datasource file: /Users/neoneye/git/simon_arc_lab/run_tasks_result/20241222_033847_task_to_prompt_o3_format/task_to_prompt.jsonl
datasource row: 0
groupname: arcagi
dataset_id: ARC-AGI
task_id: 00576224
test_index: 0
max_prompt_length: 2000
max_response_length: 1000

LLM:
{"system_prompt": null, "pydantic_program_mode": "default", "base_url": "http://localhost:11434", "model": "llama3.1:latest", "temperature": 0.0, "context_window": 3900, "request_timeout": 120.0, "prompt_key": "prompt", "json_mode": false, "additional_kwargs": {}, "is_function_calling_model": true, "keep_alive": null, "class_name": "Ollama_llm"}

SYSTEM PROMPT:
You are an expert at solving ARC (Abstraction & reasoning corpus) puzzles

---

PROMPT:
Find the common rule that maps an input grid to an output grid, given the examples below.

Example 1:

Input:
8 6
6 4
Output:
8 6 8 6 8 6
6 4 6 4 6 4
6 8 6 8 6 8
4 6 4 6 4 6
8 6 8 6 8 6
6 4 6 4 6 4

Example 2:

Input:
7 9
4 3
Output:
7 9 7 9 7 9
4 3 4 3 4 3
9 7 9 7 9 7
3 4 3 4 3 4
7 9 7 9 7 9
4 3 4 3 4 3

Below is a test input grid. Predict the corresponding output grid by applying the rule you found. Your final answer should just be the text output grid itself.

Input:
3 2
7 8


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RESPONSE:
After analyzing the examples, I've identified the common rule that maps an input grid to an output grid:

The rule is: Mirror the input grid horizontally and vertically, then repeat it in a square shape.

Applying this rule to the test input grid:

Input:
3 2
7 8

Output:
3 2 3 2 3 2
7 8 7 8 7 8
2 3 2 3 2 3
8 7 8 7 8 7
3 2 3 2 3 2
7 8 7 8 7 8

So, the predicted output grid is:

3 2 3 2 3 2
7 8 7 8 7 8
2 3 2 3 2 3
8 7 8 7 8 7
3 2 3 2 3 2
7 8 7 8 7 8

---


response byte count: 452
response item count: 215
elapsed: 7.39 seconds

expected output:
[[3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8], [2, 3, 2, 3, 2, 3], [8, 7, 8, 7, 8, 7], [3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8]]

predicted output:
[[3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8], [2, 3, 2, 3, 2, 3], [8, 7, 8, 7, 8, 7], [3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8]]

status: correct
