[2607.03502] Reading Between the Dots: Decoding Hidden Computation across Filler Tokens

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arXiv:2607.03502 (cs)

[Submitted on 3 Jul 2026]

Title:Reading Between the Dots: Decoding Hidden Computation across Filler Tokens

Authors:Kaley Brauer, Claudio Mayrink Verdun, Samuel Marks<br>View a PDF of the paper titled Reading Between the Dots: Decoding Hidden Computation across Filler Tokens, by Kaley Brauer and 2 other authors

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Abstract: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 structured, legible way: attention routes the question through the filler region to the answer, logit-lens readouts show retrieved facts emerging early and their composition crystallizing in late layers, and KV-cache transplants at filler positions causally swap outputs between examples. We introduce an unsupervised decoding pipeline that takes only hidden states as input and recovers intermediate values with 80-95% accuracy (best LLM judge) across both models and all four tasks, without ground-truth labels or training. Hidden computation that defeats behavioral CoT monitoring is, on these tasks, directly readable from the residual stream, suggesting monitorability is a property of the model's full computational trace, not just its surface tokens.

Comments:<br>Accepted to ICML 2026 Mech Interp Workshop, 10 main paper pages, 20 appendix pages

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2607.03502 [cs.CL]

(or<br>arXiv:2607.03502v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.03502

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arXiv-issued DOI via DataCite

Submission history<br>From: Kaley Brauer [view email]<br>[v1]<br>Fri, 3 Jul 2026 17:18:34 UTC (2,596 KB)

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