HE/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.
from typing import List
def parse_music(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
>>> parse_music('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 parse_music(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
>>> parse_music('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 task. It creates a dictionary mapping note symbols to their beat counts ('o'→4, 'o|'→2, '.|'→1), splits the input string by spaces, filters out empty strings with `if x`, and returns a list of corresponding beat values. The approach is idiomatic Python (dictionary lookup + list comprehension), handles the provided example correctly, and robustly handles edge cases like multiple consecutive spaces (which would produce empty strings that are filtered out). The code is clear, concise, and efficient.
The solution correctly maps the three note types to their beat values using a dictionary and splits the input string on spaces. It handles the canonical example correctly and filters empty strings (from multiple spaces or leading/trailing spaces) with `if x`. The only minor concern is that it splits only on single spaces (`' '`), so multiple consecutive spaces would produce empty strings that are filtered out — this is handled correctly. However, if the input contains an unrecognized token, it would raise a KeyError rather than gracefully handling it, but the task doesn't specify error handling. The solution is clean, idiomatic, and correct for all reasonable inputs matching the specified format.
The solution correctly parses the input string and returns the expected list of integers for the provided example. However, it does not handle cases where the input string may contain invalid notes or extra spaces effectively, which could lead to a KeyError if an unexpected note is encountered. Additionally, the comment mentions 'quater note' instead of 'quarter note', which is a minor style issue.