Problem Analysis #1

2.84s
2,810 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] The code (C) has a specific structure where each (a_i) appears twice consecutively. To find the number of binary linear codes equivalent to (C) under coordinate permutations, we need to consider all possible distinct arrangements of the coordinates that preserve the pairing structure of (C).

The key observation is that the code (C) is defined by pairs of identical coordinates. The equivalence under coordinate permutations means we can permute the 16 coordinates, but the resulting code must still have the same structure: pairs of identical coordinates.

The number of distinct codes equivalent to (C) is equal to the number of ways to partition the 16 coordinates into 8 ordered pairs, where the order within each pair does not matter (since swapping the two identical elements in a pair does not change the code).

This is equivalent to counting the number of ways to choose 8 pairs from 16 coordinates, which is given by the multinomial coefficient (\frac{16!}{(2!)^8 \cdot 8!}), since we divide by (2!) for each pair (order within the pair does not matter) and (8!) (order of the pairs does not matter).

[/THOUGHT] [PYTHON] from math import factorial

numerator = factorial(16) denominator = (factorial(2) ** 8) * factorial(8) result = numerator // denominator print(result) [/PYTHON]

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

[THOUGHT] The Python output confirms the calculation: there are 2,027,025 distinct binary linear codes equivalent to (C) under coordinate permutations. [/THOUGHT]

\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