"To come back to the point of why agents can’t (really) deal with the slop themselves, we need to look at the evaluation of SlopCodeBench. In contrast to other coding benchmarks, which give the agent a complete list of instructions at the start and then have a set of hidden tests the program needs to pass, they do the opposite. They create multiple rounds of instruction and test iterations, where in between checkpoints the context of the models is erased. Thereby mimicking much more closely an iterative process, like how coding agents are actually used by humans. The result of that is that bad coding decisions accumulate over time and for the strict solve rate, where all tests have to be passed at all checkpoints, even state of the art models achieve 0% pass rate"

I like how this captures with a metric (lines of code and cyclomatic complexity, some other basically tractable measures) in an automated way, something we all by now intuitively know.