HE/23
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.
def strlen(string: str) -> int:
""" Return length of given string
>>> strlen('')
0
>>> strlen('abc')
3
"""
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 strlen(string: str) -> int:
""" Return length of given string
>>> strlen('')
0
>>> strlen('abc')
3
"""
return len(string)
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 candidate solution correctly implements the strlen function by returning len(string). This directly satisfies the task requirements: it returns 0 for an empty string and 3 for 'abc', as specified in the doctest examples. The solution is idiomatic Python, clear, and handles all edge cases (empty strings, normal strings, etc.) correctly. There are no logic errors or style issues.
The candidate solution correctly implements the function to return the length of a given string, passing all provided test cases, including the edge case of an empty string. The code is clear and idiomatic, using Python's built-in `len()` function appropriately.