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:
None

---

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


---

RESPONSE:
After analyzing the examples, I noticed that the output grid is created by repeating the rows of the input grid in a specific pattern. Here's the common rule:

1. The first row of the output grid is the same as the first row of the input grid.
2. The second row of the output grid is the same as the second row of the input grid.
3. The third row of the output grid is the reverse of the first row of the input grid (i.e., the elements are in the opposite order).
4. The fourth row of the output grid is the reverse of the second row of the input grid.
5. The fifth and sixth rows of the output grid repeat the first two rows of the input grid, respectively.

Applying this rule to the test input grid:

Input:
3 2
7 8

The predicted output grid would be:

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

---


response byte count: 836
response item count: 251
elapsed: 7.64 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
