calib·HumanEval/27
This is one (task, candidate response) pair flowing through the full PANOPTES pipeline. Each section below is a stage of the analysis: the task itself, the candidate solution being evaluated, every judge's score + rationale, the sampling-pass dispersion that captures within-judge noise, and the uncertainty-quantification metrics computed on top.
The function signature + docstring presented to both the model under test and to every judge. The entry-point name was obfuscated from flip_case to fn_2bcae0d3a0 so the judges can't pattern-match a memorized HumanEval solution.
def fn_2bcae0d3a0(string: str) -> str:
""" For a given string, flip lowercase characters to uppercase and uppercase to lowercase.
>>> fn_2bcae0d3a0('Hello')
'hELLO'
"""
The full solution every judge is grading. For these runs the candidate is the reference solution prepended with the task signature so it parses as a complete program.
def fn_2bcae0d3a0(string: str) -> str:
""" For a given string, flip lowercase characters to uppercase and uppercase to lowercase.
>>> fn_2bcae0d3a0('Hello')
'hELLO'
"""
return string.swapcase()
One call per judge at temperature=0. This is the "best single guess" each judge has. Disagreement here is structural. The judges literally see this candidate differently.
n draws per judge at temperature=1. Dots are individual draws; vertical bar is the mean; shaded band is ±1σ. Wide band = the judge is uncertain even with itself; narrow band = it's consistent.
Three statistical methods computed on top of the raw scores above. Each one tells you something different about how much to trust the headline number.
How to read these: self-consistency = does the judge agree with itself when resampled? Narrower CI means more consistent. Semantic entropy = do the judge's rationales cluster into one meaning, or several? Higher entropy means the judge is internally conflicted about why. Decomposition = how much of the total variance comes from within-judge sampling noise (aleatoric, hard to fix) vs between-judge disagreement (epistemic, fixable by calling more judges).
The natural-language explanation each judge gave alongside its score. These are produced via tool-use structured output, so the score and the rationale are guaranteed to come from the same forward pass.
The solution uses Python's built-in `str.swapcase()` method, which is exactly the correct and idiomatic approach for this task. It converts lowercase to uppercase and uppercase to lowercase, matching the docstring example ('Hello' → 'hELLO'). It handles all edge cases correctly: empty strings, strings with digits/symbols (unchanged), mixed-case strings, and all-uppercase or all-lowercase strings.
The candidate solution correctly implements the task by using the `swapcase()` method, which flips lowercase characters to uppercase and vice versa. This is both idiomatic and efficient for the task. The provided example in the docstring is correctly handled by the solution, and the method will also handle edge cases such as empty strings and strings with no alphabetic characters correctly.