BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
- Published
- Source
- arXiv
- Paper number
- 876
- Field
- Neural Networks
- arXiv ID
- 2608.09888
Key points
- It proposes a 'latent reasoning' approach that performs recurrent reasoning in hidden state space without outputting tokens.
- With a very small 150M-parameter model, it reaches 29.5% accuracy on ARC-AGI-1 at a cost of $0.0007 per task.
- It sets a new cost-accuracy standard while cutting cost to about one-thousandth of the previous best system.
- It systematically analyzes the strengths and limits of in-context learning from demonstrations alone, using experiments with color mapping, boundary propagation, rotation, and other compositions.
- It designed an architecture that can scale naturally to 1 trillion parameters, and early experiments confirm Transformer-like scaling laws.
Paper links
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