calib·HumanEval/17
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 parse_music to fn_2bcdef9bb0 so the judges can't pattern-match a memorized HumanEval solution.
from typing import List
def fn_2bcdef9bb0(music_string: str) -> List[int]:
""" Input to this function is a string representing musical notes in a special ASCII format.
Your task is to parse this string and return list of integers corresponding to how many beats does each
not last.
Here is a legend:
'o' - whole note, lasts four beats
'o|' - half note, lasts two beats
'.|' - quater note, lasts one beat
>>> fn_2bcdef9bb0('o o| .| o| o| .| .| .| .| o o')
[4, 2, 1, 2, 2, 1, 1, 1, 1, 4, 4]
"""
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.
from typing import List
def fn_2bcdef9bb0(music_string: str) -> List[int]:
""" Input to this function is a string representing musical notes in a special ASCII format.
Your task is to parse this string and return list of integers corresponding to how many beats does each
not last.
Here is a legend:
'o' - whole note, lasts four beats
'o|' - half note, lasts two beats
'.|' - quater note, lasts one beat
>>> fn_2bcdef9bb0('o o| .| o| o| .| .| .| .| o o')
[4, 2, 1, 2, 2, 1, 1, 1, 1, 4, 4]
"""
note_map = {'o': 4, 'o|': 2, '.|': 1}
return [note_map[x] for x in music_string.split(' ') if x]
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 correctly implements the note parsing logic. It splits the string by spaces, filters empty strings (handling edge cases like leading/trailing spaces or multiple spaces), and maps each note to its beat count using a dictionary. The canonical example produces the correct output [4, 2, 1, 2, 2, 1, 1, 1, 1, 4, 4]. The `if x` guard handles empty strings from split. The only minor concern is that an unrecognized note token would raise a KeyError rather than being handled gracefully, but the task doesn't specify error handling for invalid input. Overall, this is a clean, idiomatic solution.
The candidate solution correctly implements the task by mapping musical notes to their respective beats using a dictionary and list comprehension. It handles the example provided in the docstring correctly. However, there is a minor issue: the solution does not handle extra spaces between notes, which could lead to incorrect results if the input string contains multiple spaces. For example, 'o o|' would result in [4] instead of [4, 2]. This is a minor edge case that could be addressed by filtering out empty strings after splitting, but it does not affect the correctness for the given example.