HE/29
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 filter_by_prefix(strings: List[str], prefix: str) -> List[str]:
""" Filter an input list of strings only for ones that start with a given prefix.
>>> filter_by_prefix([], 'a')
[]
>>> filter_by_prefix(['abc', 'bcd', 'cde', 'array'], 'a')
['abc', 'array']
"""
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 filter_by_prefix(strings: List[str], prefix: str) -> List[str]:
""" Filter an input list of strings only for ones that start with a given prefix.
>>> filter_by_prefix([], 'a')
[]
>>> filter_by_prefix(['abc', 'bcd', 'cde', 'array'], 'a')
['abc', 'array']
"""
return [x for x in strings if x.startswith(prefix)]
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 filter_by_prefix function using a list comprehension with Python's built-in `str.startswith()` method. It handles the empty list edge case (returns []), the canonical example correctly (['abc', 'array']), and would handle other edge cases like empty prefix (returns all strings), single-character prefix, or strings shorter than the prefix. The code is idiomatic, concise, and clear.
The candidate solution correctly implements the function to filter strings by the given prefix, passing both the provided examples and plausible edge cases, such as an empty input list. The code is idiomatic and clear, using a list comprehension effectively.