# Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

arXiv: https://arxiv.org/abs/2605.14368 · HTML: https://mlsanity.com/paper/2605.14368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Discrete and text diffusion, one of 14 directions in Diffusion models.
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 14 May 2026.

## Placed in

- Diffusion models / [Discrete and text diffusion](https://mlsanity.com/diffusion/discrete-and-text-diffusion/)

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Simple and Effective Masked Diffusion Language Models](https://mlsanity.com/paper/2406.07524/) (2024)
- [Simplified and Generalized Masked Diffusion for Discrete Data](https://mlsanity.com/paper/2406.04329/) (2024)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [Dream 7B: Diffusion Large Language Models](https://mlsanity.com/paper/2508.15487/) (2025)
- [Reducing Transformer Depth on Demand with Structured Dropout](https://mlsanity.com/paper/1909.11556/) (2019)
- [Autoregressive Diffusion Models](https://mlsanity.com/paper/2110.02037/) (2021)
- [Self-conditioned Embedding Diffusion for Text Generation](https://mlsanity.com/paper/2211.04236/) (2022)
- [Latent Diffusion for Language Generation](https://mlsanity.com/paper/2212.09462/) (2022)
- [DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models](https://mlsanity.com/paper/2310.05793/) (2023)
- [Continuous Diffusion Model for Language Modeling](https://mlsanity.com/paper/2502.11564/) (2025)
- [Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective](https://mlsanity.com/paper/2505.15045/) (2025)
- [Language modeling via stochastic processes](https://mlsanity.com/paper/2203.11370/) (2022)
- [CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think](https://mlsanity.com/paper/2603.02547/) (2026)

## Built on by (most cited first, 2 of 2)

- [Continuous Language Diffusion as a Decoder-Interface Problem](https://mlsanity.com/paper/2606.08810/) (2026)
- [Temporally-Resolved Token Attribution Reveals the Generation Dynamics of Diffusion Language Models](https://mlsanity.com/paper/2610.01177/) (2026)

## Abstract (arXiv)

Continuous diffusion language models require choosing a representation space in which denoising operates, yet it remains unclear which representations are most compatible with diffusion. We study a basic design question: where inside a pretrained language model should continuous diffusion operate? We formulate this as a hidden-state interface-selection problem and hypothesize that diffusion-friendly interfaces can be identified from representation geometry. We operationalize this hypothesis using three training-free geometric proxies: local compactness, global stiffness, and effective rank. Across two 8B-scale backbones, the resulting geometry score predicts fixed-budget diffusion bridgeability beyond the dominant effect of layer depth. We then instantiate DiHAL, a Locate-and-Replace framework that replaces the transformer prefix below a selected interface with conditional diffusion while retaining the pretrained suffix and LM head. Under full training, geometry-selected interfaces remain close to validation-loss oracles and outperform worst-layer controls, while the diffusion bridge outperforms a parameter-matched deterministic replacement under matched diagnostic conditions. These results suggest that the representation space in which diffusion operates should itself be treated as a first-class design variable for continuous diffusion in language models.