@inproceedings{rulli-etal-2026-subliminal,
title = "Subliminal Clocks: Latent Time Modelling in Diffusion Language Models",
author = "Rulli, Maximo and
Fontanari, Thomas V. and
Petruzzi, Simone and
Alvetreti, Federico and
Strano, Giorgio and
Crisostomi, Donato and
Nikolaou, Giorgos and
Mencattini, Tommaso and
Santilli, Andrea and
Rodol{\`a}, Emanuele and
Scardapane, Simone and
Devoto, Alessio",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1749/",
pages = "39904--39927",
ISBN = "979-8-89176-459-0",
abstract = "Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, MDLMs are not explicitly conditioned on a timestep, raising a natural question: do these models internally represent denoising progress, and how is such information used downstream? In this work, we show that MDLMs do in fact encode a latent representation related to the diffusion timestep within their residual streams. We find that this signal can be reliably extracted using probes across layers, indicating that denoising progress is decodable from internal activations. We further demonstrate that steering the model along a low-dimensional subspace associated with the inferred timestep allows us to systematically modulate its notion of denoising progress, leading to controlled and predictable changes in model confidence and entropy. Finally, we analyse the geometry of the identified representation, showing that it exhibits structured and interpretable properties in activation space, and shedding light on how such a signal is manipulated by these models."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rulli-etal-2026-subliminal">
<titleInfo>
<title>Subliminal Clocks: Latent Time Modelling in Diffusion Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maximo</namePart>
<namePart type="family">Rulli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="given">V</namePart>
<namePart type="family">Fontanari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simone</namePart>
<namePart type="family">Petruzzi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Federico</namePart>
<namePart type="family">Alvetreti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giorgio</namePart>
<namePart type="family">Strano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Donato</namePart>
<namePart type="family">Crisostomi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giorgos</namePart>
<namePart type="family">Nikolaou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tommaso</namePart>
<namePart type="family">Mencattini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrea</namePart>
<namePart type="family">Santilli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emanuele</namePart>
<namePart type="family">Rodolà</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simone</namePart>
<namePart type="family">Scardapane</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessio</namePart>
<namePart type="family">Devoto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, MDLMs are not explicitly conditioned on a timestep, raising a natural question: do these models internally represent denoising progress, and how is such information used downstream? In this work, we show that MDLMs do in fact encode a latent representation related to the diffusion timestep within their residual streams. We find that this signal can be reliably extracted using probes across layers, indicating that denoising progress is decodable from internal activations. We further demonstrate that steering the model along a low-dimensional subspace associated with the inferred timestep allows us to systematically modulate its notion of denoising progress, leading to controlled and predictable changes in model confidence and entropy. Finally, we analyse the geometry of the identified representation, showing that it exhibits structured and interpretable properties in activation space, and shedding light on how such a signal is manipulated by these models.</abstract>
<identifier type="citekey">rulli-etal-2026-subliminal</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.1749/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>39904</start>
<end>39927</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
%A Rulli, Maximo
%A Fontanari, Thomas V.
%A Petruzzi, Simone
%A Alvetreti, Federico
%A Strano, Giorgio
%A Crisostomi, Donato
%A Nikolaou, Giorgos
%A Mencattini, Tommaso
%A Santilli, Andrea
%A Rodolà, Emanuele
%A Scardapane, Simone
%A Devoto, Alessio
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F rulli-etal-2026-subliminal
%X Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, MDLMs are not explicitly conditioned on a timestep, raising a natural question: do these models internally represent denoising progress, and how is such information used downstream? In this work, we show that MDLMs do in fact encode a latent representation related to the diffusion timestep within their residual streams. We find that this signal can be reliably extracted using probes across layers, indicating that denoising progress is decodable from internal activations. We further demonstrate that steering the model along a low-dimensional subspace associated with the inferred timestep allows us to systematically modulate its notion of denoising progress, leading to controlled and predictable changes in model confidence and entropy. Finally, we analyse the geometry of the identified representation, showing that it exhibits structured and interpretable properties in activation space, and shedding light on how such a signal is manipulated by these models.
%U https://aclanthology.org/2026.emnlp-main.1749/
%P 39904-39927
Markdown (Informal)
[Subliminal Clocks: Latent Time Modelling in Diffusion Language Models](https://aclanthology.org/2026.emnlp-main.1749/) (Rulli et al., EMNLP 2026)
ACL
- Maximo Rulli, Thomas V. Fontanari, Simone Petruzzi, Federico Alvetreti, Giorgio Strano, Donato Crisostomi, Giorgos Nikolaou, Tommaso Mencattini, Andrea Santilli, Emanuele Rodolà, Simone Scardapane, and Alessio Devoto. 2026. Subliminal Clocks: Latent Time Modelling in Diffusion Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 39904–39927, Budapest, Hungary. Association for Computational Linguistics.