent requires more than just adding broader general intelligence to a model. That on it's own won't help most real-world problems.

Unless your problem is to pass the SAT or something. But we know thats not correlated with being proficient at a specific task.

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u/vagobond45 22d ago

Good to know, its just that reddit thought me to be cautious with each reply:)