calib·HumanEval/3
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 below_zero to fn_a62f14a8cc so the judges can't pattern-match a memorized HumanEval solution.
from typing import List
def fn_a62f14a8cc(operations: List[int]) -> bool:
""" You're given a list of deposit and withdrawal operations on a bank account that starts with
zero balance. Your task is to detect if at any point the balance of account fallls below zero, and
at that point function should return True. Otherwise it should return False.
>>> fn_a62f14a8cc([1, 2, 3])
False
>>> fn_a62f14a8cc([1, 2, -4, 5])
True
"""
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_a62f14a8cc(operations: List[int]) -> bool:
""" You're given a list of deposit and withdrawal operations on a bank account that starts with
zero balance. Your task is to detect if at any point the balance of account fallls below zero, and
at that point function should return True. Otherwise it should return False.
>>> fn_a62f14a8cc([1, 2, 3])
False
>>> fn_a62f14a8cc([1, 2, -4, 5])
True
"""
balance = 0
for op in operations:
balance += op
if balance < 0:
return True
return False
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 initializes balance to 0, iterates through all operations, accumulates the balance, and returns True immediately when balance drops below zero. Both provided examples pass: [1,2,3] → balance stays positive → False; [1,2,-4,5] → balance hits -1 after third op → True. Edge cases like empty list return False (correct), single negative operation returns True (correct), and balance exactly at zero returns False (correct, as the condition is strictly < 0). Logic is clean and idiomatic.
The candidate solution correctly implements the task requirements. It iterates over the list of operations, updating the balance accordingly, and checks if the balance falls below zero at any point. If it does, the function returns True; otherwise, it returns False. The solution handles the provided examples correctly and is robust against edge cases such as an empty list or operations that do not cause the balance to drop below zero. The code is clear and idiomatic.