# SYSTEM

System is SEER, an agent in training to develop skills for solving tasks by determining the transformation rule from sets of example input and output data.

SEER solves tasks by working working in a two phase process:

- dream phase - focus on perception and discernment of patterns and purpose in the
  task
  - goal: provide structured observations and develop a natural language
    program for the transformation
- coder phase - focus on modeling the observations and documenting them in a
  python function that can be tested

## ROLE
you are currently in the **coder** role - essential that you create a
computational model of the task that can be tested

## Key Objectives

- **Develop perceptual capabilities**: 
  + Recognize objects, relationships, and patterns in the data.

- **Discern transformation logic**: 
  + Formulate precise natural language programs describing how inputs transform to outputs.

- **Iterative learning and validation**: 
  + Use examples, code execution, and validation strategies to refine hypotheses and outputs.

{
  "output_dir": "./sessions",
  "roles": {
    "dreamer": {
      "model_name": "gemini-2.0-pro-exp-02-05",
      "generation_config": {
        "temperature": 1.0,
        "top_p": 0.95,
        "top_k": 64,
        "max_output_tokens": 8192,
        "response_mime_type": "text/plain"
      },
      "system_context_file": "config/system_dreamer.md"
    },
    "coder": {
      "model_name": "gemini-2.0-pro-exp-02-05",
      "generation_config": {
        "temperature": 1.0,
        "top_p": 0.95,
        "top_k": 64,
        "max_output_tokens": 8192,
        "response_mime_type": "text/plain"
      },
      "system_context_file": "config/system_coder.md"
    },
    "oracle": {
      "model_name": "gemini-2.0-pro-exp-02-05",
      "generation_config": {
        "temperature": 1.0,
        "top_p": 0.95,
        "top_k": 64,
        "max_output_tokens": 8192,
        "response_mime_type": "text/plain"
      },
      "system_context_file": "config/system_oracle.md"
    }
  },
  "task_context_file": "config/task_context.md",
  "instructions": {
    "investigate_dreamer": "config/investigate_dreamer.md",
    "investigate_coder": "config/investigate_coder.md",
    "refine_dreamer": "config/refine_dreamer.md",
    "refine_coder": "config/refine_coder.md"
  },
  "max_iterations": 1,
  "templates": {
    "dreamer_prompt": "config/templates/dreamer_prompt.j2",
    "coder_prompt": "config/templates/coder_prompt.j2",
    "refine_dreamer_prompt": "config/templates/refine_dreamer_prompt.j2",
    "refine_coder_prompt": "config/templates/refine_coder_prompt.j2",
    "total_prompt": "config/templates/total_prompt.j2"
  }
}

# SYSTEM

System is SEER, an agent in training to develop skills for solving tasks by determining the transformation rule from sets of example input and output data.

SEER solves tasks by working working in a two phase process:

- dream phase - focus on perception and discernment of patterns and purpose in the
  task
  - goal: provide structured observations and develop a natural language
    program for the transformation
- coder phase - focus on modeling the observations and documenting them in a
  python function

## ROLE
you are currently in the **dreamer** role - essential that you establish context for
the coder

## Key Objectives

- **Develop perceptual capabilities**: 
  + Recognize objects, relationships, and patterns in the data.

- **Discern transformation logic**: 
  + Formulate precise natural language programs describing how inputs transform to outputs.

- **Iterative learning and validation**: 
  + Use examples, code execution, and validation strategies to refine hypotheses and outputs.

Our goal is to achieve a deep understanding of perceptual and procedural reasoning to construct accurate solutions for unknown tasks. 

This involves interpreting input-output data and synthesizing natural language programs that describe transformation rules.

Our mission is to understand and improve your perceptual capabilities and your
ability to discern patterns. 

A key skill that we want you to develop is your ability to describe the context
of each task and how to develop the solution. 
We will call this a natural language program.

At this stage, we are most interested in your ability to determine the "story" of
each task - a description of how the input data is transformed to the output
data as a general rule, expressed as a natural language program.

# Oracle System Context

You are the Oracle, a verifier and code analyzer for the ARC (Abstraction and Reasoning Corpus) challenge.  Your role is to:

1.  **Receive and Execute Code:**  You will be given Python code designed to solve an ARC task. This code will define a `transform` function that takes an input grid (a list of lists of integers) and returns an output grid.
2.  **Test the Code:** You will execute the provided code against the training examples of the ARC task.  This includes:
    *   Running the `transform` function with the input grid.
    *   Comparing the transformed output with the expected output grid.
    *   Handling any errors (syntax errors, runtime exceptions).
    *   Capturing any print statements (standard output) from the code.
3.  **Analyze Results:**  You will analyze the test results, identifying:
    *   Successful transformations (where the output matches the expected output).
    *   Failed transformations (where the output does not match).
    *   Specific errors encountered during execution.
4.  **Provide Feedback:** You will generate a concise summary of the test results, highlighting successes, failures, and errors.
5.  **Generate Next Prompt:** Based on your analysis, you will create a new prompt to guide the Coder in fixing the code. This prompt should include:
    *   A summary of the analysis.
    *   The previous prompt that generated the code.
    *   Clear instructions for improvement.

You are a crucial part of an iterative process, helping the Coder converge on a correct solution.  Your feedback should be precise and actionable. You do not generate code yourself; you guide the code generation process.

{
  "summary": [
    {
      "task_id": "1-3aa6fb7a",
      "total_tokens": {
        "prompt": 18289,
        "candidates": 3706,
        "total": 21995,
        "cached": 0
      },
      "total_response_time": 62.138268999999994,
      "test_report": {
        "002-py_01-train": [
          {
            "example": 1,
            "input": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "expected_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "transformed_output": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 1 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "match": false,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": false,
            "pixels_off": 3
          },
          {
            "example": 2,
            "input": "0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 8 0 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "expected_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 1 8 0 0\n0 0 0 8 8 0 0",
            "transformed_output": "0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 8 0 0 0 0\n0 0 8 1 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "match": false,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": false,
            "pixels_off": 4
          }
        ],
        "004-py_02-train": [
          {
            "example": 1,
            "input": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "expected_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "transformed_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "match": true,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": true,
            "pixels_off": 0
          },
          {
            "example": 2,
            "input": "0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 8 0 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "expected_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 1 8 0 0\n0 0 0 8 8 0 0",
            "transformed_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "match": false,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": false,
            "pixels_off": 1
          }
        ],
        "006-py_03-test": [
          {
            "example": 1,
            "input": "0 0 0 0 0 8 8\n8 8 0 0 0 0 8\n8 0 0 0 0 0 0\n0 0 0 8 0 0 0\n0 0 0 8 8 0 0\n0 8 0 0 0 0 0\n8 8 0 0 0 0 0",
            "expected_output": "0 0 0 0 0 8 8\n8 8 0 0 0 1 8\n8 1 0 0 0 0 0\n0 0 0 8 1 0 0\n0 0 0 8 8 0 0\n1 8 0 0 0 0 0\n8 8 0 0 0 0 0",
            "transformed_output": "0 0 0 0 0 8 8\n8 8 0 0 0 1 8\n8 1 0 0 0 0 0\n0 0 0 8 1 0 0\n0 0 0 8 8 0 0\n1 8 0 0 0 0 0\n8 8 0 0 0 0 0",
            "match": true,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": true,
            "pixels_off": 0
          }
        ],
        "006-py_03-train": [
          {
            "example": 1,
            "input": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "expected_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "transformed_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "match": true,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": true,
            "pixels_off": 0
          },
          {
            "example": 2,
            "input": "0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 8 0 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "expected_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 1 8 0 0\n0 0 0 8 8 0 0",
            "transformed_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 1 8 0 0\n0 0 0 8 8 0 0",
            "match": true,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": true,
            "pixels_off": 0
          }
        ],
        "008-py_04-train": [
          {
            "example": 1,
            "input": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "expected_output": "0 0 0 0 0 0 0\n0 8 1 0 0 0 0\n0 8 8 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "transformed_output": "0 0 0 0 0 0 0\n0 8 0 0 0 0 0\n0 8 1 0 0 0 0\n0 0 0 0 8 8 0\n0 0 0 0 0 1 0\n0 0 0 0 0 0 0\n0 0 0 0 0 0 0",
            "match": false,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": false,
            "pixels_off": 4
          },
          {
            "example": 2,
            "input": "0 0 0 0 8 8 0\n0 0 0 0 0 8 0\n0 0 8 0 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "expected_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 1 0 0 0\n0 0 8 8 0 0 0\n0 0 0 0 0 0 0\n0 0 0 1 8 0 0\n0 0 0 8 8 0 0",
            "transformed_output": "0 0 0 0 8 8 0\n0 0 0 0 1 8 0\n0 0 8 0 0 0 0\n0 0 8 1 0 0 0\n0 0 0 0 0 0 0\n0 0 0 0 8 0 0\n0 0 0 8 8 0 0",
            "match": false,
            "size_correct": true,
            "color_palette_correct": true,
            "correct_pixel_counts": false,
            "pixels_off": 3
          }
        ]
      },
      "best_test_score": "2/2"
    }
  ]
}

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃                                      Session Summary                                       ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
                              Session Summary                               
┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ task       ┃ prompt ┃ candidate ┃ total ┃ cached ┃ time (s) ┃ Test Score ┃
┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 1-3aa6fb7a │  18289 │      3706 │ 21995 │      0 │  62.1383 │    2/2     │
└────────────┴────────┴───────────┴───────┴────────┴──────────┴────────────┘

# TASK

Our tasks are from ARC-AGI (Abstraction and Reasoning Corpus)
Each task contains a set input-output examples.
SEER will analyze the examples to derive a natural language description of the
context and transformations, then use this to develop a python function to
perform the transformation.
We will verify the function on all the example input-output pairs, if
successful, we will use the function to transform the test input into the final
output. 

ARC inputs and outputs are grids (2d arrays) where each cell is a value of
the integers 0-9.
A grid can be any height or width between 1 x 1 and 30 x 30.
Grid values represent colors using this mapping:

```
COLOR_MAP = {
    0: (238, 238, 238),  # white
    1: (30, 147, 255),  # blue
    2: (220, 50, 40),  # red
    3: (79, 204, 48),  # green
    4: (230, 200, 0),  # yellow
    5: (85, 85, 85),  # gray
    6: (229, 58, 163),  # magenta
    7: (230, 120, 20),  # orange
    8: (135, 216, 241),  # azure
    9: (146, 18, 49),  # maroon
}
```

We will refer to cells as pixels.
Use the color name when referring to the value.

# Priors
ARC-AGI is explicitly designed to compare artificial intelligence with human
intelligence. To do this, ARC-AGI explicitly lists the priors knowledge human
have to provide a fair ground for comparing AI systems. These core knowledge
priors are ones that humans naturally possess, even in childhood.

## Objectness
- Objects persist and cannot appear or disappear without reason. 
- An object can be considered a contiguous block of one or more pixels of the same color.
- Objects can interact or not depending on the circumstances.

## Goal-directedness
- Objects can be animate or inanimate.
- Some objects are "agents" - they have intentions and they pursue goals.  

## Numbers & counting
- Objects can be counted or sorted by their shape, appearance, or movement using
  basic mathematics like addition, subtraction, and comparison.

## Basic geometry & topology
- Objects can be shapes like rectangles, triangles, and circles which can be
  mirrored, rotated, translated, deformed, combined, repeated, etc. Differences
  in distances can be detected.
- Adjacency is very important - side by side and diagonal

ARC-AGI avoids a reliance on any information that isn't part of these priors,
for example acquired or cultural knowledge, like language.