Problem Analysis #1

22.23s
3,475 toks
Problem Statement

Let CC be the binary linear code

C={(a1,a1,a2,a2,,a8,a8)F216:aiF2}.C=\{(a_1,a_1,a_2,a_2,\dots,a_8,a_8)\in\mathbb{F}_2^{16} : a_i\in\mathbb{F}_2\}.

How many binary linear codes are there which are equivalent (under coordinate permutations) to CC?

Ground Truth
2027025
Execution Trace
Reasoning Process

[THOUGHT] To count codes equivalent to (C) under coordinate permutations, compute the orbit size of (C) under (S_{16}). By orbit–stabilizer, this is (16!/|\mathrm{Aut}(C)|). Here (\mathrm{Aut}(C)) consists of permutations that send each of the 8 coordinate-pairs to a (possibly different) coordinate-pair, with an optional swap inside each pair, so (|\mathrm{Aut}(C)|=2^8\cdot 8!). I will compute (16!/(2^8\cdot 8!)) exactly with Python. [/THOUGHT] [PYTHON] import math from fractions import Fraction

val = math.factorial(16) // (28 * math.factorial(8)) val2 = Fraction(math.factorial(16), 28 * math.factorial(8)) print(val) print(val2) print(val == val2) [/PYTHON][THOUGHT] To count codes equivalent to (C) under coordinate permutations, compute the orbit size of (C) under (S_{16}). By orbit–stabilizer, this is (16!/|\mathrm{Aut}(C)|). Here (\mathrm{Aut}(C)) consists of permutations that send each of the 8 coordinate-pairs to a (possibly different) coordinate-pair, with an optional swap inside each pair, so (|\mathrm{Aut}(C)|=2^8\cdot 8!). I will compute (16!/(2^8\cdot 8!)) exactly with Python. [/THOUGHT] [PYTHON] import math from fractions import Fraction

val = math.factorial(16) // (28 * math.factorial(8)) val2 = Fraction(math.factorial(16), 28 * math.factorial(8)) print(val) print(val2) print(val == val2) [/PYTHON]

Tool Output
[PYTHON OUTPUT] 2027025 2027025 True [/PYTHON OUTPUT] [PYTHON OUTPUT] 2027025 2027025 True [/PYTHON OUTPUT]
Reasoning Process

[THOUGHT] Using orbit–stabilizer, the number of distinct codes equivalent to (C) under coordinate permutations is (16!/(2^8\cdot 8!)), which Python evaluated as 2027025. [ \boxed{2027025} ]

Final Model Output
2027025

Comparative Analysis (P1) (Average across runs)

Avg Time Taken

K-EXAONE-236B-A23B
171.0
Solar-Open-100B
60.6
Gemini-3-Pro-Preview
50.2
Kanana-2-30B-Thinking-2601
39.1
Kanana-2-30B-Thinking
33.8
naver-hyperclovax/HCX-007
24.0
Solar-Pro 2
21.7
GLM-4.5-Air
17.0
GPT-5.2 (high)
15.3
model_d_r1
8.3
axk1
5.1
EXAONE-4.0-32B
4.2

Avg Token Usage

Solar-Open-100B
10335.3
Kanana-2-30B-Thinking-2601
7969.3
Gemini-3-Pro-Preview
7038.0
K-EXAONE-236B-A23B
6654.7
Kanana-2-30B-Thinking
6208.0
Solar-Pro 2
5698.3
EXAONE-4.0-32B
3295.0
GPT-5.2 (high)
2904.0
naver-hyperclovax/HCX-007
2848.3
model_d_r1
2757.7
GLM-4.5-Air
2691.3
axk1
2440.3