calib·HumanEval/5
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 intersperse to fn_232f52cff5 so the judges can't pattern-match a memorized HumanEval solution.
from typing import List
def fn_232f52cff5(numbers: List[int], delimeter: int) -> List[int]:
""" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'
>>> fn_232f52cff5([], 4)
[]
>>> fn_232f52cff5([1, 2, 3], 4)
[1, 4, 2, 4, 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
def fn_232f52cff5(numbers: List[int], delimeter: int) -> List[int]:
""" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'
>>> fn_232f52cff5([], 4)
[]
>>> fn_232f52cff5([1, 2, 3], 4)
[1, 4, 2, 4, 3]
"""
if not numbers:
return []
result = []
for n in numbers[:-1]:
result.append(n)
result.append(delimeter)
result.append(numbers[-1])
return result
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 handles all cases: empty list returns [], single-element list returns that element without any delimiter, and multi-element lists get the delimiter inserted between every consecutive pair. The logic iterates through all elements except the last, appending each followed by the delimiter, then appends the final element. This matches both provided doctests and handles edge cases (empty list, single element) correctly. Code is clean and idiomatic.
The candidate solution correctly implements the function to insert a delimiter between every two consecutive elements of the input list. It handles the edge case of an empty list by returning an empty list, as expected. The solution is clear, idiomatic, and passes the provided examples. It also correctly handles edge cases such as a single-element list, where no delimiter should be added. The logic is sound and efficient for the task.