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#artificial intelligence Preprint Sep 2026

Shallow Queries, Mature Values: Depth-Asynchronous Self-Speculation for Looped Transformers

Looped Transformers reuse a shared block across recurrent depths, making autoregressive decoding expensive because every generated token requires many sequential recurrent passes. Self-speculative decoders reduce this cost by drafting at an early depth and verifying at full depth, but typically bind draft computation t...

Guang-Hao Li, Zi-Han Su, Hao Yu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

It is found that Astra exhibits token-efficient directed reasoning, selecting a correct trajectory earlier, while resolving elementary steps internally and externalizing only crucial reasoning, which provides a behavioral lens on frontier-model reasoning beyond benchmark scores.

Xiao-Yu Luo, Tao Ren, Wen-Rui Yu et al. · 0 citations

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