I’ve been exploring the ARC (Abstraction and Reasoning Corpus) dataset and recently conducted a deep dive into two tasks: train_0000 and train_0743. Using a visualization app to examine the grids, I identified key transformation rules and patterns. Below is a summary of my findings.

Task: train_0000

This task involves transforming input grids filled mostly with 7s. Specific values (4 and 5) act as triggers for drawing new lines of 1s.

Key Rules:

• All original non-7 values are preserved in place.
• If a value 5 appears, a vertical line of 1s is drawn from that point downward (or upward if blocked).
• If a value 4 appears, a horizontal line of 1s is drawn from that point rightward (or leftward if blocked).
• Lines stop when they hit a non-7 value or the grid edge.
• If the direction is blocked by a boundary, it reverses.

Example Insight:

In one example:

• A value 5 at position (1,8) creates a vertical line below it.
• A value 4 at position (5,1) creates a horizontal line to the right.

Screenshot: https://github.com/mubrij/My-ARC-AGI-Starter-Notebook-with-Zero-Shot-Learning-Approach/blob/main/Screenshot%202025-07-28%20230455.png

Task: train_0743

This task presents more complexity. It features multiple distinct values (1, 3, 5, 6), each generating vertical lines. Intersection points are marked with the value 4.

Key Rules:

• Each value generates a vertical line of itself within its column.
• When values interact, 4s are placed at the cross-reference positions of the grid.
• The grid allows for multiple simultaneous transformations.

Example Insight:

• A value 3 at (5,9) and a 1 at (10,3) produce lines and result in 4s at their intersection points: (5,3) and (10,9).

Screenshot: https://github.com/mubrij/My-ARC-AGI-Starter-Notebook-with-Zero-Shot-Learning-Approach/blob/main/Screenshot%202025-07-28%20230517.png

Comparative Analysis

https://github.com/mubrij/My-ARC-AGI-Starter-Notebook-with-Zero-Shot-Learning-Approach/blob/main/Screenshot%202025-07-28%20225913.png

Visual Comparison Screenshot: https://github.com/mubrij/My-ARC-AGI-Starter-Notebook-with-Zero-Shot-Learning-Approach/blob/main/Screenshot%202025-07-28%20225913.png

Final Thoughts

• train_0000 demonstrates directional line creation with basic boundary constraints.
• train_0743 showcases multi-value interactions and complex spatial logic.
• Both tasks reveal the rich reasoning challenges posed by the ARC dataset.

I’m open to feedback or discussion feel free to share your thoughts if you’ve explored ARC tasks too!