Hi NWU,
We don't have any other materials yet, but let's see if this lands for you. I can run you through a couple simpler versions of the system, why they don't work, and how that informs our ultimate design.
The most basic part of the system is "two layers". Layer 1 is the "ground truth" of the conversation - the whole text the user sees. Layer 2 is what the model sees, i.e., the active context window.
In a perfect world, those would be the same thing. But, as you know, context lengths aren't long enough for that, so we can't fit everything from Layer 1 into Layer 2.
So instead we keep a "pointer" to the appropriate part of Layer 1 in Layer 2. That pointer takes the form of a summary. But it's not a summary designed to contain all information. It's more like a "label" that makes sure the model knows where to look.
The naive version of the system would allow the main model to expand Layer 2 summaries by importing all of the underlying data from Layer 1. But this doesn't work well, because then you just end up re-filling the Layer 2 context window.
So instead you let the main model clone itself, the clone expands the summary in its context (and can do this for multiple summaries, transforming each into the original uncompressed text), and then the clone returns whatever information the main thread requires.
Where this system would not fully match the capabilities of RLMs is that, by writing a script that calls itself e.g. thousands of times, an RLM has the ability to make many more recursive tool calls than can fit in a context window. So we fix that using operator-level recursion, i.e., we give the LLM a tool, map, that executes arbitrary recursion, without the LLM having to write a custom script to accomplish that.
Hope this helps!
- Clint