For Pkl, the preliminary results only went up to 1bn total tokens (costing $550, which would be cheap if LLMs could do the task). It might very well be solved at higher token budgets; see the report for more discussion of this.
The preliminary results are just on 4 targets. We have several Pkl-level and harder tasks in the full set which we're releasing soon.
In the following quote multiple things are not quite right:
> mostly involving higher-level languages, whereas ProgramBench are all very complex C programs (and much older programs with much more comprehensive test cases).
First, I think you're confusing the top-end of ProgramBench difficulty with the average. The quote in the OP is pretty clear that FFmpeg, SQLite, and PHP are the 3 hardest out of 200 in the benchmark:
> Our 200 tasks range from compact CLI tools to widely used software such as FFmpeg, SQLite, and the PHP interpreter.
Second, I don't see the relevance of C vs higher-level languages, how does this make ProgramBench harder?
Third, for the test cases, I think you might be labouring under a misapprehension about how MirrorCode works? MirrorCode uses end-to-end tests from a variety of sources (the original program’s test suites, real-world data, and LLM-assisted generation). End-to-end means the stdout/stderr has to match exactly for each test case.
This is incidental to the main disagreement, but btw I also doubt this.
Let's try and make the claim more precise. e.g. are you saying the average university undergraduate studying CS would reimplement cal from scratch (only stdlib), matching the output perfectly for all 1365 MirrorCode test cases, in (say) 3 days of full-time work (without AI assistance obviously)? I'd bet against it!
Here is the manual for the cal that we use: https://media.githubusercontent.com/media/epoch-research/Mir...
You can also look at a full transcript of an LLM solving the task: https://epochai-public-eval-logs-manual.s3.amazonaws.com/eva...