datasource file: /Users/neoneye/git/simon_arc_lab/run_tasks_result/20241223_162108_task_to_prompt_o3_format_with_bad_predictions/task_to_prompt.jsonl
datasource row: 172
groupname: arcagi
dataset_id: ARC-AGI
task_id: 27a77e38
test_index: 0
max_prompt_length: 2000
max_response_length: 2000

LLM:
{"system_prompt": null, "pydantic_program_mode": "default", "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo", "temperature": 0.5, "max_tokens": 1024, "logprobs": null, "top_logprobs": 0, "additional_kwargs": {}, "max_retries": 3, "timeout": 60.0, "default_headers": null, "reuse_client": true, "api_key": "SECRET", "api_base": "https://api.together.xyz/v1", "api_version": "", "strict": false, "context_window": 3900, "is_chat_model": true, "is_function_calling_model": false, "tokenizer": null, "class_name": "TogetherLLM"}

SYSTEM PROMPT:
You are an ARC solver. Figure out whats wrong with the 'maybe output'. Then explain your reasoning and provide your own final answer. The answer must be different than the 'maybe output'.

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

Example 1:

Input:
2 2 3
5 5 5
0 0 0
Output:
2 2 3
5 5 5
0 2 0

Example 2:

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

Example 3:

Input:
1 9 9 6 1 8 4
4 6 7 8 9 7 1
9 3 1 4 1 3 6
5 5 5 5 5 5 5
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
Output:
1 9 9 6 1 8 4
4 6 7 8 9 7 1
9 3 1 4 1 3 6
5 5 5 5 5 5 5
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 1 0 0 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:
9 1 2 8 4 9 8 2 1
4 4 3 1 2 7 6 7 9
2 1 6 9 7 8 4 3 6
9 8 6 3 4 2 9 1 7
5 5 5 5 5 5 5 5 5
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
Maybe output:
2 2 2 2 2 2 1 2 1
2 2 2 2 2 2 1 2 9
2 1 2 2 2 2 1 2 6
2 2 2 2 2 2 1 2 7
5 5 5 5 5 5 5 5 5
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 1 0 0


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RESPONSE:
The rule that maps the input grid to the output grid is to copy the input grid to the output grid, except for the last row of zeros. In the last row of zeros, the rule is to place a non-zero value (which seems to be the value from the row above the row of zeros, in the middle column) in the middle column.

However, upon closer inspection of the examples, we can see that the value placed in the last row of zeros is actually the value from the first row of the input grid, in the middle column, but only if the middle column exists (i.e., if the number of columns is odd). If the number of columns is even, the value is placed in the second-to-last column of the last row of zeros, but this is not the case here.

Given the input grid:
9 1 2 8 4 9 8 2 1
4 4 3 1 2 7 6 7 9
2 1 6 9 7 8 4 3 6
9 8 6 3 4 2 9 1 7
5 5 5 5 5 5 5 5 5
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0

The middle column is the 5th column. The value in the first row of the input grid in the 5th column is 4.

The output grid should be:
9 1 2 8 4 9 8 2 1
4 4 3 1 2 7 6 7 9
2 1 6 9 7 8 4 3 6
9 8 6 3 4 2 9 1 7
5 5 5 5 5 5 5 5 5
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 4 0 0 0 0

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response byte count: 1196
response item count: 529
elapsed: 6.64 seconds

expected output:
[[9, 1, 2, 8, 4, 9, 8, 2, 1], [4, 4, 3, 1, 2, 7, 6, 7, 9], [2, 1, 6, 9, 7, 8, 4, 3, 6], [9, 8, 6, 3, 4, 2, 9, 1, 7], [5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0]]

predicted output:
[[9, 1, 2, 8, 4, 9, 8, 2, 1], [4, 4, 3, 1, 2, 7, 6, 7, 9], [2, 1, 6, 9, 7, 8, 4, 3, 6], [9, 8, 6, 3, 4, 2, 9, 1, 7], [5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]

status: incorrect

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Conclusion: Only 1 pixel is wrong.