EduDevCommons/ARC_AGI_Wayfinder_Agent

ARC-AGI-3 Wayfinder Agent

Competition agent for the ARC Prize 2026 — ARC-AGI-3 track (Kaggle Code Competition).

Architecture

The agent uses a hybrid world-model + planning architecture combining four modules mapped to the benchmark's core capabilities:

Module	Role	Capability
Perception Encoder	CNN over one-hot 64×64×16 frames → compact latent	—
World/Transition Model	Self-supervised P(frame changes	state, action) + forward model
State Memory Graph	Hash-deduplicated directed graph of observed states	Exploration
Intrinsic Reward	Extrinsic Δscore + graph novelty + prediction-error curiosity	Goal-setting
Planner	Short-horizon tree search using world model as simulator	Planning
Action Head	Hierarchical: action-type softmax + conv coordinate head for ACTION6	—

Quick Start

# Install
uv pip install -e ".[dev]"

# Run against a public game
uv run main.py --agent=wayfinder --game=ls20

# Run tests
uv run pytest

# Offline evaluation
uv run python eval/run_local_eval.py --agent=wayfinder --games=ls20,ls21,ls22

Repository Layout

agents/wayfinder/     # Core agent modules (perception, world_model, memory_graph, ...)
training/             # Replay buffer, training loops, configs
eval/                 # Offline evaluation harness, metrics
notebooks/            # Kaggle submission notebook
tests/                # Unit + integration tests

Key Constraints

• No internet at inference time — Kaggle scoring sessions disable network access.
• MIT/CC0 license — all authored code; third-party deps must be permissively licensed.
• Action budget — agent self-terminates stuck levels (~5× human median actions).
• No per-game hardcoding — same code runs against all unseen games.

Reproducing Results

1. Install dependencies: uv pip install -e ".[dev]"
2. Download public games via the SDK's local mode.
3. Run evaluation: uv run python eval/run_local_eval.py --agent=wayfinder
4. Results are logged to eval/results/ with per-game/level breakdowns.

License

MIT — see LICENSE.