calib·HumanEval/2
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 truncate_number to fn_3273f6bf8f so the judges can't pattern-match a memorized HumanEval solution.
def fn_3273f6bf8f(number: float) -> float:
""" Given a positive floating point number, it can be decomposed into
and integer part (largest integer smaller than given number) and decimals
(leftover part always smaller than 1).
Return the decimal part of the number.
>>> fn_3273f6bf8f(3.5)
0.5
"""
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_3273f6bf8f(number: float) -> float:
""" Given a positive floating point number, it can be decomposed into
and integer part (largest integer smaller than given number) and decimals
(leftover part always smaller than 1).
Return the decimal part of the number.
>>> fn_3273f6bf8f(3.5)
0.5
"""
return number % 1.0
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.
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 `number % 1.0` correctly returns the decimal (fractional) part of a positive floating point number. For the canonical example, `3.5 % 1.0 = 0.5`. It handles edge cases like whole numbers (e.g., `3.0 % 1.0 = 0.0`) and numbers less than 1 (e.g., `0.7 % 1.0 = 0.7`) correctly. The task specifies positive numbers only, so negative number behavior is not a concern. There may be minor floating-point precision issues (e.g., `1.1 % 1.0` gives `0.10000000000000009`), but this is inherent to floating-point arithmetic and not a logic bug. The solution is idiomatic and concise.
The candidate solution correctly returns the decimal part of a positive floating point number by using the modulus operation with 1.0. This approach effectively isolates the fractional part of the number, as demonstrated in the provided example where fn_3273f6bf8f(3.5) returns 0.5. The solution is idiomatic, clear, and handles plausible edge cases such as whole numbers (e.g., 4.0 % 1.0 would correctly return 0.0) and very small decimal numbers (e.g., 0.0001 % 1.0 would return 0.0001).