Bridging the Discrete-Continuous Gap: Unified Multimodal Generation via Coupled Manifold Discrete Absorbing Diffusion

• URL: http://arxiv.org/abs/2601.04056v1
• Date: Wed, 07 Jan 2026 16:21:19 GMT
• Title: Bridging the Discrete-Continuous Gap: Unified Multimodal Generation via Coupled Manifold Discrete Absorbing Diffusion
• Authors: Yuanfeng Xu, Yuhao Chen, Liang Lin, Guangrun Wang,
• Abstract summary: Bifurcation of generative modeling into autoregressive approaches for discrete data (text) and diffusion approaches for continuous data (images) hinders development of truly unified multimodal systems.<n>We propose textbfCoM-DAD, a novel probabilistic framework that reformulates multimodal generation as a hierarchical dual-process.<n>Our method demonstrates superior stability over standard masked modeling, establishing a new paradigm for scalable, unified text-image generation.
• Score: 60.186310080523135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The bifurcation of generative modeling into autoregressive approaches for discrete data (text) and diffusion approaches for continuous data (images) hinders the development of truly unified multimodal systems. While Masked Language Models (MLMs) offer efficient bidirectional context, they traditionally lack the generative fidelity of autoregressive models and the semantic continuity of diffusion models. Furthermore, extending masked generation to multimodal settings introduces severe alignment challenges and training instability. In this work, we propose \textbf{CoM-DAD} (\textbf{Co}upled \textbf{M}anifold \textbf{D}iscrete \textbf{A}bsorbing \textbf{D}iffusion), a novel probabilistic framework that reformulates multimodal generation as a hierarchical dual-process. CoM-DAD decouples high-level semantic planning from low-level token synthesis. First, we model the semantic manifold via a continuous latent diffusion process; second, we treat token generation as a discrete absorbing diffusion process, regulated by a \textbf{Variable-Rate Noise Schedule}, conditioned on these evolving semantic priors. Crucially, we introduce a \textbf{Stochastic Mixed-Modal Transport} strategy that aligns disparate modalities without requiring heavy contrastive dual-encoders. Our method demonstrates superior stability over standard masked modeling, establishing a new paradigm for scalable, unified text-image generation.

Related papers

• IdGlow: Dynamic Identity Modulation for Multi-Subject Generation [23.20674988897558]
  We present IdGlow, a mask-free, progressive two-stage framework built upon Flow Matching diffusion models.<n>In the supervised fine-tuning (SFT) stage, we introduce task-adaptive timestep scheduling aligned with diffusion generative dynamics.<n>In the second stage, we design a Fine-Grained Group-Level Direct Preference Optimization (DPO) with a weighted margin formulation to simultaneously eliminate multi-subject artifacts.
  arXiv  Detail & Related papers  (2026-02-28T11:56:34Z)
• Latent Discrete Diffusion Models [18.979326092796896]
  We study discrete diffusion for language and other categorical data.<n>We propose emphLatent Discrete Diffusion Models (LDDM)<n>We present two instantiations: (i) FUJI-LDDMs, which perform fully joint denoising of tokens and latents, and (ii) SEQ-LDDMs, which sequentially resolve the latent and then the discrete chain conditionally on it.<n>For both variants we derive ELBO-style objectives and discuss design choices to learn informative latents yet amenable to diffusoin modeling.
  arXiv  Detail & Related papers  (2025-10-20T21:26:52Z)
• Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner [66.86440230599656]
  We argue that diffusion language models do not necessarily need to be in the discrete space.<n>In particular, we prove that continuous diffusion models have stronger expressivity than discrete diffusions and looped transformers.<n>We propose Coevolutionary Continuous Diffusion (CCDD), which defines a joint multimodal diffusion process on the union of a continuous representation space and a discrete token space.
  arXiv  Detail & Related papers  (2025-10-03T17:44:41Z)
• Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling [87.34677262370924]
  Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token.<n>This creates an 'information void' where semantic information that could be inferred from unmasked tokens is lost between denoising steps.<n>We introduce Continuously Augmented Discrete Diffusion, a framework that augments the discrete state space with a paired diffusion in a continuous latent space.
  arXiv  Detail & Related papers  (2025-10-01T18:00:56Z)
• Unifying Continuous and Discrete Text Diffusion with Non-simultaneous Diffusion Processes [9.29387855908007]
  NeoDiff is a novel diffusion model that integrates the strengths of both discrete and continuous approaches.<n>Our approach unifies the theories of discrete and continuous diffusion models, offering a more principled and effective framework for text generation.
  arXiv  Detail & Related papers  (2025-05-28T09:28:52Z)
• Unifying Autoregressive and Diffusion-Based Sequence Generation [3.1853022872760186]
  We present significant extensions to diffusion-based sequence generation models, blurring the line with autoregressive language models.<n>We introduce hyperschedules, which assign distinct noise schedules to individual token positions.<n>Second, we propose two hybrid token-wise noising processes that interpolate between absorbing and uniform processes, enabling the model to fix past mistakes.
  arXiv  Detail & Related papers  (2025-04-08T20:32:10Z)
• Constrained Discrete Diffusion [61.81569616239755]
  This paper introduces Constrained Discrete Diffusion (CDD), a novel integration of differentiable constraint optimization within the diffusion process.<n>CDD directly imposes constraints into the discrete diffusion sampling process, resulting in a training-free and effective approach.
  arXiv  Detail & Related papers  (2025-03-12T19:48:12Z)
• Generalized Interpolating Discrete Diffusion [65.74168524007484]
  Masked diffusion is a popular choice due to its simplicity and effectiveness.<n>We generalize a new family of general interpolating discrete diffusion (GIDD) which offers greater flexibility in the design of the noising processes.<n>Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise, leading to improved sample quality.
  arXiv  Detail & Related papers  (2025-03-06T14:30:55Z)
• RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction [17.005198258689035]
  Diffusion Probabilistic Models (DPMs) have emerged as the de facto approach for high-fidelity image synthesis.<n>We introduce a novel generative framework, the Recurrent Diffusion Probabilistic Model (RDPM), which enhances the diffusion process through a recurrent token prediction mechanism.
  arXiv  Detail & Related papers  (2024-12-24T12:28:19Z)
• A Cheaper and Better Diffusion Language Model with Soft-Masked Noise [62.719656543880596]
  Masked-Diffuse LM is a novel diffusion model for language modeling, inspired by linguistic features in languages. Specifically, we design a linguistic-informed forward process which adds corruptions to the text through strategically soft-masking to better noise the textual data. We demonstrate that our Masked-Diffuse LM can achieve better generation quality than the state-of-the-art diffusion models with better efficiency.
  arXiv  Detail & Related papers  (2023-04-10T17:58:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.