HE/19
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.
from typing import List
def sort_numbers(numbers: str) -> str:
""" Input is a space-delimited string of numberals from 'zero' to 'nine'.
Valid choices are 'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight' and 'nine'.
Return the string with numbers sorted from smallest to largest
>>> sort_numbers('three one five')
'one three five'
"""
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 sort_numbers(numbers: str) -> str:
""" Input is a space-delimited string of numberals from 'zero' to 'nine'.
Valid choices are 'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight' and 'nine'.
Return the string with numbers sorted from smallest to largest
>>> sort_numbers('three one five')
'one three five'
"""
value_map = {
'zero': 0,
'one': 1,
'two': 2,
'three': 3,
'four': 4,
'five': 5,
'six': 6,
'seven': 7,
'eight': 8,
'nine': 9
}
return ' '.join(sorted([x for x in numbers.split(' ') if x], key=lambda x: value_map[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 creates a mapping from word representations of numbers to their numeric values, splits the input string by spaces, filters out empty strings (handling edge cases like multiple spaces), sorts by the numeric value, and rejoins with spaces. The docstring example 'three one five' → 'one three five' is correctly handled. The approach is idiomatic Python, using list comprehension with filtering and a lambda key function. Edge cases are well-handled: empty input returns empty string, single numbers work correctly, and multiple spaces are gracefully filtered. The code is clear and efficient.
The solution correctly maps word-numbers to their integer values and sorts them using those values. It handles the canonical example ('three one five' → 'one three five') correctly. The filter `if x` handles potential empty strings from splitting (e.g., empty input string or extra spaces). The only minor concern is that `numbers.split(' ')` with a literal space rather than `numbers.split()` could leave empty strings if there are multiple consecutive spaces, but the `if x` filter mitigates this. For an empty string input, `numbers.split(' ')` returns `['']`, and the filter removes it, returning `''` — which is correct. Overall the solution is clean, correct, and handles edge cases well.
The solution correctly sorts the space-delimited string of numerals from 'zero' to 'nine' and handles edge cases such as empty inputs. The implementation is clear and idiomatic, using a dictionary for mapping and a list comprehension for splitting and filtering the input. The provided example in the docstring is also correctly handled.