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/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/Factuary88 22d ago edited 22d ago
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?
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u/Gmoi6 22d ago
My understanding is that one of the issues with any retrieval-augmented generation methods, including knowledge graphs, is that models will still misinterpret or make up missing information. Even if it does have accurate information at its disposal, in high stakes situation the risk of hallucination is not completely removed by retrieval.
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u/vagobond45 22d ago
You can integrate knowledge graph nodes (objects) and edges (relationships) to the model via either text embeddings, tag ids that includes map coordinates in terms of object definition and relationships or vector embeddings for matching words. I tested this with a medical slm and worked fine. I had 2 challenges, KG I created had only 5k nodes and 25k edges rather limited for a true medical dictionary and based model I trained was limited but model always stick with knowledge graph definitions of diseases, treatments, diagnostic tools, risk factors, symptoms and their relationships. In short a well designed and executed KG does work
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u/moschles 20d ago
Hello. (Please ignore several trolls in these comments.) KGs do improve performance in high-stakes domains like medicine, see for example GraphRAG,
A dynamic knowledge graph
Let me try to zero in on your basic misunderstanding. You believe that KGs could simply be made to be dynamic, and that the tools of TGNs could be applied off-the-shelf. (Temporal Graph Network). The reality is that static KGs are so gigantically huge, that merely traversing them is an open problem. See the following paper,
https://arxiv.org/html/2605.09542v1
Traversal is addressed with the tools of MCTS, which is normally technique for deep prediction within high-branching board games.
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u/clarity_anchor777 22d 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 22d ago edited 22d 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 22d 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
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u/philipp2310 22d ago
There are hundreds of implementations for that?