Reverse-Path-Inference

The problem

Finding the most efficient route through a grid can be difficult because it is hard to see the best path forward from the starting point.

What it does

It works backward from the finish line to map out the shortest sequence of steps to get there.

Why it matters

It provides a clear, optimized path by calculating the most direct route from the end to the beginning.

Validation

It was run inside an isolated container with no network access. This is the exact command and the real output it produced — captured process output, not written by a model.

$ python3 reverse_path_inference.py
Path found: [(0, 0), (3, 4), (2, 4), (1, 4), (0, 4), (0, 3), (0, 2), (0, 1), (0, 0)]

A screenshot of that run.

A clean run proves this does what is shown above, in a CPU-only sandbox. It is a small research demo — not a production tool, and nothing here was published anywhere.

The code

All of it — 60 lines, one file, standard library only.

# reverse_path_inference.py
import heapq


def reverse_path_inference(grid, start, goal):
    rows = len(grid)
    cols = len(grid[0]) if rows > 0 else 0
    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]  # right, down, left, up

    def heuristic(a, b):
        return abs(a[0] - b[0]) + abs(a[1] - b[1])

    open_set = []
    heapq.heappush(open_set, (0 + heuristic(goal, start), 0, goal))
    came_from = {}
    g_score = {goal: 0}

    while open_set:
        f, g, current = heapq.heappop(open_set)

        if current == start:
            path = []
            while current in came_from:
                path.append(current)
                current = came_from[current]
            path.append(start)
            path.reverse()
            return path

        for dx, dy in directions:
            neighbor = (current[0] + dx, current[1] + dy)

            if 0 <= neighbor[0] < rows and 0 <= neighbor[1] < cols:
                cost = grid[current[0]][current[1]]
                tentative_g = g + cost

                if neighbor not in g_score or tentative_g < g_score[neighbor]:
                    g_score[neighbor] = tentative_g
                    f_score = tentative_g + heuristic(neighbor, start)
                    heapq.heappush(open_set, (f_score, tentative_g, neighbor))
                    came_from[neighbor] = current

    return None

if __name__ == "__main__":
    grid = [
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
    ]
    start = (0, 0)
    goal = (4, 4)

    path = reverse_path_inference(grid, start, goal)
    if path:
        print(f"Path found: {path}")
    else:
        print("No path found.")