HE/7
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_substring(strings: List[str], substring: str) -> List[str]:
""" Filter an input list of strings only for ones that contain given substring
>>> filter_by_substring([], 'a')
[]
>>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')
['abc', 'bacd', '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_substring(strings: List[str], substring: str) -> List[str]:
""" Filter an input list of strings only for ones that contain given substring
>>> filter_by_substring([], 'a')
[]
>>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')
['abc', 'bacd', 'array']
"""
return [x for x in strings if substring in 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 candidate solution correctly implements the required functionality. It uses a list comprehension to filter strings that contain the given substring, which is both idiomatic Python and efficient. The solution passes both provided doctest examples: (1) empty list returns empty list, and (2) filtering ['abc', 'bacd', 'cde', 'array'] by 'a' correctly returns ['abc', 'bacd', 'array']. The logic is straightforward and handles edge cases well: empty input lists, substrings that don't match any strings, and substrings appearing at different positions within strings. The code is clear, concise, and uses the appropriate Python idiom (the `in` operator for substring checking).
The solution is a clean, idiomatic one-liner using a list comprehension with the `in` operator. It correctly handles the empty list case (returns []), the canonical example, and edge cases like empty substring (returns all strings, which is correct since every string contains ""), case-sensitive matching, and substrings not present in any string. No bugs or style issues.
The solution correctly implements the filtering of strings based on the presence of the specified substring. It passes all provided test cases, including the edge case of an empty input list. The code is clear and idiomatic, using a list comprehension for concise expression.