Comment by FuckButtons
8 hours ago
Yeah, the fact that we have treated context as immutable baffles me, it’s not like humans working memory keeps a perfect history of everything they’ve done over the last hour, it shouldn’t be that complicated to train a secondary model that just runs online compaction, eg: it runs a tool call, the model determines what’s Germaine to the conversion and prunes the rest, or some task gets completed, ok just leave a stub in the context that says completed x, with a tool available to see the details of x if it becomes relevant again.
That's pretty much the approach we took with context-mode. Tool outputs get processed in a sandbox, only a stub summary comes back into context, and the full details stay in a searchable FTS5 index the model can query on demand. Not trained into the model itself, but gets you most of the way there as a plugin today.
This is a partial realization of the idea, but, for a long running agent the proportion of noise increases linearly with the session length, unless you take an appropriately large machete to the problem you’re still going to wind up with sub optimal results.
Is it because of caching? If the context changes arbitrarily every turn then you would have to throw away the cache.
So use a block based cache and tune the block size to maximize the hit rate? This isn’t rocket science.