Problem Analysis #8

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Problem Statement

Let n=24n=24. We consider the family F\mathcal{F} of all linear subspaces C{0,1}24C \subseteq \{0,1\}^{24} that satisfy the following three conditions:

  • The dimension of CC is 12.

  • The Hamming weight of every vector in CC is a multiple of 4.

  • The vector 1=(1,1,,1)\mathbf{1} = (1, 1, \dots, 1) is contained in CC.

For any CFC \in \mathcal{F}, let Ak(C)A_k(C) denote the number of vectors in CC having Hamming weight kk. It is a known theoretical result that the weight distribution sequence (A0(C),A1(C),,A24(C))(A_0(C), A_1(C), \dots, A_{24}(C)) for any CFC \in \mathcal{F} can be uniquely expressed as a linear combination of the weight distributions of two fixed "basis" codes, C1C_1 and C2C_2, which are also in F\mathcal{F}.

The basis codes are defined as follows:

  • \textbf{Code C1C_1}: The weight enumerator polynomial WC1(x,y)=vC1x24wt(v)ywt(v)W_{C_1}(x, y) = \sum_{v \in C_1} x^{24-\text{wt}(v)} y^{\text{wt}(v)} is given by:

    WC1(x,y)=(x8+14x4y4+y8)3W_{C_1}(x, y) = \left( x^8 + 14x^4y^4 + y^8 \right)^3
  • \textbf{Code C2C_2}: This code has no vectors of weight 4 (i.e., A4(C2)=0A_4(C_2) = 0) and has exactly 759 vectors of weight 8 (i.e., A8(C2)=759A_8(C_2) = 759).

Let CC^* be a specific code in F\mathcal{F} that contains exactly 6 vectors of weight 4.

Calculate the number of vectors in CC^* that have weight 12.

Ground Truth
2612
Execution Trace
Reasoning Process

<|channel|>commentary to=python code<|message|>import sympy as sp x,y = sp.symbols('x y') W_C1 = (x8 + 14*x4*y4 + y8)**3 sp.expand(W_C1)

Tool Output
[PYTHON OUTPUT] x**24 + 42*x**20*y**4 + 591*x**16*y**8 + 2828*x**12*y**12 + 591*x**8*y**16 + 42*x**4*y**20 + y**24 [/PYTHON OUTPUT]
Reasoning Process

<|channel|>commentary to=python code<|message|># Compute A12(C*) as derived A12_C1 = 2828 A12_C2 = 2576 A12_Cstar = (1/7)*A12_C1 + (6/7)*A12_C2 A12_Cstar

Tool Output
[PYTHON OUTPUT] 2612.0 [/PYTHON OUTPUT]
Reasoning Process

[THOUGHT] We have verified that the weight distribution of C2 is indeed (A_0=1,; A_4=0,; A_8=759,; A_{12}=2576,; A_{16}=759,; A_{20}=0,; A_{24}=1). Using the linear combination coefficients (\alpha=1/7,;\beta=6/7) derived from (A_4(C^)=6) and (\alpha+\beta=1), we compute (A_{12}(C^) = \frac{1}{7}A_{12}(C_1)+\frac{6}{7}A_{12}(C_2) = \frac{1}{7}\cdot2828+\frac{6}{7}\cdot2576 = 404+2208 = 2612). Thus the number of weight‑12 vectors in (C^*) is (\boxed{2612}).

Final Model Output
2612

Comparative Analysis (P8) (Average across runs)

Avg Time Taken

EXAONE-4.0.1-32B (high)
1193.7
Gemma-3-27B
684.9
Deepseek-R1-distill-Qwen-32B (high)
466.1
Gemini-3-Pro-Preview
303.8
GPT-oss-20B (high)
176.4
Solar-Pro-2 (31B)(high)
103.4
HCX-007(high)
74.7
Llama-VARCO-8B-Instruct
62.4
Grok-4.1-fast
55.5
Deepseek-V3.2
54.8
Qwen3-30B-A3B-2507
52.1
A.X-4.0 (72B)
45.7
GPT-5.1 (high)
44.6
Claude-Opus-4.5
21.1

Avg Token Usage

GPT-oss-20B (high)
27906.0
Gemini-3-Pro-Preview
24028.3
Solar-Pro-2 (31B)(high)
14501.0
HCX-007(high)
13209.7
Deepseek-V3.2
12658.3
Llama-VARCO-8B-Instruct
11392.3
Deepseek-R1-distill-Qwen-32B (high)
11316.3
Grok-4.1-fast
10882.7
EXAONE-4.0.1-32B (high)
7810.0
Qwen3-30B-A3B-2507
6876.7
GPT-5.1 (high)
6638.7
A.X-4.0 (72B)
6391.0
Claude-Opus-4.5
5126.7
Gemma-3-27B
2709.3