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

# ARC-AGI-3 Scoring Methodology

> How ARC-AGI-3 scoring works

ARC-AGI-3 uses **Relative Human Action Efficiency** (RHAE, pronounced "ray") to score AI systems.

RHAE measures per-level action efficiency compared to a human baseline, normalized per game, across all games.

## What Gets Measured

AI is scored on two criteria:

1. **Completion** — How many levels did the AI complete in each game?
2. **Efficiency** — How many actions did the AI take compared to humans?

## What Counts as an Action

An *action* is a discrete interaction with the environment. Each turn where the agent submits a command, move, or input that affects the game state counts as an action.

Internal operations that do not alter the environment (tool calls, reasoning steps, retries) are **not counted** as actions.

## Human Baseline

Human baselines are established through controlled testing where participants play each ARC-AGI-3 game for the first time (having never seen the game before). For each game, multiple first-time players are observed, and the **upper median human** (by fewest actions) per level is recorded as the baseline.

The upper median is used rather than the average. For an even number of players, the upper of the two middle entries is selected. For example, if four players complete a level, third place is the baseline; if five players complete it, third place is still the baseline.

Using the upper median human per level:

* Reflects typical proficient human performance rather than outlier runs
* Reduces the impact of luck on any individual level
* Keeps the baseline grounded in real play, not theoretical speed-runs

## How Scoring Works

### Per-Level Scoring

For each level the AI completes, calculate:

```
level_score = (human_baseline_actions / ai_actions) ^ 2
```

* If human baseline is 10 actions and AI takes 10 → level score is 1.0 (100%)
* If human baseline is 10 actions and AI takes 20 → level score is 0.25 (25%)
* If human baseline is 10 actions and AI takes 100 → level score is 0.01 (1%)

### Per-Level Score Cap

The maximum score per level is capped at **1.15x** human baseline. If an AI discovers a shortcut and completes a level faster than humans, it can receive at most 1.15.

This ensures a single subpar level does not disproportionately drag down the overall score for an AI that generalizes well across an entire game.

### Per-Game Aggregation

The game score is the **weighted average** of all per-level scores, using the 1-indexed level number as the weight. This underweights the starting tutorial/easy levels and overweights the more difficult later levels where mastery must be demonstrated.

The maximum game score is also determined by this weighted average structure — it is capped based on how many levels the AI actually completed. To unlock a maximum game score of 100%, the AI must complete all levels, including the final one.

**Example:** A game has 5 levels and the AI completes only the first 4:

```
max_game_score = (1 + 2 + 3 + 4) / (1 + 2 + 3 + 4 + 5) = 10 / 15 = 66.7%
```

No matter how efficiently the AI played levels 1–4, its game score cannot exceed 66.7%.

### Total Score

Total score is the **average of all game scores**, resulting in a final score between 0% and 100%.

## Score Interpretation

| Score | Interpretation                                                                |
| ----- | ----------------------------------------------------------------------------- |
| 100%  | AI completes all games/levels while matching or surpassing human efficiency   |
| 1-99% | A mixture of level completion rates and efficiency relative to human baseline |
| 0%    | AI never completes a level across any game                                    |