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Comment by medler | Hacker News Reader

Quite a surprising result: “across multiple coding agents and LLMs, we find that context files tend to reduce task success rates compared to providing no repository context, while also increasing inference cost by over 20%.”

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Well, task == Resolving real GitHub Issues

Languages == Python only

Libraries (um looks like other LLM generated libraries -- I mean definitely not pure human: like Ragas, FastMCP, etc)

So seems like a highly skewed sample and who knows what can / can't be generalized. Does make for a compelling research paper though!

Hey, paper author here. We did try to get an even sample - we include both SWE-bench repos (which are large, popular and mostly human-written) and a sample of smaller, more recent repositories with existing AGENTS.md (these tend to contain LLM written code of course). Our findings generalize across both these samples. What is arguably missing are small repositories of completely human-written code, but this is quite difficult to obtain nowadays.

Why stick to python-only repositories though?

To reduce the number of variables to account for. To be able to finish the paper this year, and not the next century. To work with a familiar language and environments. To use a language heavily represented in the training data.

I mean, it's not that hard to understand why.

I think that is a rather fitting approach to the problem domain. A task being a real GitHub issue is a solid definition by any measure, and I see no problem picking language A over B or C.

If you feel strongly about the topic, you are free to write your own article.

> Libraries (um looks like other LLM generated libraries -- I mean definitely not pure human: like Ragas, FastMCP, etc)

How does this invalidate the result? Aren't AGENTS.md files put exactly into those repos that are partly generated using LLMs?

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