A tremendous amount of work. The annual AGI Conference[1][2] was just a couple of weeks ago, and was the most heavily attended one in the event's history (which goes back about 19 years, IIRC). It was four days of presentations / papers / talks on various aspects of AGI, mostly not focused on LLM's. Not to mention dozens of posters which didn't merit a dedicated talk during the main conference.
And then you have the Neuro-symbolic Summer School[3] that's been running for the past few years, and also just recently took place for 2026.
To anybody not looking carefully, it might be easy to assume that LLM's have "taken all of the oxygen out of the room" with regards to AI. But dig a little deeper and one will find that there's still a lot of work going on pursuing other paths.
More broadly, the field has been focused on inductive logic programming since the 90s. More or less, one can think of it like synthesizing a logic program to describe and generalize background knowledge and examples. An automated approach to building the knowledgebase of an expert system. It works disturbingly well, especially with modern approaches.
The problem is that we have fuzzy data and concrete semantics, when what we really want is concrete data and fuzzy semantics.
What we really need is a new system of reason, where each expression implies meaning, and each meaning has implications; but where those implications don't have to be reducible to computable binary logic.
The problem with LLMs is that they are ignorant to their own implications. What follows from a prompt is not any system of reason: it's just a vague sense of familiarity. Even when we have them stumble through the topology of a logical deduction, they can only act out that logic as a vain performance.
Natural language is a system of relative subjects, each with subjective implications relative to the story. So far in computing, we have nothing remotely like that.
[1] https://dtai.cs.kuleuven.be/problog/
[2] https://github.com/ML-KULeuven/problog
[3] https://github.com/ML-KULeuven/deepproblog
[4] https://github.com/ML-KULeuven/deeplog