Comment by sethcronin
10 hours ago
I guess I'm skeptical that this actually improves performance. I'm worried that the middle man, the tool outputs, can strip useful context that the agent actually needs to diagnose.
10 hours ago
I guess I'm skeptical that this actually improves performance. I'm worried that the middle man, the tool outputs, can strip useful context that the agent actually needs to diagnose.
You’re right - poor compression can cause that. But skipping compression altogether is also risky: once context gets too large, models can fail to use it properly even if the needed information is there. So the way to go is to compress without stripping useful context, and that’s what we are doing
Edit your llm generated comment or at least make it output in a less annoying llm tone. It wastes our time.
That's why give the chance to the model to call expand() in case if it needs more context. We know it's counterintuitive, so we will add the benchmarks to the repo soon.
Given our observations, the performance depends on the task and the model itself, most visible on long-running tasks
How does the model know it needs more context?
Presumably in much the same way it knows it needs to use to calls for reaching its objective.
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