Latent Chain-of-Thought Improves Structured-Data Transformers
Chain-of-thought and more broadly test-time compute are known to augment the expressive capabilities of language models and have led to major innovations in reasoning. Motivated by this success, this paper explores latent chain-of-thought as well as the impact of depth and looping for time-series and tabular data. We propose a recurrent scheme in which a structured-data transformer, after an initial forward pass, compresses its query-position hidden states into feedback tokens that are appended to the input and processed again, allowing multiple rounds of latent computation before prediction. We compare CoT models against a same-depth no-CoT baseline, a deeper baseline matched to the CoT ...
| Authors | Carson Dudley, Samet Oymak |
|---|---|
| Primary category | cs.LG |
| Published | 11 May 2026 |
| Last updated at source | 19 May 2026 |
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