NeurIPS 2026 Papers

A generative model is factored when each latent dimension independently controls one factor of variation: changing a single latent predictably changes one semantic attribute while leaving the rest unchanged, enabling controllable generation, compositional generalization, and reproducible representations. Existing approaches either constrain the latent distribution, e.g., requiring it to shift with an auxiliary variable, or regularize the model, e.g., sparsity, quantization, or Hessian penalties, neither of which matches how modern conditional generative models actually work: a conditioning signal u reshapes the generator g(·, u) while the latent prior stays fixed. We prove an identifiability theorem showing that generating mechanism diversity, the natural variation that arises when u sufficiently reshapes g, is sufficient for the model to be provably factored, with no parametric assumption on the latent distribution. To actively enforce this condition, we propose Mechanistic Contrastive Learning (MCL), a model-agnostic contrastive objective over generator Jacobians. Empirically, MCL achieves state-of-the-art latent concept disentanglement on three benchmarks equipped with a latent diffusion model, and improves prediction quality and zero-shot cross-task transfer in latent action world models with a 1.4B video generative model as the backbone.

We introduce a novel Mutual Information (MI) estimator that fundamentally reframes the discriminative approach. Instead of training a classifier to discriminate between joint and marginal distributions, we learn a normalizing flow that transforms one into the other. This technique produces a computationally efficient and precise MI estimate that scales well to high dimensions and across a wide range of ground-truth MI values.

Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal state, or developmental stage. These processes are often observed only as unpaired longitudinal snapshots: the same cells, neurons, or animals are not tracked as matched trajectories, even though population states are sampled across successive stages. This creates two coupled challenges. First, trajectories must respect curved low-dimensional manifolds embedded in high-dimensional biological measurements. Second, the model must identify when the transport mechanism itself changes. We introduce FLUX (FLow matching for Unpaired longitudinal data with miXture-of-experts), a geometry-aware longitudinal flow-matching framework for joint transport modeling and unsupervised regime discovery. FLUX learns a data-dependent metric from pooled labeled and unlabeled observations, uses that metric to construct geometry-aware conditional paths between adjacent marginals, and decomposes the resulting velocity field into sparse expert vector fields selected by a Straight-Through Gumbel-Softmax router. Across manifold controls, a regime-switching Lorenz system, widefield cortical calcium imaging during associative learning, and embryoid body single-cell differentiation, FLUX reconstructs longitudinal transport while recovering interpretable regime structure. In neural data, the router separates early/intermediate training from late training, coinciding with the behavioral divergence of CS+ and CS- lick indices. In embryoid body differentiation, FLUX separates expression-evolution regimes associated with pluripotent and more differentiated cell populations. Ablations show that mixture-of-experts routing alone is insufficient: FLUX without geometric learning can fit local transport but fails or weakens regime discovery when regimes are encoded in local dynamics. These results suggest that geometry-aware velocity decomposition provides a general strategy for discovering latent biological state transitions from unpaired longitudinal snapshots.

Generative models such as diffusion and flow matching have become dominant paradigms for visuomotor policy learning, yet their reliance on iterative denoising incurs high inference latency incompatible with real-time robotic control. We present a Fast Legendre-polynomial Action policy via Sparse History-anchored flow (FLASH), which replaces discrete action-chunk generation with continuous Legendre polynomial trajectory representation. Specifically, by fitting expert demonstrations under sparse temporal sampling, FLASH enables a single inference to cover a significantly extended action horizon. To further accelerate generation, FLASH initiates the flow matching process from history polynomial coefficients rather than uninformative Gaussian noise, shortening the transport distance and enabling accurate single-step inference. Moreover, analytic polynomial differentiation directly provides desired velocity feed-forward signals to the torque controller without numerical approximation. Extensive experiments on five simulated and two real-world manipulation tasks demonstrate that FLASH achieves state-of-the-art success rates ($\ge 92\%$ across all tasks), a per-episode inference time of $31.40\,ms$ (up to $175\times$ faster than diffusion policies and $18\times$ faster than prior flow matching policies), up to $4\times$ faster training convergence than ACT, and $5\times$ to $7\times$ reduction in controller tracking error compared to discrete-action baselines.

Light-field (LF) spatial super-resolution hinges on exploiting cross-view correspondences to recover high-frequency details while preserving geometric structure. However, existing methods often rely on implicit feature interaction or coarse disparity maps, which suffers noticeable performance degradation in regions with large disparities. In this paper, we propose Fine-Grained Epipolar Attention Diffusion (FEAD), which adopts epipolar attention as an explicit mechanism to characterize cross-view geometric correspondence along epipolar lines. To enable more accurate feature alignment and long-range spatial–angular interaction, we introduce a diffusion-based epipolar attention refinement strategy that progressively improves the initial attention. With the refined attention as guidance, cross-view features are explicitly warped and fused to enforce geometric consistency and aggregate complementary details across views for fine-grained reconstruction. Extensive experiments demonstrate that FEAD achieves state-of-the-art performance, with particularly strong gains in large-disparity scenarios.

Modeling resting-state functional magnetic resonance imaging (rs-fMRI) data is crucial for understanding brain-wide neural activity. However, traditional methods struggle to capture complex temporal dynamics over long horizons, to account for the brain's anatomical spatial structure, and to model high-dimensional ambient signals that lie on a low-dimensional intrinsic subspace. We propose FAST-Brain, a unified flow-aligned spatio-temporal surrogate brain model that addresses all three challenges. At its core is a flow-aligned generative framework that directly predicts the clean blood-oxygen-level-dependent (BOLD) signal, paired with a graph convolutional network that captures spatial structural constraints and a Transformer that models long-range temporal dependencies. Theoretically, we show that under a low-dimensional subspace assumption, the approximation error of our model scales with the intrinsic dimension rather than the ambient dimension, which justifies our direct modeling of the BOLD signal. Extensive experiments on synthetic and Human Connectome Project datasets demonstrate that FAST-Brain achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional signal subspace.

Diffusion Transformers (DiTs) have established themselves as the preeminent paradigm for high-fidelity generative modeling across various modalities, yet their practical deployment is hindered by the severe computational overhead of iterative sampling. While recent feature-caching methods mitigate this latency through feature reuse or forecasting based on integer-order operators, they remain limited in adapting to the non-stationary and highly curved dynamics of DiT latent trajectories. In this work, we reveal that DiT feature evolution exhibits anomalous diffusion characteristics and a certain degree of spatiotemporal heterogeneity, rendering static, integer-order forecasting operators inadequate and prone to error accumulation. To address this, we propose Fractional Anomalous Dynamics Extrapolation (FADE), a unified framework for training-free diffusion transformers acceleration. FADE models DiT feature evolution via anomalous diffusion and employs a Mittag--Leffler fractional operator to better adapt to high-curvature and non-linear latent trajectories of DiTs. Leveraging the empirical observation that these complex dynamics maintain cross-sample stability, we further construct a Look-Up Table with multiple dimensions through offline profiling. This enables zero-overhead, spatiotemporally adaptive parameter retrieval during online inference. Extensive evaluations across diverse architectures and modalities demonstrate that FADE achieves state-of-the-art acceleration, preserving exceptional generation fidelity under aggressive skip intervals with negligible latency overhead. Our code is anonymously available at https://anonymous.4open.science/status/FADE-8CB5.

This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views. This task is highly ambiguous due to intra-class variations, symmetries, and partial observations. Current solutions struggle to balance reliability and efficiency: deterministic methods are prone to error propagation from intermediate structures, while diffusion models suffer from high inference latency and complex post-hoc candidate extraction. To address this, we present F2G-Pose, a real-time direct, single-pass framework that predicts category-level pose, metric size, and camera-frame completed shape from a single RGB-D observation in one feed-forward pass. F2G-Pose lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and integrates object-level structure with a Mixture-of-Experts Transformer. Joint size prediction and camera-frame shape completion encourage metric consistency through dense geometric supervision. Trained only on synthetic SOPE data, F2G-Pose achieves state-of-the-art accuracy on SOPE and ROPE while running at 28 FPS, and further demonstrates strong camera-frame geometry on HouseCat6D and real-object transfer on HANDAL. Analyses of latency, robustness, stability, shape completion, and GenPose++ candidate behavior further demonstrate the efficiency and reliability of F2G-Pose, with real-world manipulation experiments demonstrating deployment potential under segmented RGB-D inputs.

Extremely sparse-view computed tomography (xSV-CT) is critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections. In many such scenarios, the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable. This exposes a gap in current sparse-view approaches: Optimization-based methods, which use the sparse measurements directly but lack data-driven priors to suppress artifacts, and learning-based methods, which use strong priors but usually require densely scanned training volumes acquired outside the xSV-CT regime. We study this train-sparse / infer-sparse setting by learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency for tomographic reconstruction. On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.

Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive models. However, diffusion language models (DLMs) can denoise masked tokens at arbitrary positions. Thus, prefix-only probing reveals only one facet of memorization in DLMs and significantly underestimates the risk of training-data extraction. In order to realistically model extractability of training data in DLMs, we introduce infilling extraction, a data-extraction protocol parameterized by an arbitrary binary mask that subsumes prefix-only probing and accounts for the bidirectional inductive bias of DLMs. Instantiating it on LLaDA-8B and Dream-7B across five extraction modes, three training pipelines, and three corpora covering verbatim and partial leakage, we find that mask geometry governs extractability: edge-conditioned masks extract up to three times more verbatim sequences than prefix-conditioned ones, and bidirectional access opens channels inaccessible in autoregressive models. In particular, we show that a realistic adversary with access to training data where personally identifiable information has been redacted, can even achieve higher recall on extracting redacted email addresses from DLMs than from scale-matched autoregressive models. Tunable parameters for decoding measurably affect extraction performance, while a follow-up supervised finetuning stage does not eliminate the prior memorization.

Visual Autoregressive (VAR) models have emerged as a strong alternative to diffusion for image synthesis, yet their fixed training resolution prevents direct generation at higher resolutions. Naively transferring training-free extrapolation methods from LLMs or diffusion models to VAR yields three characteristic failure modes: global repetition, local repetition, and detail degradation. We trace them to a unified band-stage mismatch: VAR generates images in a coarse-to-fine, scale-wise process where each stage is driven by a distinct dominant RoPE frequency band, and each failure mode emerges when the dominant band of a particular stage is disrupted. Building on this insight, we propose Stage-Aware RoPE Remapping, a training-free strategy that assigns each frequency band a stage-specific remapping rule, jointly suppressing all three failure modes. We further observe that attention becomes systematically dispersed as the image resolution increases. Existing methods typically depend on predefined attention scaling factors, which are neither adaptive to the target resolution nor capable of faithfully capturing the actual extent of attention dispersion. We therefore propose Entropy-Driven Adaptive Attention Calibration, which quantifies dispersion via a resolution-invariant normalized entropy and yields a closed-form per-head scaling factor that realigns the extrapolated-resolution attention entropy with its training-resolution counterpart. Extensive experiments show that our method consistently outperforms prior resolution-extrapolation methods in both structural coherence and fine-detail fidelity. Our codes have been released in supplementary materials and will be released on Github.

Multimodal learning has advanced visual understanding through powerful multimodal LLMs (MLLMs). In visual generation, however, these models are often used only as global text or context encoders for diffusion generators, limiting their ability to provide structured spatial and temporal guidance. This creates an interface gap: MLLMs can parse complex layouts, attributes, and knowledge-intensive scenes, yet current generation pipelines often struggle to transfer such understanding into images and videos with precise, controllable structure. We propose MetaCanvas, a lightweight framework that organizes MLLM representations into spatially indexed canvas tokens and interfaces them with diffusion generators through patch-wise residual fusion. This provides structured spatial and spatiotemporal conditioning rather than relying only on a single global embedding or 1D query sequence. We implement MetaCanvas on three diffusion backbones and evaluate it across six generation and editing tasks requiring precise layouts, robust attribute binding, and fine-grained multimodal control. Under matched settings, MetaCanvas consistently improves over global-conditioning baselines, narrows the gap to specialized closed-source visual generation and editing systems, and unlocks new editing and in-context capabilities for video diffusion backbones. These results suggest that spatially aligned MLLM-derived canvas tokens provide a promising latent interface for transferring multimodal understanding into diffusion-based generation.

As text-to-image diffusion models (T2I DMs) become widely deployed, adversarial advertising has emerged as a realistic threat: an attacker may compromise a T2I DM so that it implants target product brands into images generated from users' non-advertising prompts. Two key challenges remain largely unresolved in this setting: achieving natural and semantically coherent adversarial advertisement and ensuring robust adversarial advertisement. To address these challenges, we develop a new estimation algorithm for the multivariate continuously scaled phase-type with Lévy (MCPHL) distribution to capture the intrinsic distribution of natural advertisement prompts in the prompt-embedding space. With the estimated MCPHL, we construct an attack that pushes non-advertising prompts toward high-density regions of this distribution, making the resulting perturbed prompts better aligned with natural advertising prompts. We then propose a masked parameter smoothing approach grounded in mollification theory, yielding a smoothed T2I DM equipped with a dimension-invariant certified guarantee against advertisement degradation under model fine-tuning. The masking mechanism preserves utility by avoiding unnecessary smoothing on sensitive parameters, and our theoretical analysis shows that the smoothed model better preserves adversarial advertisements under fine-tuning, while maintaining better generation quality.

Solving optimal transport (OT) on random minibatches is a common surrogate for exact OT in large-scale learning. In flow matching (FM), this surrogate is used to obtain OT-like couplings that can straighten probability paths and reduce numerical integration cost. Yet, the population-level coupling induced by repeated minibatch OT remains only partially understood. We formalize this coupling as the expected batch OT plan $\overline{\pi}\_{k}$, obtained by averaging empirical OT plans over independent minibatches of size $k$. We then establish its large-batch consistency and, in the semidiscrete case relevant to generative modeling, derive rates for both the transport-cost bias and the convergence of $\overline{\pi}\_{k}$ to the OT plan. For FM, this yields a population coupling whose induced velocity field is regular enough to define a unique flow from the source to the discrete target. We finally quantify how OT batch size interacts with numerical integration in a tractable two-atom model and in synthetic and image experiments.

Flow maps have enabled remarkable progress in few-step generative modeling across both continuous and discrete state spaces. Despite their promise, existing parameterizations are restricted to flows over fixed dimensions or fixed sequence lengths. Here, we introduce **Expanding Generative Flows** (EFlows), which define flows between distributions of *increasing dimensionality* through an expanding interpolant built from *augmented distributions of conditional noise*. Building on this framework, we propose **Expanding Flow Maps** (EFMs), a new class of flow maps that distill EFlows into efficient few-step generative models. Each EFM factors the map between any two timesteps into two learned components: an *expand operator*, which augments the state with new coordinates or tokens, and a *transport operator*, which pushes the expanded state forward along the interpolant. Composing these operators yields a single map that jointly *expands* and *denoises* the state, recovering existing fixed-canvas flow maps as the special case in which the expand operator is the identity. We further extend the framework to the discrete simplex, enabling variable-length sequence generation via token insertions. Across domains, EFlows and EFMs provide a principled approach to generative problems in which output size is itself learned, controllable degree of freedom.

Text-to-image (T2I) diffusion models often synthesize policy-violating content, necessitating robust safeguards and rigorous evaluation frameworks. However, current approaches remain largely monolithic in both execution and evaluation, limited to coarse-grained categorization within broad unsafe concepts. Such approaches fail to account for the specific constituent factors of a violation, resulting in weak learning signals and allowing model collapse to bypass safety checks. To overcome these limitations, we propose Example-Based Spatial Guidance (EBSG), a training-free method that utilizes user-editable exemplar packs to provide granular spatial guidance for precise concept steering. Unlike existing methods that coarsely steer the entire image away from a broad concept, EBSG decomposes these categories into specific sub-concepts via exemplar text-image pairs, providing explicit, localized steering signals for more robust safety control. Furthermore, we introduce a vision language model (VLM)-based evaluation protocol that provides a fine-grained assessment, avoiding the pitfall of conventional binary evaluators that permit model collapse to bypass safety checks. Empirically, EBSG achieves state-of-the-art erasure performance across diverse safety datasets and concept categories, spanning four nudity benchmarks, seven I2P harmful-concept slices, and four MJA metaphor categories, while supporting multi-concept removal and transferring to a modern backbone, SD3.

Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption. To use this prior to solve a linear inverse problem, one needs to sample from the posterior, but the score that the prior provides is the unconditional score, not the posterior score. Existing methods either steer a fixed pretrained denoiser with approximate measurement-matching corrections, or train a conditional restoration model that abandons the denoising structure of the prior. We derive the exact posterior score in closed form for linear Gaussian inverse problems under general Gaussian interpolants, and show that posterior sampling reduces to a denoising problem at an operator-dependent shifted pivot under an anisotropic noise covariance. We turn this identity into **Exact Posterior Score (EPS)**, a denoising training objective that preserves the input/output structure of standard pretraining and can therefore be trained from scratch or fine-tuned from a pretrained denoiser. At inference, EPS uses the same sampler as the underlying backbone, with no likelihood gradients or projections. We evaluate EPS on five linear inverse problems across FFHQ and ImageNet, where it outperforms training-free and training-based baselines on fidelity, perceptual, and distributional metrics, while using roughly an order of magnitude fewer denoiser evaluations than gradient-based posterior samplers.

Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture in high-fidelity generative modeling, but their massive computational demands and multi-step inference process limit their deployment in practical scenarios. Post-training quantization (PTQ) offers a promising solution for accelerating inference, but DiTs suffer from severe activation outlier issues, which can lead to catastrophic accuracy loss in integer formats. Although some recent outlier mitigation methods employ orthogonal transformations or diagonal scaling to smooth the activation distribution, they either solve the transformation matrix solely for activations or fail to fully balance the inter-channel variance between activations and weights. Additionally, the construction of the calibration set by taking the weighted average of the activation values for each time step introduces spurious temporal correlations. To address these issues, we propose a joint diagonalization of the activation-weight covariance matrix based on the solution of a generalized eigenvalue problem (GEVP). We also transform the feature fusion of the calibration set's temporal dimension into probabilistic sampling and assembly in the spatial dimension using Uniform Token Splicing (UTS). Specifically, compared to suboptimal baseline methods, our approach reduces the FID by 16.06 and 6.49 on both the W3A4-quantized DiT-XL/2 and PixArt-$\Sigma$ models, respectively. This demonstrates that our method successfully suppresses outliers in activations and weights, maintaining DiT's performance under low-bit quantization conditions. Code is available at the anonymous repository \url{https://anonymous.4open.science/r/JDUS-DiT-74E1}.

Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce **SKILD**, a **S**cale-invariant **K**-Space **I**mage **L**earning **D**iffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep: *no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor*. Empirically, SKILD reaches FID $2.65$ and Inception Score $9.63$ on unconditional CIFAR-10, performs $2\times$–$8\times$ super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.