HE/22
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, Any
def filter_integers(values: List[Any]) -> List[int]:
""" Filter given list of any python values only for integers
>>> filter_integers(['a', 3.14, 5])
[5]
>>> filter_integers([1, 2, 3, 'abc', {}, []])
[1, 2, 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.
from typing import List, Any
def filter_integers(values: List[Any]) -> List[int]:
""" Filter given list of any python values only for integers
>>> filter_integers(['a', 3.14, 5])
[5]
>>> filter_integers([1, 2, 3, 'abc', {}, []])
[1, 2, 3]
"""
return [x for x in values if isinstance(x, int)]
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 core task: filtering a list to keep only integers using isinstance(x, int). It passes both provided doctest examples. The code is idiomatic, clear, and efficient (O(n) time complexity). However, there is a subtle but important edge case: in Python, bool is a subclass of int, so isinstance(True, int) and isinstance(False, int) both return True. This means the function will incorrectly include boolean values in the output. For example, filter_integers([True, False, 1]) would return [True, False, 1] rather than [1]. While the provided examples don't test this edge case, it represents a realistic scenario where the solution's behavior may not match the intended specification of filtering "only for integers" (which typically excludes booleans). This is a minor but real correctness issue that prevents a perfect score.
The solution correctly filters the list to include only integers, passing the provided examples and plausible edge cases. The use of list comprehension is idiomatic and clear, making the code both efficient and readable.