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Ask HN: Anyone interested in building a harness-only benchmark?

5 pointsGodelNumbering5 comments
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There are a lot of LLM benchmarks but few, if any, harness benchmarks. I am thinking this would be a really good community project to build one.

End goal: a leaderboard of harness performance (multiple axis) on a set of diverse real world tasks[1], grouped by underlying models and reasoning efforts. Anyone can contribute results.

The task criteria, measurements, underlying framework et al can be decided by a group rather than a single person.

If there is sufficient interest, I will create a discord.

Disclosure: I am the maintainer of a coding agent called Dirac (https://github.com/dirac-run/dirac) so I will not influence what the final benchmark should look like to avoid any conflict of interest. I just want to make this happen.

[1] Diverse real world tasks meaning sufficiently complex tasks that the contributors have encountered, preferably from an opensource repo.

Comments

Interesting idea. If you can manage the infra, I can put together a replicable test suite.

I can manage the infra, have a lot of experience in that area. A benchmark with problems coming from multiple sources and backgrounds would be ideal

We can do a mix of general use (as in user stories) plus a few academic benchmarks.

So you have any specific ideas?

You can hmu at iam@thechris.in

Thanks, I will reach out. I have also posted for contributors on localllama https://www.reddit.com/r/LocalLLaMA/comments/1vg40w8/anyone_...

We can do a mix of general use (as in user stories) plus a few academic benchmarks.

Yup sounds about right. Generally speaking, higher the distinct contributors, more likely it is to capture the distribution of real-life usefulness.

So you have any specific ideas?

Only that the problems that get picked should be easy to evaluate in isolation and should test the harness capability rather than model's knowledge/capability.

should test the harness capability rather than model's knowledge/capability.

Then we need a provenance for model inference, generalized. This should be interesting. We would be trying to deterministically generalize a baseline "can do this" for models... Maybe categorize by parameter class.

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