calib·HumanEval/18
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 how_many_times to fn_f1820e35c0 so the judges can't pattern-match a memorized HumanEval solution.
def fn_f1820e35c0(string: str, substring: str) -> int:
""" Find how many times a given substring can be found in the original string. Count overlaping cases.
>>> fn_f1820e35c0('', 'a')
0
>>> fn_f1820e35c0('aaa', 'a')
3
>>> fn_f1820e35c0('aaaa', 'aa')
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.
def fn_f1820e35c0(string: str, substring: str) -> int:
""" Find how many times a given substring can be found in the original string. Count overlaping cases.
>>> fn_f1820e35c0('', 'a')
0
>>> fn_f1820e35c0('aaa', 'a')
3
>>> fn_f1820e35c0('aaaa', 'aa')
3
"""
times = 0
for i in range(len(string) - len(substring) + 1):
if string[i:i+len(substring)] == substring:
times += 1
return times
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 overlapping substring counting using a sliding window approach. It passes all three provided doctests: empty string returns 0, 'aaa'/'a' returns 3, 'aaaa'/'aa' returns 3. Edge cases like empty substring could be problematic (infinite loop or ZeroDivisionError? No — `range(len(string) - 0 + 1)` = `range(len(string)+1)` which is valid, and `string[i:i+0] == ''` is always True, so it returns `len(string)+1` which is a reasonable/debatable behavior). The logic is sound for all realistic inputs. Minor style note: no handling of empty substring edge case, but the task doesn't specify it. Overall correct and clean.
The candidate solution correctly implements the function to count overlapping occurrences of a substring within a string. It iterates over the string, checking each possible starting position for the substring and increments the count when a match is found. The solution passes all provided test cases, including edge cases such as an empty string and overlapping substrings. The implementation is clear and idiomatic.