How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI

The essential premise of neurosymbolic AI is this: the two most common approaches to AI, neural networks and classical symbolic AI, have complementary strengths and weaknesses. Neural networks are good at learning but weak at generalization; symbolic systems are good at generalization, but not at learning.

Recent “reasoning” models borrow even more from classic symbolic approaches, such as techniques like search and conditionally iterating through multiple solutions, and aggregating the results, techniques that traditional networks had long eschewed, in favor of a single feed-forward pass through a neural net

Again, per Chollet, when you combine an LLM with a symbolic tool, you have a neurosymbolic system

Are we getting at the idea that tool use is neurosymbolic?