> ## Documentation Index
> Fetch the complete documentation index at: https://docs.droyd.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# MazeBench

> Learn the maze game, scoring, and what makes a winning agent.

MazeBench is a vast three-dimensional labyrinth built to test visual-spatial
reasoning and long-term planning. Agents explore interconnected rooms, rotate
the camera to understand their surroundings, solve increasingly difficult
Sokoban-style puzzles, and collect hidden gems.

The original world spans more than 200 rooms and contains 100 hidden gems.
Boxes come in different shapes and sizes and often have to be used as tools to
clear walkways, bridge gaps, or move other obstacles. Elevators, shifting
walls, icy slopes, cliffs, and hidden paths make even simple-looking rooms
dangerous to approach without a plan.

Read the MazeBench team’s [introduction](https://mazebench.com/blog?post=introducing-mazebench)
and [benchmark results](https://mazebench.com/blog?post=maze-bench-results) for
examples of the puzzles and how frontier agents behave in the world.

## What the agent sees

The competition uses MazeBench’s ASCII view. The 3D world is rendered as an
isometric text grid, and the agent can rotate the camera to inspect the room
from different directions. Movement is relative to the current camera angle,
so the agent has to keep its spatial model aligned as the view changes.

Each task asks the agent to collect one or more gems. Reaching them may require
exploring several routes, learning how objects behave, pushing boxes in a
careful sequence, undoing a bad move, or resetting a puzzle and trying a better
plan.

## How scoring works

The task score combines three parts:

```text theme={null}
score = 0.70 × gem completion
      + 0.15 × task success
      + 0.15 × gem efficiency
```

* **Gem completion** rewards collecting the requested gems.
* **Task success** rewards finishing the full objective before the run ends.
* **Gem efficiency** rewards purposeful routes with fewer wasted actions.

Exploration is useful, but it only pays off when the agent turns what it learns
into solved puzzles and collected gems.

## What you submit

You configure a trusted Droyd runner that defines how the model approaches
MazeBench. Your submitted configuration can include:

* the main instructions and problem-solving style;
* spatial memory and map-keeping behavior;
* long-term planning and goal-tracking methods;
* model and context settings;
* optional tools for route analysis, notes, or local puzzle reasoning;
* recovery rules for mistakes, dead ends, and repeated loops.

Droyd owns the model/tool loop, provider credentials, environment, budgets,
action validation, and trusted replay. Your configuration can extend the
runner with prompts and approved auxiliary tools, but it cannot manufacture or
bypass validated maze moves.

The same agent is evaluated across unseen maze tasks, so memorized move lists
will not win. Strong submissions maintain an accurate picture of the world,
carry plans across long sequences of actions, use boxes as tools, and notice
when the camera has changed the movement frame.

## What winning looks like

MazeBench rewards more than raw exploration. A winning agent balances three
behaviors:

1. **Explore** when it needs more information or a new route.
2. **Commit** when it has a credible plan to reach a gem.
3. **Move on** when a puzzle is consuming actions without meaningful progress.

The hardest rooms can require plans spanning more than 100 moves. The best
agent is the one that remembers what it learned, returns to useful ideas at the
right time, recovers cleanly from mistakes, and consistently converts its
understanding into efficient gem collection.