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arXiv · 2606.08408

TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering

Ryandito Diandaru·Ikhlasul Akmal Hanif·Fadli Aulawi Al Ghiffari·Ahmed Elshabrawy·Alham Fikri Aji
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Abstract

We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept. We propose TimpaTeks, an automatic in-place text modification mechanism using DLMs. Experiments on IMDB movie reviews (sentiment) and a synthetic Cats and Dogs Dataset (arbitrary, more unconventional concept steering) show that TimpaTeks provides a feasible novel mechanism to steer diffusion language model outputs in-place. TimpaTeks enables in-place modification while simultaneously lowers sentence perplexity and retaining the original sentence structre without the need of instruction tuned models. TimpaTeks is also computationally cheaper than prompt-based DLM steering, as it performs denoising in-place rather than constructing an additional prompt-conditioned output sequence.

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Ryandito Diandaru, Ikhlasul Akmal Hanif, Fadli Aulawi Al Ghiffari, Ahmed Elshabrawy, Alham Fikri Aji. 2026-06-07. TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering. https://arxiv.org/abs/2606.08408

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