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Recurrent Looped Transformer
The Recurrent Looped Transformer architecture integrates a causal encoder with a recurrent decoder to allow reasoning depth to grow alongside the input sequence.
What: This model keeps a layer-wise sliding-window attention cache across tokens, effectively extending latent computation as sequences grow longer. It aims to bridge the gap between fixed-depth transformers and unbounded temporal reasoning.
Why it matters: This represents a move toward hardware-efficient model architectures that can maintain state over long durations, a prerequisite for persistent, long-horizon AI agents.
Decoder
- Causal encoder: A part of a neural network that processes input tokens in a sequence-dependent manner, where each token can only attend to previous tokens.
- Latent computation: Information or 'thought' steps maintained in the model's internal state that aren't directly output to the user but are used for reasoning.
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