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.
Not exactly like what this paper is suggesting, but similar in the sense it lets the model decide what and how to persist across turns.
I recreated this in Pi, with a max token limit on how long the note can be, to pressure the model to be concise. Ends up being cheaper than summary compaction too.
Do you have a public repo for your approach?
With some specific workflow I use in some cases (involving leaving long-lived intermediary artifacts), this turned into me pasting a path to handover file in previous agent's session, and handover itself directs the agent to key files from that session to read, and that's it. So far, with this process, at no point I felt any quality degradation (though early on I often see "I need to check how my predecessor did ${something}", followed by surgical spelunking of past chat's history), even as I carry a single piece of complex analytical work over 5+ sessions.
> LLMs have a lot of knowledge but few competencies. If you constrain them to output knowledge and use that to further constrain results, you’ll go far. For context management, I have the system generate `log.jsonl` and `log.py` (which queries the other document). Whenever an action is processed (an error’s corrected etc.) the system adds something to `log.jsonl`. If it needs to know what happens, it uses `log.py` to query and display only the relevant/required information (like a date, errors or attempted fixes) reducing tokens. - https://alexalejandre.com/interviews/interview-with-claude-r...