calib·HumanEval/42
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 incr_list to fn_971332783c so the judges can't pattern-match a memorized HumanEval solution.
def fn_971332783c(l: list):
"""Return list with elements incremented by 1.
>>> fn_971332783c([1, 2, 3])
[2, 3, 4]
>>> fn_971332783c([5, 3, 5, 2, 3, 3, 9, 0, 123])
[6, 4, 6, 3, 4, 4, 10, 1, 124]
"""
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_971332783c(l: list):
"""Return list with elements incremented by 1.
>>> fn_971332783c([1, 2, 3])
[2, 3, 4]
>>> fn_971332783c([5, 3, 5, 2, 3, 3, 9, 0, 123])
[6, 4, 6, 3, 4, 4, 10, 1, 124]
"""
return [(e + 1) for e in l]
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.
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 task using a list comprehension that increments each element by 1. It matches both provided examples, handles edge cases like empty lists (returns []), and works correctly for any numeric elements. The implementation is idiomatic and clear.
The candidate solution correctly implements the function to increment each element of the input list by 1. It matches the provided examples in the docstring and handles edge cases such as an empty list, which would return an empty list. The implementation is idiomatic and clear, using a list comprehension to achieve the desired result.