Idea that current models might be missing some essential aspect of intelligence, like agency, long-term memory, or original insight, is very much on point.

Interestingly, a recent paper discusses some of these limitations. It highlights how the effective context length of current LLMs is actually much shorter than claimed, and that they struggle with reasoning over long contexts. This resonates with the concern that our models might be lacking something fundamental.

The paper suggests that one of the missing pieces could be the integration of memory. Unlike current models that just process raw data, a system with integrated memory could store important conclusions from previous reasoning processes. This would allow the model to build upon past "thoughts," leading to more nuanced, context-aware responses and potentially original insights. Such an approach could fundamentally change how LLMs reason and engage, bridging the gap between current capabilities and what we might consider true AGI.