This repository generates fresh ARC-AGI-1-style tasks that resemble the public dataset. These tasks can be used to evaluate frontier models such as BDH-CQ on problems they are unlikely to have encountered before.

BDH-CQ is a reasoning model that combines in-context learning through evolving recurrent memory with iterative reasoning in a structured, continuous latent space.

It builds on Dragon Hatchling (BDH), a post-Transformer recurrent architecture in which neuron-like units communicate through low-rank interactions and maintain context in an evolving associative state. BDH-CQ extends this architecture: at inference time, its recurrent memory is continuously updated, and queries are solved through iterative computation in latent space rather than by generating an intermediate, text-based chain of thought.

We evaluate BDH-CQ on the public ARC-AGI-1 evaluation set and use controlled ARC-like interventions to study what the model learns from demonstrations.

A 150M-parameter configuration achieves 29.5% pass@2 on the public ARC-AGI-1 evaluation set at a computed inference cost of $0.0007 per task. This is 11× cheaper per task than GPT-5.6 Luna (Low), following an 80% price reduction on July 30.

The architecture has also been tested in pretraining experiments ranging from 1B to 600B parameters, demonstrating Transformer-like scaling while retaining BDH-CQ's recurrent latent-reasoning capabilities.

This repository accompanies the following paper:

B. Engdahl, A. Kosowski, J. Chorowski, Z. Stamirowska, P. Uznański, J. Jiang, R. Phadke, R. Kinas, and R. Zhong. BDH-CQ: Introducing In-Context Learning with Recurrent Latent Reasoning.

For a broader introduction and discussion, read the accompanying blog post.

As a public benchmark, ARC-AGI-1 cannot fully isolate few-shot rule induction from potential prior familiarity with its tasks.

To provide a complementary measure, arc-task-gen creates a private evaluation set with similar properties, enabling meaningful comparisons between public-benchmark performance and performance on newly generated tasks.

The generated tasks.json file follows the standard ARC format:

{
  "train": [],
  "test": []
}

It is compatible with existing ARC evaluation harnesses.

Follow the instructions in instructions.md to start generating tasks.

BDH-CQ's ARC-AGI-1 results were evaluated and reproduced by Łukasz Kaiser, a co-author of the Transformer architecture and TensorFlow.

The results were also independently reproduced by Remigiusz Kinas, a contributor to Bielik, and Richard Zhong, an NYU researcher. Their work focused on model evaluation and benchmark robustness.