PANOPTES
panoptes-44c4e9b3

calib·HumanEval/37

humaneval-calibprobe · code

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.

mean score
0.900
inter-judge spread
0.200
judges polled
2
sampling draws
0
1. the task

The function signature + docstring presented to both the model under test and to every judge. The entry-point name was obfuscated from sort_even to fn_4d58b82570 so the judges can't pattern-match a memorized HumanEval solution.



def fn_4d58b82570(l: list):
    """This function takes a list l and returns a list l' such that
    l' is identical to l in the odd indicies, while its values at the even indicies are equal
    to the values of the even indicies of l, but sorted.
    >>> fn_4d58b82570([1, 2, 3])
    [1, 2, 3]
    >>> fn_4d58b82570([5, 6, 3, 4])
    [3, 6, 5, 4]
    """
2. the candidate response judged

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_4d58b82570(l: list):
    """This function takes a list l and returns a list l' such that
    l' is identical to l in the odd indicies, while its values at the even indicies are equal
    to the values of the even indicies of l, but sorted.
    >>> fn_4d58b82570([1, 2, 3])
    [1, 2, 3]
    >>> fn_4d58b82570([5, 6, 3, 4])
    [3, 6, 5, 4]
    """
    evens = l[::2]
    odds = l[1::2]
    evens.sort()
    ans = []
    for e, o in zip(evens, odds):
        ans.extend([e, o])
    if len(evens) > len(odds):
        ans.append(evens[-1])
    return ans
3a. point-pass scores (temperature 0)

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.

claude-sonnet
1.000
$0.0079
gpt-4o
0.800
$0.0036
3b. sampling-pass dispersion (temperature 1)

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.

no sampling pass for this run
5. judge rationales

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.

claude-sonnet
score 1.000 · 1951 tokens · 4335ms

The solution correctly extracts even-indexed elements, sorts them, keeps odd-indexed elements unchanged, and interleaves them back. It handles the edge case where the list has an odd length (more even-indexed elements than odd-indexed). Both provided doctests pass: [1,2,3]→[1,2,3] and [5,6,3,4]→[3,6,5,4]. Edge cases like empty list and single-element list also work correctly.

gpt-4o
score 0.800 · 1106 tokens · 2158ms

The candidate solution correctly implements the task by sorting the even-indexed elements while keeping the odd-indexed elements unchanged. It handles the example cases correctly and accounts for lists with an odd number of elements by appending the last even-indexed element if necessary. However, the solution could be slightly improved in terms of efficiency by using a more idiomatic approach to combine the sorted even and unchanged odd elements, such as using list comprehension or itertools. Overall, the solution is correct but has minor style issues.