SanSi: A Looped Typed Decision Model for System 1.5 Thinking
Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass. A single pass is fast, intuitive System 1 thinking. We study what lies between one pass and generated reasoning: looping, in which the same layers are recursively applied several times before one typed readout. Each loop lets the model revise its hidden state before it commits to an answer, without generating a token; we call this System 1.5 thinking. We propose SanSi, which turns a pre-trained looped language model into a typed decision model. The option probabilities are read after every loop, and every loop is trained...
| Authors | Shuyu Gan, Young-Jun Lee, Dongyeop Kang |
|---|---|
| Primary category | cs.CL |
| Published | 6 October 2026 |
| Last updated at source | 10 October 2026 |
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