SOURCE-LINKED INTELLIGENCE
Trading Depth for Time in Recurrent Transformers
Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of the next. This raises a natural question: is additional computation better spent on more temporal steps or greater physical depth? We investigate this question using Latent Recurrent Transformers (LRTs), which retain one backbone forward pass per vocabulary token during decoding and provide a controlled setting for comparing these two ways of adding computation. Specifically, we insert a latent thought token between consecutive vocabulary tokens.
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T10:34:51.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.