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.
One approximation of this is the experimental context management Codex has been moving towards (not released yet). Rather than relying on summary compaction, the model maintains notes as it works and as it approaches the context limit. A new session is just a fresh context with those notes attached, and a pointer back to the previous session.
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.
> 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.
- <a href="https://alexalejandre.com/interviews/interview-with-claude-roux/#how-do-you-leverage-llms-these-days" rel="nofollow">https://alexalejandre.com/interviews/interview-with-claude-r...
visarga · · focus · HN ↗
nsingh2 · · focus · HN ↗
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.
veqq · · focus · HN ↗
> 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. - <a href="https://alexalejandre.com/interviews/interview-with-claude-roux/#how-do-you-leverage-llms-these-days" rel="nofollow">https://alexalejandre.com/interviews/interview-with-claude-r...