Ff71a9 2

datasource file: /Users/neoneye/git/simon_arc_lab/run_tasks_result/20241222_033847_task_to_prompt_o3_format/task_to_prompt.jsonl
datasource row: 495
groupname: arcagi
dataset_id: ARC-AGI
task_id: 25ff71a9
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 concise

---

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

Example 1:

Input:
1 1 1
0 0 0
0 0 0
Output:
0 0 0
1 1 1
0 0 0

Example 2:

Input:
0 0 0
1 1 1
0 0 0
Output:
0 0 0
0 0 0
1 1 1

Example 3:

Input:
0 1 0
1 1 0
0 0 0
Output:
0 0 0
0 1 0
1 1 0

Example 4:

Input:
0 2 2
0 0 2
0 0 0
Output:
0 0 0
0 2 2
0 0 2

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:
2 0 0
2 0 0
0 0 0


---

RESPONSE:
Output:
0 0 0
2 0 0
2 0 0

---


response byte count: 25
response item count: 20
elapsed: 1.12 seconds

expected output:
[[0, 0, 0], [2, 0, 0], [2, 0, 0]]

predicted output:
[[0, 0, 0], [2, 0, 0], [2, 0, 0]]

status: correct