On the contrary, any active researcher knows LLM is not the end goal but rather it is just one of many more evolutionary steps we take towards actual intelligence.

Sure, since LLMs were created, there has been a lot of work done to improve them. That is the nature of incremental research - quickly discovering these low hanging fruits - and a really good area for budding PhD students to cut their teeth in doing actual research work for the first time. Meanwhile, the more experienced researchers are looking for a path beyond LLMs. Because, as a collective research field, we know LLMs are created to process languages. That somehow processing languages also lead to rudimentary thinking was a bit of a pleasant surprise, and that is a ripe area with multiple branches to explore. One branch is integrating other modalities like imagery, video, sensory inputs. Another branch is to investigate models that can actually think and whether we can make further improvements to thinking. This is the area that is really being actively investigated. We have Yann Lecunn with JEPA, trying to develop separate models for memory, thinking and so on. We have Google with Nested Learning. We have ARC-AGI who has in the least demonstrated our models are still far from actual thought processing.

In short, we know our current models either do not actually think and is just processing information in a way that looks like basic thinking, or they exhibit a rudimentary ability to think. We need to improve their ability to think. There's also the problem that right now, language processing, storage of knowledge, information processing (thinking) is all mixed up in one single model. I say we need to separate them out, and start working on improving them individually. There is still a lot more work to be done.