NeurIPS 2026 论文列表

Discrete diffusion models are increasingly competitive for language modeling, yet it remains unclear how their denoising objectives organize learning. Although these objectives target the full data distribution, we show that the exact reverse process induces a hierarchy between coarse *support* information and finer *frequency* information. For uniform and absorbing (a.k.a. masking) diffusion, we prove that, in the small-noise regime of the final denoising steps, each single-token reverse edit decomposes into a leading scale, determined by whether it moves toward the data *support* (e.g., grammatically valid sentences), and a finer coefficient, determining *relative probabilities* within the same scale. Thus, recovering validity structure only requires learning the correct order of magnitude of reverse probabilities, whereas recovering data frequencies requires coefficient-level estimation. The separation is mechanism-dependent: uniform diffusion exhibits a trichotomy into validity-improving, validity-preserving, and validity-worsening edits, while absorbing diffusion places its leading-order mass on validity-improving moves. Experiments on a masked language diffusion model and synthetic regular-language tasks support these predictions: support-localization emerges earlier than within-support frequency ranking, and the contrast between uniform and absorbing diffusion matches the predicted rate separation. Together, our results suggest that discrete diffusion models learn data support before data frequencies.

Diffusion-based style transfer faces two fundamental questions: which regions should prioritize structure preservation and which are more amenable to texture transfer, namely the “where” question, and how to achieve such region-aware stylization without corrupting content, namely the “how” question. Existing methods typically rely on a single mechanism to control local structure, local texture, and global style together, making them fragile in region-rich scenes: uniform stylization strength may distort salient objects, while conservative control leaves texture-dominant regions under-stylized. We propose StyleRoute, a region-aware diffusion stylization framework that explicitly separates where style should be transferred from how the transfer should be optimized. For the where question, StyleRoute infers a region-level routing variable from structure confidence, texture confidence, and route disagreement, deciding per region whether to protect structure or to inject texture. For the how question, StyleRoute coordinates the regional routing with diffusion optimization to guide global style evolution, suppress style-content conflicts, and stabilize coarse layout. Together, StyleRoute transforms the implicit style-content trade-off into an explicit, interpretable, and region-aware transfer process. Experiments on diverse content-style pairs show consistent improvements over prior diffusion-based and region-matching stylization methods. Code will be released.

In joint multiuser decoding, a receiver recovers a set of messages from a single noisy aggregate of many simultaneous transmissions. Classical decoders rely on rule-based mechanisms such as successive interference cancellation, joint belief propagation, or list recovery, all of which become brittle or expensive as ambiguity increases. We propose CIDER, a learned multiuser decoder with masked-diffusion refinement steps. CIDER uses demixing to prevent duplicate-row collapse and uses parity-aware propagation to provide soft guidance from the code constraints. In higher-load regimes, we further improve reliability via a lightweight quality-guided remasking step that selectively re-decodes low-confidence sequences. On commonly used error correcting codes, CIDER matches or improves on FFT-accelerated joint belief propagation-style decoding in symbol error rate while running more than $6\times$ to over $100\times$ faster, with the speedup widening as the blocklength grows. Code is available at https://anonymous.4open.science/r/CIDER_2026/.

Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent structure. Latent-variable models, by contrast, capture structure but often sacrifice generative quality. We bridge this gap by proposing Structured Coupling for Flow Matching (SCFM), a cooperative framework that augments flow matching with structured latent representation learning. By introducing structured latent variables and exogenous noise into the source, SCFM jointly learns a structured prior (via latent variable modeling) and a continuous transport map (via flow matching). It uses a shared time-dependent recognition network for both latent variable model variational inference and intermediate-time flow velocity estimation. This yields a structurally informed yet unconditional, simulation-free flow model, where the latent variable model can also assist flow sampling. Empirically, SCFM facilitates unsupervised latent representation learning for clustering, disentanglement and downstream tasks, while remaining competitive with flow matching in sample quality, showing that meaningful structure can be learned without sacrificing generative fidelity.

Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such as flipping or rotation. Recent work has achieved visual anagram synthesis by leveraging pretrained text-to-image (T2I) diffusion models, yet still suffers from several key limitations including computational inefficiency, suboptimal aesthetic quality, and weak semantic fidelity and expressiveness. This work focuses on generating visual anagrams with substantially improved visual quality at minimal computational cost, thereby advancing intelligent creation of illusionary digital art. To increase image resolution while reducing time overhead, we adapt the cutting-edge parallel denoising algorithm from pixel-based T2I model to the adversarially distilled latent-based one, and accordingly propose a structure-semantic co-optimization (S2CO) framework to counteract the consequent visual degradation. As the core of our approach, S2CO framework comprises three key innovations: (\romannumeral1) null-text structure alignment optimization; (\romannumeral2) semantic enhancement optimization; (\romannumeral3) attention-guided noise fusion. Building upon these components, our method dubbed \textbf{S2CO-Anagram} is able to generate higher-resolution anagram images with noticeably superior visual harmony and semantic faithfulness than related SOTA approaches, all while achieving substantially faster inference speed. Code will be publicly available.

Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoencoder (VAE) suffer from information loss, limited detail, and the inability to support end-to-end training. In contrast, models operating directly in the pixel space incur prohibitive computational cost. Although cascade models can mitigate computational cost, stage-wise separation prevents effective end-to-end optimization, hampers knowledge sharing, and often results in inaccurate distribution learning within each stage. To address these challenges, we introduce a unified multi-stage generative framework formulated under the \emph{stochastic interpolant} formalism with our \textbf{Conditional Dependent Coupling} strategy as a Flow-Matching variant. It decomposes the generative process into interpolant trajectories at multiple stages, ensuring accurate distribution learning while enabling end-to-end optimization. Importantly, the entire process is modeled as a single unified Diffusion Transformer, eliminating the need for disjoint modules and also enabling knowledge sharing. Under explicitly stated assumptions, we provably reduce both transport cost and asymptotic inference time, we empirically validate the underlying NFE--transport-cost and per-evaluation-cost relations. Extensive experiments demonstrate that our method achieves competitive fidelity and substantially lower wall-clock cost in the low-NFE regime across multiple resolutions.

This work introduces a family of generative diffusion models based on the stochastic heat equation. The deterministic heat equation has natural connections with kernels over discrete structures and has been successfully applied in machine learning to discriminative tasks for various types of data such as graphs. We extend it to a stochastic version by formulating our diffusion as a Gaussian process whose transition kernel can be written in closed form for both the forward- and reverse-time processes. Our diffusion process propagates noise smoothly among neighbours under the guidance of a Laplacian matrix that describes the global structure of the input. Following a flow matching-based approach, we train a denoising neural network that takes noisy representations as input and gradually removes noise to generate clean samples. We experimentally show that, by exploiting the Laplacian during training, our decoder is able to recover the global structure of the input.

For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challenging because the reward is defined for clean output images, but the alignment procedure requires value function estimates at noisy intermediate latents. Existing methods resort to Tweedie-style or Monte Carlo approximations, trading off estimator bias against computational cost: Tweedie estimates are efficient but biased, while Monte Carlo estimates are more accurate but require expensive rollouts. A natural alternative would be a learned value function, but it remains an open question how to effectively train a strong and general value model specifically for noisy latents. Here, we propose StitchVM (Stitched Value Model), a model stitching framework that efficiently transfers reward models pretrained for clean images to the noisy latent regime. StitchVM starts from an existing, truncated pixel-space reward model and attaches a frozen diffusion backbone to it as its head. From the pixel-space model, the resulting hybrid retains a carefully pretrained, robust reward capability; from the diffusion backbone, it inherits its native ability to handle noisy latents. The stitching procedure is exceptionally lightweight, e.g., stitching and finetuning CLIP ViT-L and SD 3.5 Medium takes only $\approx$10 GPU-hours. By lifting powerful pixel-space reward models to latent space, StitchVM opens up a new style of diffusion alignment: instead of rough, yet costly per-sample approximation of the value function, the correct function for the actual, noisy latents is constructed once and then amortized over many samples and iterations. We show that this approach yields improvements across a broad range of downstream steering and post-training methods: DPS becomes $3.2\times$ faster while halving peak GPU memory, and DiffusionNFT becomes $2.3\times$ faster.

Text-guided image editing with diffusion models struggles with spatial edits (e.g. manipulating objects, changing viewpoints, manipulating occlusions) that depend on the 3D structure understanding of a scene. Marigold and follow-up work demonstrate that a pretrained diffusion backbone already harbors strong geometric priors that can be activated for depth estimation, yet how to redirect these priors toward editing remains unresolved. Through per-layer gradient probing, we discover that the editing and depth objectives are compatible in early transformer layers but actively conflict in late layers, explaining why naive depth-conditioned editing either ignores geometry or degrades visual quality. We present StitchEdit, which first activates the backbone's geometric capability via a lightweight depth-denoising adapter, then selectively fine-tunes this adapter for editing using a probe-guided per-layer learning rate schedule that preserves depth knowledge where it helps and prunes it where it conflicts. On the proposed StitchEditBench, a benchmark of 150 geometry-dependent edits spanning spatial transformation, occlusion manipulation, and viewpoint change, StitchEdit consistently surpasses both instruction-based and depth-conditioned baselines on perceptual quality and geometric fidelity.

We introduce Sticky Jump Diffusions (SJDs), continuous-time Markov processes on $\mathbb R^d$ with a discrete anchor set identified with token embeddings. In forward time, anchors release their mass at a hazard rate and the released mass diffuses in the continuous ambient space; time reversal couples a score-driven SDE with a sticky jump kernel whose rate and destination are fixed by flux balance with the forward law. We estimate the score and the per-anchor reverse hazards from a single denoising classifier via Denoising Hazard Matching, the hazard analogue of denoising score matching, with simulation-free cross-entropy training. SJD generalizes the classical sticky-boundary diffusions of Feller and Itô to vocabulary-sized anchor sets, and recovers masked diffusion, continuous diffusion, and hybrid diffusion as limits. Beyond these limits, the framework exposes two design axes that previous work hold fixed: the un-sticking kernel, which encodes both per-anchor geometry and cross-position structure of the corruption, and the per-anchor forward hazard, which encodes the commitment schedule. We evaluate SJD on CIFAR-10, ImageNet $64{\times}64$, Text8, and Sudoku, where it is competitive with discrete, continuous, and hybrid baselines.

Diffusion large language models (dLLMs) generate tokens in parallel via iterative denoising, but their bidirectional attention prevents the KV caching used by autoregressive models and recomputes the full attention map at every step, incurring quadratic per-step cost. Existing sparse attention methods for dLLMs compute a sparse mask once at the first denoising step and reuse it throughout, while allocating the sparsity budget uniformly across heads. We argue both choices ignore key structural properties of attention in dLLMs: empirically, attention patterns are only locally consistent across steps and shift abruptly at certain transitions, and different heads concentrate their attention mass to very different extents. To this end, we design Step-dLLM, which periodically refreshes a block-sparse mask at anchor steps along the denoising trajectory and reuses it within each locally consistent interval, while a head-adaptive global budget assigns more blocks to heads with more concentrated attention. Backed by a fused IO-aware Triton kernel for block-sparse attention, Step-dLLM applies to pre-trained LLaDA-1.5 and Dream-7B-Instruct without retraining, achieving a $4.2\times$ kernel-level speedup over dense FlashAttention at 16K context with 90% sparsity (up to $8.5\times$ at 95%), and achieves better model accuracy and lower latency than prior methods.

We propose a nonparametric, kernel-based approach to generative modeling based on Stein operators and particle transport. Our method constructs a continuous-time transport from a simple reference distribution (typically, a standard Gaussian) to a target distribution using a Stein formulation of the continuity equation. Unlike alternative Stein transport methods, which require access to the target density or its gradient, we focus on the sample-only setting and introduce a tractable surrogate objective based on an Ornstein--Uhlenbeck (OU) probability path. This construction allows the velocity field driving the transport to be learned using only samples from the target distribution and Gaussian noise. By restricting the velocity field to a reproducing kernel Hilbert space, we obtain closed-form solutions via kernel ridge regression at each time step. The resulting method is particularly well suited to small data regimes, where heavily parameterised neural generative models may be difficult to train or overfit. We demonstrate the usefulness of the approach for sample-based generative modeling in the small data regime (generative data augmentation), and for posterior emulation in Bayesian inverse problems, where expensive Monte Carlo samplers produce limited sets of posterior samples that must be efficiently augmented.

Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain, video text editing remains largely unexplored: it is a localized task demanding stroke-level precision within small text regions, which compounds the challenges of cross-frame accuracy, temporal coherence, and stylistic fidelity. We introduce SteerVTE, a unified framework that \underline{\textbf{steer}}s a frozen video diffusion model to perform precise \underline{\textbf{V}}ideo \underline{\textbf{T}}ext \underline{\textbf{E}}diting through style and glyph control. Built on a frozen diffusion transformer, SteerVTE attaches a lightweight text context adapter with two complementary modules: a style encoder capturing the original text's visual attributes, and dual-granularity glyph encoders encoding the target text at both the line and character levels. To overcome the inherently weak text rendering priors of video foundation models, we further propose a glyph-aware spatial-focal loss and a three-stage progressive training curriculum that scales from image to video data. To support large-scale training, we also develop an automatic synthesis pipeline and construct SteerVTE-1M, a dataset of one million triplets spanning diverse scenes, fonts, and stylistic effects. Extensive experiments demonstrate that SteerVTE substantially outperforms existing video editing baselines across text accuracy, style consistency, and temporal coherence. Our anonymous project website is available at \url{https://steervte-paper.github.io}.

Diffusion models steer conditional generation with a tunable guidance scale, which users routinely adjust to trade off prompt alignment and diversity. However, these models often reflect and amplify social biases, whose mitigation has been a long-standing concern. Current debiasing techniques are often optimized for a single guidance scale, leaving them vulnerable to fairness degradation when that scale is adjusted. We trace this behavior to a previously overlooked source by decomposing total bias into two components: a model bias and a guidance bias. While prior work primarily targets the former, we show that the guidance bias grows monotonically with the guidance scale, eventually dominating the high-guidance regimes users prefer. To address this, we extend Strong Demographic Parity to guidance and derive a condition under which the target guided distribution retains its group ratio across guidance scales. We propose StayFair, which leverages this condition to design fair guidance algorithms in both regimes. For classifier guidance, it equalizes the classifier's output distributions across groups; for classifier-free guidance, it shifts the null embedding by a prompt-dependent offset. Since StayFair modifies only guidance, it is orthogonal to model debiasing and can be layered onto existing fair diffusion models to extend their fairness across guidance scales. Across class-conditional and text-to-image generation, StayFair decouples fairness from the guidance scale without sacrificing image quality.

Flow matching learns continuous-time transports from a simple source distribution to a target data distribution by training a velocity field on intermediate states along interpolating trajectories. However, because the model observes only the projected data-space state $(x_t,t)$, different transport trajectories can induce conflicting velocity directions at nearby intermediate states, leading to curved transport paths that limit the fidelity of low-NFE sampling. To address this limitation, we propose \emph{State Augmented Flows} (SAF), a lightweight framework that lifts flow matching into a transient augmented state space. SAF transports an augmented state $(x_t,z_t)$, where auxiliary coordinates provide additional contextual information during transport while vanishing at the terminal time. This enlarges the state observed by the velocity model without changing the final sample space, and is compatible with existing coupling strategies and flow-matching architectures. Across MNIST, CIFAR-10, and ImageNet-32, SAF consistently improves over the rectified-flow baseline, with the largest gains in the low-NFE regime. These results suggest that augmenting the transported state provides a new and complementary mechanism for improving flow-based generative modeling.

Immunohistochemical (IHC) staining encodes molecular protein expression critical for clinical diagnosis, yet its chemical procedures are costly and time-consuming. Virtual staining offers a compelling alternative by digitally synthesizing IHC images from hematoxylin-eosin (H\&E) stained slides. Despite remarkable progress in diffusion- and flow-matching-based virtual staining, supervised fine-tuning (SFT) remains fundamentally limited in pathological fidelity due to its coarse, spatially averaged supervision signal that starves sparse DAB-positive regions of effective gradients. Reinforcement learning (RL) can exploit inter-rollout variance for outcome-level pathological supervision, but naively applying DAB-based rewards triggers severe reward hacking that collapses the image distribution and degrades perceptual quality. This paper presents StainNFT, a flow matching RL post-training framework built on a curriculum reward strategy that gates fine-grained optical density supervision on per-sample DAB mask IoU. To enhance fine-grained pathological fidelity, we introduce expression-aware reweighting, multi-scale block supervision, and a closed-loop implicit pathological semantic reward derived from pathology foundation model priors. Extensive experiments on seven benchmarks demonstrate that StainNFT consistently outperforms existing methods in both perceptual quality and pathological fidelity for H\&E-to-IHC virtual staining. Ablation studies confirm the effectiveness of each proposed component. Our code and trained models will be released.

Current diffusion models for audio-driven avatar video generation struggle to synthesize long videos with natural audio synchronization and identity consistency. This paper presents StableAvatar, the first end-to-end video diffusion transformer that synthesizes ultra-long videos. We observe that the primary reason preventing existing models from generating long videos is their audio modeling, which typically relies on third-party off-the-shelf audio embeddings that are introduced into the diffusion model via cross-attention. Since current diffusion backbones lack any audio-related priors, they cannot correctly couple audio cues with latent dynamics, causing systematic latent distribution deviations at each segment. These deviations accumulate across segments, gradually shifting the latent trajectory and producing noticeable quality drift. To address this, StableAvatar introduces a novel Timestep-aware Audio Adapter that prevents error accumulation via timestep-aware modulation. During inference, we propose a novel Audio Native Guidance Mechanism to further enhance the audio synchronization by leveraging the diffusion’s joint audio-latent prediction as a dynamic guidance signal. To enhance the smoothness of the ultra-long videos, we introduce a Dynamic Weighted Sliding-window Strategy that fuses latent over time. Experiments on benchmarks show the effectiveness of StableAvatar both qualitatively and quantitatively.

The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic data is inevitably incorporated into the training pipelines of future model generations, forming a *self-consuming training loop*. Prior work has studied the effects of such recursive self-consuming training, but analyses have largely been limited to *isolated* models, where a model consumes only its own synthetic data, or to simplified interactions between two models. This paper takes a first step toward understanding *networked* self-consuming generative models, in which multiple models consume synthetic data generated by one another through complex interaction pathways. We introduce a theoretical framework representing models as nodes in a directed, weighted graph, with edge weights governing the flow of synthetic data among models. Using this framework, we analyze the long-term behavior of networked models under retraining dynamics, establishing conditions for convergence and characterizing the resulting fixed points. We further investigate how the system's long-term stability and diversity are shaped by each model’s access to real data, cross-model data consumption, and the structure of the interaction graph.

Latent action models (LAMs) aim to learn compact representations of state transitions directly from visual observations, but they suffer from a fundamental ambiguity: the latent code can encode target-state information rather than true dynamics. Existing approaches address this issue through restrictive bottlenecks, which reduce leakage at the cost of limiting expressivity. We propose StaDy, a regularization framework that instead enforces conditional informativeness: the latent code should aid prediction only when paired with the source state, while remaining uninformative about the target on its own. Concretely, we introduce a likelihood-matching objective that aligns the decoder’s predictions conditioned solely on the latent variable with its unconditional predictions. This discourages target memorization without constraining latent capacity. Experiments in video modeling show that, unlike bottleneck-based methods, StaDy scales effectively with increased latent dimensionality, reducing static content leakage while improving the representation of more complex dynamics.