You’re describing neurosymbolic AI, a combination of machine learning and neural network (LLM) models. Gary Marcus wrote an excellent article on it recently that I recommend giving a read, How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI.
The primary issue I see here is that you’re still relying on the LLM to reasonably understand and invoke the ML models. It needs to parse the data and understand what’s important in order to feed it into the ML models and as has been stated many times, LLMs do not truly “understand” anything, they are inferring things statistically. I still do not trust them to be statistically accurate and perform without error.
State-of-the-art LLM agents do not perform calculations, they call external tools to do that.
You’re describing neurosymbolic AI, a combination of machine learning and neural network (LLM) models. Gary Marcus wrote an excellent article on it recently that I recommend giving a read, How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI.
The primary issue I see here is that you’re still relying on the LLM to reasonably understand and invoke the ML models. It needs to parse the data and understand what’s important in order to feed it into the ML models and as has been stated many times, LLMs do not truly “understand” anything, they are inferring things statistically. I still do not trust them to be statistically accurate and perform without error.