Reading Between the Dots: Decoding Hidden Computation across Filler Tokens
Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visible chain-of-thought (CoT). This is a limit case for behavioral oversight, where surface tokens carry no information about the underlying reasoning. But hidden from the output is not the same as hidden from us. On four task families (fact retrieval, parallel numeric composition, string manipulation, and in-context computation), two open-weights frontier models (DeepSeek V3, Kimi K2) compute over filler tokens in a legible way: attention routes the question through the filler region to the answer, logit-lens readouts show retrieved facts emer...
| Authors | Kaley Brauer, Claudio Mayrink Verdun, Samuel Marks |
|---|---|
| Primary category | cs.CL |
| Published | 3 July 2026 |
| Last updated at source | 1 October 2026 |
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