Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead. Latent reasoning offers a promising alternative by carrying part of the computation in continuous representations. However, existing methods typically predefine when latent computation is invoked and how it is allocated during decoding, leaving a key problem unresolved: when to invoke latent computation, what type of computation to perform, and how much budget to allocate. We propose \textbf{Ty}ped \textbf{L}at\textbf{e}nt \textbf{R}easoning (Tyler), a typed and b...
| Authors | Hanyu Lin, Min Cai, Jiawei Wen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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