calib·HumanEval/6
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 parse_nested_parens to fn_be3eebd770 so the judges can't pattern-match a memorized HumanEval solution.
from typing import List
def fn_be3eebd770(paren_string: str) -> List[int]:
""" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.
For each of the group, output the deepest level of nesting of parentheses.
E.g. (()()) has maximum two levels of nesting while ((())) has three.
>>> fn_be3eebd770('(()()) ((())) () ((())()())')
[2, 3, 1, 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_be3eebd770(paren_string: str) -> List[int]:
""" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.
For each of the group, output the deepest level of nesting of parentheses.
E.g. (()()) has maximum two levels of nesting while ((())) has three.
>>> fn_be3eebd770('(()()) ((())) () ((())()())')
[2, 3, 1, 3]
"""
def parse_paren_group(s):
depth = 0
max_depth = 0
for c in s:
if c == '(':
depth += 1
max_depth = max(depth, max_depth)
else:
depth -= 1
return max_depth
return [parse_paren_group(x) for x in paren_string.split(' ') if 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 solution correctly implements the required functionality. It splits the input string by spaces, processes each group to find the maximum nesting depth by tracking current depth with a counter, and returns a list of max depths. The canonical example `fn_be3eebd770('(()()) ((())) () ((())()())')` returns `[2, 3, 1, 3]` as expected. Edge cases are handled: empty strings between spaces are filtered with `if x`, and the logic correctly tracks depth increments/decrements. The code is clean and idiomatic.
The candidate solution correctly implements the function to determine the deepest level of nesting for each group of parentheses in the input string. The solution splits the input string by spaces to handle each group separately, and uses a helper function `parse_paren_group` to calculate the maximum depth of nesting for each group. The logic for tracking the depth of parentheses is correct, incrementing for '(' and decrementing for ')', and it correctly updates the maximum depth encountered. The solution passes the provided example and handles edge cases such as empty groups or balanced parentheses correctly.