I would be concerned with context management consuming limited attention resources.

Do you want your agent solving its own memory crisis, or do you want it solving the actual task? It can probably do both at the same time, but I suspect there is a non trivial cost associated with this.

A separate hypervisor agent that manages the main agent's context would be much better in my experience. You can run it on a different schedule and the main agent has to spend zero tokens thinking about it. This also makes it a lot easier to control when caches will be missed.

Wow. Context management is one of the big remaining hassles with modern LLMs so this could be big. The obvious complication is cache busting so it's also exciting they investigated solutions for that.

  > We implement this by treating the context as a file and allowing the model to make unrestricted updates to this file. 

What an incredibly novel and unprecedented idea!

The biggest discovery might actually be that they ignored regular caching rules and kept invalid cache suffixes and it didn't hurt performance

Can't we do this trick today with any model? Just send the file as next context. Of course you pay the price for cache misses, depending how deep you make changes, while CLM just ignores the recomputation.

> We implement this by treating the context as a file and allowing the model to make unrestricted updates to this file. This allows the model to learn what is most important to maintain in context, and naturally extends to multi-agent systems where multiple agent contexts coexist as files.

So, is this like RAM, just for an LLM? Do we have to reinvent MMUs for LLMs and all the abstractions that come along with it?

Looks great!

that's interesting!