datasource file: /Users/neoneye/git/simon_arc_lab/run_tasks_result/20241222_033847_task_to_prompt_o3_format/task_to_prompt.jsonl
datasource row: 173
groupname: arcagi
dataset_id: ARC-AGI
task_id: 66e6c45b
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:
0 0 0 0
0 3 4 0
0 7 6 0
0 0 0 0
Output:
3 0 0 4
0 0 0 0
0 0 0 0
7 0 0 6

Example 2:

Input:
0 0 0 0
0 5 6 0
0 8 3 0
0 0 0 0
Output:
5 0 0 6
0 0 0 0
0 0 0 0
8 0 0 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:
0 0 0 0
0 2 3 0
0 4 9 0
0 0 0 0


---

RESPONSE:
Based on the examples provided, I notice that the rule involves rearranging the non-zero values in a specific order. In both Example 1 and Example 2, the output grid has the second column value from the input grid as the first element of the output grid's first row, followed by zero values.

Applying this observation to the test input grid:

Input:
0 0 0 0
0 2 3 0
0 4 9 0
0 0 0 0

The corresponding output grid would be:

Output:
2 0 0 3
0 0 0 0
0 0 0 0
4 0 0 9

---


response byte count: 464
response item count: 147
elapsed: 4.15 seconds

expected output:
[[2, 0, 0, 3], [0, 0, 0, 0], [0, 0, 0, 0], [4, 0, 0, 9]]

predicted output:
[[2, 0, 0, 3], [0, 0, 0, 0], [0, 0, 0, 0], [4, 0, 0, 9]]

status: correct
