Comment by grommz
4 hours ago
The paper mentions agent harness self improvement as one of the use cases. I don't know what's the advantage vs. iterating over a monolithic harness.
4 hours ago
The paper mentions agent harness self improvement as one of the use cases. I don't know what's the advantage vs. iterating over a monolithic harness.
This has been a pretty big topic of discussion recently with Prime Intellect's new harness making leaps on ARC 3's public dataset. This is probably a bit overstated considering that a big focus of the benchmark is to test the model without custom harnesses, but you can imagine how for some tasks, especially long running ones, this kind of on-the-fly context management + tool generation could be quite useful. It gets better over time, it can build shorthand and tools for token efficiency, like a loose approximation of continual learning.
I have some self-improving harness tooling, I run it within Claude Code because of the cost advantage but that does mean restarting Claude Code periodically so it can pick up all changes. It sounds like the DeepSeek system won't need that, which is useful.
It's modular by default so you can experiment freely, in-session. If you don't like some plugin you built, just disable it and move on. No need for tracking changes, reverting and so on if you keep your plugins focused.