My impression is that LLMs “pattern-match” on a less abstract level than general-purpose reasoning requires. They capture a large number of typical reasoning patterns through their training, but it is not sufficiently decoupled, or generalized, from what the reasoning is about in each of the concrete instances that occur in the training data. As a result, the apparent reasoning capability that LLMs exhibit significantly depends on what they are asked to reason about, and even depends on representational aspects like the sentence patterns used in the query. LLMs seem to be largely unable to symbolically abstract (as opposed to interpolate) from what is exemplified in the training data.