calib·HumanEval/1
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 separate_paren_groups to fn_2b625257b3 so the judges can't pattern-match a memorized HumanEval solution.
from typing import List
def fn_2b625257b3(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> fn_2b625257b3('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
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_2b625257b3(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> fn_2b625257b3('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
result = []
current_string = []
current_depth = 0
for c in paren_string:
if c == '(':
current_depth += 1
current_string.append(c)
elif c == ')':
current_depth -= 1
current_string.append(c)
if current_depth == 0:
result.append(''.join(current_string))
current_string.clear()
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 tracks depth and accumulates characters, appending a completed group to results when depth returns to 0. Spaces are implicitly ignored (neither '(' nor ')' branch handles spaces, so they're skipped). The canonical example produces ['()', '(())', '(()())'] as expected. Edge cases like empty string return [], single group works, and nested groups are handled correctly. The logic is clean and idiomatic.
The candidate solution correctly separates groups of balanced parentheses into separate strings, as demonstrated by the example provided in the docstring. It handles spaces by ignoring them, which is consistent with the task requirements. However, the solution does not explicitly handle edge cases such as empty input strings or strings without any parentheses, which could lead to unexpected behavior. Additionally, the solution could be more idiomatic by using a list comprehension or generator to build the result list. Despite these minor issues, the core logic is sound and works for the given example.