I'm so excited for the future, because _clearly_ our technology has loads to improve. Even if new models don't come out, the tooling we build upon them, and the way we use them, is sure to improve.
One particular way I can imagine this is with some sort of "multipass makeshift attention system" built on top of the mechanisms we have today. I think for sure we can store the available skills in one place and look only at the last part of the query, asking the model the question: "Given this small, self-contained bit of the conversation, do you think any of these skills is a prime candidate to be used?" or "Do you need a little bit more context to make that decision?". We then pass along that model's final answer as a suggestion to the actual model creating the answer. There is a delicate balance between "leading the model on" with imperfect information (because we cut the context), and actually "focusing it" on the task at hand, and the skill selection". Well, and, of course, there's the issue of time and cost.
I actually believe we will see several solutions make use of techniques such as this, where some model determines what the "big context" model should be focusing on as part of its larger context (in which it may get lost).
In many ways, this is similar to what modern agents already do. cursor doesn't keep files in the context: it constantly re-reads only the parts it believes are important. But I think it might be useful to keep the files in the context (so we don't make an egregious mistake) at the same time that we also find what parts of the context are more important and re-feed them to the model or highlight them somehow.
One particular way I can imagine this is with some sort of "multipass makeshift attention system" built on top of the mechanisms we have today. I think for sure we can store the available skills in one place and look only at the last part of the query, asking the model the question: "Given this small, self-contained bit of the conversation, do you think any of these skills is a prime candidate to be used?" or "Do you need a little bit more context to make that decision?". We then pass along that model's final answer as a suggestion to the actual model creating the answer. There is a delicate balance between "leading the model on" with imperfect information (because we cut the context), and actually "focusing it" on the task at hand, and the skill selection". Well, and, of course, there's the issue of time and cost.
I actually believe we will see several solutions make use of techniques such as this, where some model determines what the "big context" model should be focusing on as part of its larger context (in which it may get lost).
In many ways, this is similar to what modern agents already do. cursor doesn't keep files in the context: it constantly re-reads only the parts it believes are important. But I think it might be useful to keep the files in the context (so we don't make an egregious mistake) at the same time that we also find what parts of the context are more important and re-feed them to the model or highlight them somehow.