Autoregressive Model

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Diffusion Language Models

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Autoregressive Model

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Autoregressive Language Models

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Next-Token Prediction

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Masked Diffusion Language Models

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Autoregressive Generation

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Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement
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Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
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May 14, 2026 · cs.CLJ/K move · Enter open · S save

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

Injin Kong, Hyoungjoon Lee, Yohan Jo

Graduate School of Data Science, Seoul National University · Department of Biosystems & Biomaterials Science and Engineering, Seoul National University

Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language denoising and token recovery. We propose DiHAL, a geometry-guided diffusion-transformer hybrid that asks where diffusion should enter a pretrained transformer. DiHAL scores layers with geometry-based proxies, selects a diffusion-friendly hidden-state interface, and replaces the lower transformer prefix with a diffusion bridge while retaining the upper layers and original LM head. By reconstructing the selected-layer hidden state rather than tokens, DiHAL avoids direct continuous-to-discrete recovery. Experiments on 8B-scale backbones show that the geometry score predicts effective shallow insertion layers under a fixed bridge-training protocol and that hidden-state recovery improves over continuous diffusion baselines in a diagnostic comparison matching the diffusion/recovery training budget. These results suggest that hidden-state geometry helps identify where diffusion-based replacement is feasible inside pretrained language models.

Diffusion Language Models Autoregressive Transformers Hidden States

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