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patr1ckzhu/evaluation_results_parallel.json

Created October 23, 2025 10:33
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evaluation_results_parallel.json
{
"summary": {
"total": 20,
"correct": 15,
"accuracy": 0.75,
"model": "deepseek-chat",
"temperature": 1.0,
"max_workers": 4,
"total_time": 430.5924618244171,
"avg_time_per_task": 21.529623091220856
},
"results": [
{
"task_id": 1,
"correct": true,
"predicted": [
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
2,
2,
2,
4,
2,
2
],
[
0,
0,
0,
8,
0,
0
]
],
"ground_truth": [
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
0,
0,
0,
8,
0,
0
],
[
2,
2,
2,
4,
2,
2
],
[
0,
0,
0,
8,
0,
0
]
],
"model_output": "Let’s break down the examples.\n\n---\n\n**Example 1** \nInput: \nRow 0: 0, 8, 0, 0, 0, 0 \nRow 1: 0, 8, 0, 0, 0, 0 \nRow 2: 0, 0, 0, 0, 0, 0 \nRow 3: 0, 0, 0, 0, 2, 2 \nRow 4: 0, 0, 0, 0, 0, 0 \nRow 5: 0",
"api_time": 52.445820808410645
},
{
"task_id": 2,
"correct": true,
"predicted": [
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
],
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
],
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
]
],
"ground_truth": [
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
],
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
],
[
2,
2,
2
],
[
0,
2,
0
],
[
0,
2,
0
]
],
"model_output": "Let’s analyze the training examples step by step.\n\n---\n\n**Step 1 – Compare input and output dimensions** \nInput: 6×3 \nOutput: 9×3 \nSo the number of rows increases by 3, but columns remain the same.",
"api_time": 53.029547691345215
},
{
"task_id": 3,
"correct": false,
"predicted": [
[
0,
0,
3,
0,
0,
0
],
[
0,
0,
0,
3,
0,
0
],
[
0,
0,
0,
0,
3,
0
],
[
0,
0,
0,
0,
0,
3
],
[
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0
]
],
"ground_truth": [
[
0,
0,
3,
0,
0,
0
],
[
0,
0,
0,
3,
0,
0
],
[
0,
4,
9,
0,
3,
0
],
[
0,
0,
4,
9,
0,
3
],
[
0,
0,
0,
4,
9,
0
],
[
0,
0,
0,
0,
4,
9
]
],
"model_output": "Let’s analyze the pattern from the training examples.\n\n**Step 1 – Observations about input → output size** \nAll examples: 3×3 input → 6×6 output. \nSo output size = double the input size in each dime",
"api_time": 95.11387801170349
},
{
"task_id": 4,
"correct": true,
"predicted": [
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
5,
0,
0,
0,
0
],
[
5,
2,
0,
4,
0
],
[
5,
2,
6,
4,
3
]
],
"ground_truth": [
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
5,
0,
0,
0,
0
],
[
5,
2,
0,
4,
0
],
[
5,
2,
6,
4,
3
]
],
"model_output": "Let's break down the training examples to find the pattern.\n\n---\n\n**Example 1** \nInput: \n```\n[0, 0, 0, 0, 0, 9]\n[0, 0, 0, 8, 0, 0]\n[0, 0, 0, 0, 0, 0]\n[4, 0, 0, 0, 0, 0]\n[4, 0, 7, 8, 0, 0]\n[4, 0, 7, ",
"api_time": 65.63393521308899
},
{
"task_id": 5,
"correct": true,
"predicted": [
[
1,
1,
0,
0,
1,
1
],
[
0,
3,
2,
2,
3,
0
],
[
3,
3,
0,
0,
3,
3
],
[
3,
3,
0,
0,
3,
3
],
[
0,
3,
2,
2,
3,
0
],
[
1,
1,
0,
0,
1,
1
]
],
"ground_truth": [
[
1,
1,
0,
0,
1,
1
],
[
0,
3,
2,
2,
3,
0
],
[
3,
3,
0,
0,
3,
3
],
[
3,
3,
0,
0,
3,
3
],
[
0,
3,
2,
2,
3,
0
],
[
1,
1,
0,
0,
1,
1
]
],
"model_output": "Let’s break down the pattern from the training examples.\n\n**Step 1 – Observe size change** \nInput: 3×3 → Output: 6×6. \nSo the output is double the size in both dimensions.\n\n**Step 2 – Identify symme",
"api_time": 65.76794385910034
},
{
"task_id": 6,
"correct": true,
"predicted": [
[
5,
4,
4
],
[
4,
5,
4
],
[
4,
5,
4
]
],
"ground_truth": [
[
5,
4,
4
],
[
4,
5,
4
],
[
4,
5,
4
]
],
"model_output": "Let’s analyze the training examples.\n\n---\n\n**Step 1 – Understanding the input structure** \nAll inputs are \\( 9 \\times 3 \\) grids, outputs are \\( 3 \\times 3 \\) grids. \nThe \\( 9 \\times 3 \\) grid can b",
"api_time": 74.32951021194458
},
{
"task_id": 7,
"correct": true,
"predicted": [
[
0,
0,
0
],
[
2,
0,
0
],
[
0,
2,
0
]
],
"ground_truth": [
[
0,
0,
0
],
[
2,
0,
0
],
[
0,
2,
0
]
],
"model_output": "Let's break this down step by step.\n\n---\n\n## 1. Observations\n\nWe have **6×3 input → 3×3 output** in all examples.\n\nInputs have **two colors**: `0` and `9` in the first 3 rows, and `0` and `1` in the l",
"api_time": 110.049152135849
},
{
"task_id": 8,
"correct": true,
"predicted": [
[
0,
2,
2
],
[
2,
2,
2
],
[
2,
0,
2
]
],
"ground_truth": [
[
0,
2,
2
],
[
2,
2,
2
],
[
2,
0,
2
]
],
"model_output": "Let’s break this down step by step.\n\n**1. Input grid structure** \nAll inputs are 3×7 grids. \nThey seem to be split into two parts: \n- Columns 1–3 (index 0–2) \n- Column 4 (index 3) always has value",
"api_time": 60.2498939037323
},
{
"task_id": 9,
"correct": true,
"predicted": [
[
4,
4,
9,
9
],
[
4,
4,
4,
4
],
[
4,
4,
9,
9
],
[
4,
4,
9,
9
],
[
4,
4,
4,
4
],
[
4,
4,
9,
9
]
],
"ground_truth": [
[
4,
4,
9,
9
],
[
4,
4,
4,
4
],
[
4,
4,
9,
9
],
[
4,
4,
9,
9
],
[
4,
4,
4,
4
],
[
4,
4,
9,
9
]
],
"model_output": "Let’s analyze the examples step by step.\n\n---\n\n**Step 1: Observe input/output structure**\n\nAll examples: \n- Input: 3×4 grid \n- Output: 6×4 grid \n\nSo the output is **double the height** of the input",
"api_time": 28.590656042099
},
{
"task_id": 10,
"correct": false,
"predicted": [
[
8,
7,
7,
7,
8
],
[
2,
4,
7,
4,
9
],
[
7,
7,
7,
7,
7
],
[
9,
8,
7,
8,
9
],
[
4,
2,
7,
2,
8
]
],
"ground_truth": [
[
8,
9,
7,
2,
8
],
[
2,
4,
7,
4,
9
],
[
7,
7,
7,
7,
7
],
[
9,
8,
7,
4,
9
],
[
4,
2,
7,
2,
8
]
],
"model_output": "Let’s analyze the examples step by step.\n\n---\n\n**Step 1 – Identify the pattern in the training examples**\n\nAll inputs and outputs are 5×5 grids.\n\nLooking at **Example 1**:\n\nInput: \n[7, 1, 7, 8, 0] \n",
"api_time": 123.91388487815857
},
{
"task_id": 11,
"correct": false,
"predicted": [
[
2,
5,
0,
0,
1,
1
],
[
2,
5,
1,
5,
5,
1
],
[
3,
1,
1,
1,
1,
0
],
[
3,
2,
2,
1,
1,
3
],
[
1,
5,
5,
1,
5,
2
],
[
1,
1,
0,
0,
5,
2
]
],
"ground_truth": [
[
2,
5,
0,
3,
2,
2
],
[
2,
5,
1,
1,
5,
5
],
[
3,
1,
1,
1,
1,
0
],
[
0,
1,
1,
1,
1,
3
],
[
5,
5,
1,
1,
5,
2
],
[
2,
2,
3,
0,
5,
2
]
],
"model_output": "Let’s break this down step by step.\n\n---\n\n## **Step 1: Observing the transformation pattern**\nAll examples are 3×3 → 6×6.\n\nLet’s label the 3×3 input as:\n\n\\[\nA = \\begin{bmatrix}\na & b & c \\\\\nd & e & f ",
"api_time": 165.5873248577118
},
{
"task_id": 12,
"correct": true,
"predicted": [
[
0,
2
],
[
2,
3
]
],
"ground_truth": [
[
0,
2
],
[
2,
3
]
],
"model_output": "Let’s break down the examples to find the pattern.\n\n---\n\n**Example 1** \nInput (7×7): \nRow 2: `[1, 1, 3, 1, 2, 1, 1]` \nRow 3: `[1, 1, 3, 1, 2, 1, 1]` \nOutput (2×3): `[[3, 0, 2], [3, 0, 2]]` \n\nObse",
"api_time": 48.22935700416565
},
{
"task_id": 13,
"correct": true,
"predicted": [
[
4,
0,
4
],
[
0,
0,
4
],
[
4,
4,
0
]
],
"ground_truth": [
[
4,
0,
4
],
[
0,
0,
4
],
[
4,
4,
0
]
],
"model_output": "Let’s break this down step by step.\n\n---\n\n## **Step 1: Identify the pattern from examples**\n\nAll examples are 3×3 input → 3×3 output. \nLet’s compare input and output values.\n\n**Example 1:**\nInput: \n",
"api_time": 47.205260038375854
},
{
"task_id": 14,
"correct": true,
"predicted": [
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
0,
0,
5,
0,
5
],
[
5,
5,
1,
5,
1
]
],
"ground_truth": [
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0
],
[
0,
0,
5,
0,
5
],
[
5,
5,
1,
5,
1
]
],
"model_output": "Let’s analyze the examples step by step.\n\n---\n\n**Observation 1: Grid structure** \nAll inputs are 5×5 grids with:\n- Row 1–2: all 0 \n- Row 3: has some 1’s and 0’s \n- Row 4: has some 5’s and 0’s \n- R",
"api_time": 35.08729910850525
},
{
"task_id": 15,
"correct": true,
"predicted": [
[
3,
3,
4
],
[
7,
0,
1
],
[
7,
0,
1
]
],
"ground_truth": [
[
3,
3,
4
],
[
7,
0,
1
],
[
7,
0,
1
]
],
"model_output": "Let’s break this down step by step.\n\n**Step 1 — Observe the input–output mapping in training examples**\n\nAll inputs are 6×6 grids, outputs are 3×3 grids. \nThe 6×6 grid has many zeros and a few nonzer",
"api_time": 32.07454299926758
},
{
"task_id": 16,
"correct": true,
"predicted": [
[
5,
0,
3,
0,
2,
2
],
[
0,
5,
3,
0,
0,
0
],
[
0,
0,
5,
0,
8,
6
],
[
0,
0,
0,
5,
8,
0
],
[
0,
0,
0,
0,
5,
0
],
[
0,
0,
0,
0,
0,
5
]
],
"ground_truth": [
[
5,
0,
3,
0,
2,
2
],
[
0,
5,
3,
0,
0,
0
],
[
0,
0,
5,
0,
8,
6
],
[
0,
0,
0,
5,
8,
0
],
[
0,
0,
0,
0,
5,
0
],
[
0,
0,
0,
0,
0,
5
]
],
"model_output": "Let’s analyze the examples step-by-step.\n\n---\n\n**Observation 1 – Diagonal preservation** \nIn all examples, the main diagonal (top-left to bottom-right) is unchanged: \n- Example 1: `5, 5, 5, 5` stays",
"api_time": 59.83599090576172
},
{
"task_id": 17,
"correct": false,
"predicted": [
[
1,
7,
6,
7,
7,
7,
5
],
[
6,
6,
6,
7,
7,
7,
7
],
[
7,
7,
6,
7,
7,
7,
7
],
[
7,
7,
7,
7,
6,
6,
7
],
[
7,
7,
7,
7,
6,
6,
7
],
[
6,
6,
8,
7,
7,
7,
7
],
[
7,
6,
7,
7,
7,
7,
7
]
],
"ground_truth": [
[
7,
7,
4,
7,
7,
7,
7
],
[
4,
4,
4,
7,
7,
7,
7
],
[
7,
7,
4,
7,
7,
7,
7
],
[
7,
7,
7,
7,
1,
1,
7
],
[
7,
7,
7,
7,
1,
1,
7
],
[
8,
8,
7,
7,
7,
7,
7
],
[
7,
8,
7,
7,
7,
7,
7
]
],
"model_output": "Let’s analyze the training examples carefully.\n\n---\n\n**Example 1** \nInput 5×5: \n```\n7 6 6 3 7\n7 7 7 4 7\n7 7 7 6 7\n7 7 6 6 6\n7 7 7 6 7\n```\nOutput 5×5: \n```\n7 3 3 7 7\n7 7 7 7 7\n7 7 7 4 7\n7 7 4 4 4\n7 ",
"api_time": 72.28674626350403
},
{
"task_id": 18,
"correct": true,
"predicted": [
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
],
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
],
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
]
],
"ground_truth": [
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
],
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
],
[
3,
3,
3,
3
],
[
0,
3,
3,
0
],
[
3,
3,
3,
3
]
],
"model_output": "Let’s break down the examples.\n\n---\n\n**Step 1: Observe dimensions** \nInput: 3×2 → Output: 9×4 \nThat means: \n- Height: 3 → 9 = 3×3 \n- Width: 2 → 4 = 2×2 \n\nSo each input cell becomes a 3×2 block in",
"api_time": 162.41480207443237
},
{
"task_id": 19,
"correct": true,
"predicted": [
[
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0
],
[
0,
0,
1,
2,
0,
0
],
[
0,
0,
2,
1,
0,
0
],
[
1,
2,
1,
2,
1,
2
],
[
2,
1,
2,
1,
2,
1
]
],
"ground_truth": [
[
0,
0,
0,
0,
0,
0
],
[
0,
0,
0,
0,
0,
0
],
[
0,
0,
1,
2,
0,
0
],
[
0,
0,
2,
1,
0,
0
],
[
1,
2,
1,
2,
1,
2
],
[
2,
1,
2,
1,
2,
1
]
],
"model_output": "Let's break this down step by step.\n\n---\n\n**Step 1: Identify the transformation rule from examples**\n\nAll examples:\n- Input: 3×3 grid containing `0` or `5`.\n- Output: 6×6 grid containing `0`, `1`, or ",
"api_time": 46.436574935913086
},
{
"task_id": 20,
"correct": false,
"predicted": [
[
1,
8,
1,
3
],
[
1,
8,
1,
0
],
[
1,
8,
0,
0
],
[
1,
0,
0,
0
],
[
1,
0,
0,
0
]
],
"ground_truth": [
[
1,
8,
2,
3,
4
],
[
1,
8,
2,
3,
0
],
[
1,
8,
2,
0,
0
],
[
1,
8,
0,
0,
0
],
[
1,
0,
0,
0,
0
],
[
1,
0,
0,
0,
0
]
],
"model_output": "Let’s break this down step by step.\n\n---\n\n## **Step 1: Understanding the transformation**\n\nFrom training examples, the output grid is **taller** than the input grid, and **sometimes wider** (but not a",
"api_time": 118.19801330566406
}
]
}
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