datasource file: /Users/neoneye/git/simon_arc_lab/run_tasks_result/20241222_033847_task_to_prompt_o3_format/task_to_prompt.jsonl
datasource row: 451
groupname: arcagi
dataset_id: ARC-AGI
task_id: 08ed6ac7
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:
Be brief and clear in your responses

---

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 5 0 0 0
0 5 0 0 0 5 0 0 0
0 5 0 0 0 5 0 0 0
0 5 0 5 0 5 0 0 0
0 5 0 5 0 5 0 0 0
0 5 0 5 0 5 0 0 0
0 5 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0
Output:
0 0 0 0 0 1 0 0 0
0 2 0 0 0 1 0 0 0
0 2 0 0 0 1 0 0 0
0 2 0 3 0 1 0 0 0
0 2 0 3 0 1 0 0 0
0 2 0 3 0 1 0 0 0
0 2 0 3 0 1 0 4 0
0 2 0 3 0 1 0 4 0
0 2 0 3 0 1 0 4 0

Example 2:

Input:
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 5 0
0 0 0 0 0 0 0 5 0
0 0 0 0 0 0 0 5 0
0 0 0 5 0 0 0 5 0
0 0 0 5 0 5 0 5 0
0 0 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0
Output:
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 1 0
0 0 0 2 0 0 0 1 0
0 0 0 2 0 3 0 1 0
0 0 0 2 0 3 0 1 0
0 4 0 2 0 3 0 1 0
0 4 0 2 0 3 0 1 0

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 0 0 0 0
0 5 0 0 0 0 0 0 0
0 5 0 0 0 5 0 0 0
0 5 0 0 0 5 0 0 0
0 5 0 0 0 5 0 5 0
0 5 0 0 0 5 0 5 0
0 5 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0
0 5 0 5 0 5 0 5 0


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RESPONSE:
Based on the examples provided, I notice that the output grid is similar to the input grid, but with some values replaced by smaller numbers. Specifically:

* The value 5 in the input grid is replaced by a smaller number (1, 2, or 4) in the output grid.
* The replacement pattern seems to be based on the distance of the cell from the top-left corner of the grid.

Using this observation, I predict that the corresponding output grid for the test input grid will be:

0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0
0 1 0 0 0 2 0 0 0
0 1 0 0 0 2 0 0 0
0 1 0 0 0 2 0 3 0
0 1 0 0 0 2 0 3 0
0 1 0 4 0 2 0 3 0
0 1 0 4 0 2 0 3 0
0 1 0 4 0 2 0 3 0

---


response byte count: 629
response item count: 264
elapsed: 7.69 seconds

expected output:
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0]]

predicted output:
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0]]

status: correct
