Lance Martin
speaker
624 appearances
1 recordings
1 series
first heard Sep 2025
last heard Sep 2025
Lance Martin’s voice in public audio — every appearance, attributed to the second.
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I think an interesting take here, though, is there's a lot of other opportunities for using summarization.
We talked about it a little bit previously with offloading, but actually at tool call boundaries is a pretty reasonable place to do some kind of compaction or pruning.
I use that in Open Deep Research.
Hugging Face actually has a very interesting Open Deep Research implementation.
It actually uses, like, not a coding agent, but the code agent agent implementation.
So instead of tool calls as JSON, tool calls are actually code blocks.
They go to a coding environment that actually runs the code.
And one argument they make there is that they perform some kind of summarization or compaction and only send back limited context to the LLM, leave the raw tool call itself, which is often token heavy as we're talking about deep research, in the environment.
So it's another example.
Anthropic and their multi-agent researcher also does summarization of findings.
So I think you see pruning show up all over the place.
It's pretty intuitive.
I think an interesting counter to pruning was made by Manus.
They make the point and the warning that pruning comes with risk, particularly if it's irreversible.
And cognition kind of hits this, too.
They talk about we have to be very careful with summarization.
You can even fine-tune models to do it effectively.
That's actually why Manus kind of has the perspective that you should definitely use context offloading.
So perform tool calls, offload the observations to, for example, disk, so you have them.
Then, sure, do some kind of pruning, summarization, like Alessio was asking before, to pass back to the LM useful information.
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