NeurIPS 2026 论文列表

Compositional prompts in text-to-molecule generation often specify several structural constraints at once, such as scaffolds, functional groups, substituents, and finer modifiers. Existing molecular latent diffusion models typically inject the full prompt once and apply it uniformly throughout denoising, which can cause later constraints to overwrite or distort earlier structural commitments. We propose **Chain-of-Generation (COG)**, a training-free progressive latent diffusion strategy for text-guided molecular design. Technically, CoG realizes progressive generation as multi-stage latent denoising over cumulative molecular sub-prompts: it factorizes a prompt into chemistry-informed coarse-to-fine segments, conditions each stage on the cumulative sub-prompt, and continues denoising from the previous-stage latent using a fixed partial-denoising schedule. This turns one-shot conditional generation into a sequence of stage-wise denoising steps that help retain earlier structure while introducing later constraints. Across benchmark, out-of-distribution, and ablation settings, CoG improves structural prompt fidelity over one-shot conditioning on the tested latent graph-diffusion backbones.

Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not representative of the underlying populations. Due to its practical importance, related works have been proposed to address the problem under local [Versteeg et al., 2022] or interventional settings [Dai et al., 2025a], but general algorithms for observational data remain elusive. In this paper, we focus on truncated data (hard selection), and provide a general result for Additive Noise Models (ANM). We show that score-based methods are a surprisingly suitable candidate due to a special property of the score function ($\nabla \log p(x)$): *deterministic truncation preserves both the score* of the joint distribution as well as the Jacobian of the score. While established score-matching algorithms still fail in most settings, we actively leverage this observation to propose a new method for causal discovery that is robust under truncated data with ANMs. Overall, our approach offers a general, robust solution that is agnostic to external information about the selection process and still achieves comparable performance to the state-of-the-art approaches when selection is not present.

Generative models can produce individually plausible samples while deviating substantially from a reference set in the distribution of key features. For example, a model pretrained on broad drug-like chemical space may generate molecules whose molecular features differ from those of a therapeutic class of interest, such as known antibiotics. Correcting such distributional _miscalibration_ is challenging: direct finetuning on the reference set can overfit and does not control which features are matched. To fill this gap, we introduce kernel Calibrating Generative Models (kCGM). kCGM minimizes a maximum mean discrepancy (MMD) between generated and reference feature distributions using an unbiased score-function estimator, with KL regularization to remain close to the pretrained model. On a reference set of 174 antibiotics, direct finetuning sacrifices chemical validity for feature-distribution matching, whereas kCGM improves target feature matching while increasing validity. We further demonstrate kCGM in protein and DNA generation tasks, showing it can adapt autoregressive, continuous-space diffusion, and discrete diffusion models using only feature-level supervision.

Although recent training-free acceleration methods have achieved promising speedups for Diffusion Transformers, most rely on historical features, either by directly reusing cached features or by forecasting future features from them. Such a history-to-future design can accumulate errors when the prediction horizon becomes long. To address this issue, we propose FuCa, a Cache-the-Future training-free acceleration framework. Instead of extrapolating future information solely from the past, FuCa caches sparsely computed future features and uses them as online revision signals. FuCa revises the latent-state update and the corresponding velocity, and then recovers skipped timesteps through lightweight interpolation. It requires no extra training or model-specific auxiliary modules, while keeping the number of DiT forward evaluations low. Experiments on FLUX.1-dev, Qwen-Image, Wan2.1-1.3B, and Hunyuan Video show that FuCa achieves up to about 5$\times$ speedup for image generation and more than 4.3$\times$ speedup for video generation, with higher reconstruction quality than existing training-free baselines.

We introduce Contrast Sensitive Flow (CSFlow), a weighting scheme that connects the human eye's Contrast Sensitivity Function (CSF) to the iterative denoising steps of flow matching. Because real-world images concentrate signal at low spatial frequencies, these components reach high signal-to-noise ratio earlier during continuous diffusion than high-frequency components. When generating images with diffusion or flow matching models, this induces a soft autoregressive structure in Fourier space, where coarse image content stabilizes before fine detail. Meanwhile, the human visual system is unequally sensitive to spatial frequencies: very low and very high frequencies require significantly higher contrast to be perceived. We for the first time merge these observations through two contributions: (1) a metric that estimates which frequencies are generated at each reverse flow interval and (2) timestep weights obtained by aligning the frequencies generated at each noise level with human contrast sensitivity. We validate our contributions experimentally showing that these weights can improve generative performance by lowering FID by 4.7%, increasing Inception Score by 2.2% and improving GenEval scores by 2.5% using inference-only timestep modification or short fine-tuning. Qualitatively, we find that our CSFlow weights lead to better visual realism and less cartoonish appearance of generated images.

Finite-shot quantum tomography often leaves a posterior over many density matrices compatible with the same measurement record, but fast reconstruction methods collapse this uncertainty to a single state. We frame this setting as amortized posterior sampling and introduce BuresTomFlow, a measurement-conditioned Flow Matching sampler for full-rank density matrices. The method transports base density matrices along Bures-Wasserstein paths on the positive matrix cone, projects the path back to the trace-one state space, and trains the velocity field with a Bures-aligned tangent loss. Thus the sampler is trained in the same fidelity-based geometry used to judge posterior quality. We evaluate BuresTomFlow in dense four-qubit simulations with count, shadow, and mixed measurement records. Against Cholesky, log-density, and generic Riemannian Flow Matching controls at comparable learned-sampler runtime, BuresTomFlow improves Bures distance, credible-ball coverage, and held-out observable prediction. In the main comparison across multiple seeds, mean Bures distance decreases from $0.618$ for the Cholesky baseline to $0.367$ for BuresTomFlow, and mean coverage error decreases from $0.203$ to $0.100$. These results show that the value of Bures geometry is not merely enforcing physical states, but producing calibrated posterior uncertainty when finite-record tomography cannot be reduced to one reconstruction.

Synthetic time series generation is critical for data augmentation, privacy-preserving sharing, and simulation. Autoregressive models extend to arbitrary horizons but suffer compounding errors, while diffusion models achieve high fidelity through bidirectional refinement only at fixed lengths. We propose **Horizon Diffusion**, a unified framework that resolves this tension by decomposing generation into variable-length *horizon blocks* produced autoregressively across blocks yet jointly denoised within each block. Two innovations make this hybrid practical: *Horizon-Causal Attention*, a structured mask that simultaneously preserves cross-block causality and within-block bidirectional refinement, and the *Block Size Curriculum*, a deterministic Warmup--Stable--Decay schedule that fluidly transitions training from full-sequence diffusion to block-wise autoregressive generation. Our method consistently outperforms strong baselines on fidelity, diversity, and downstream utility, and remains stable when sequence length scales by $4\times$ beyond training from a single trained model. Code is available at Supplementary Material.

Continuous-time generative models compress endpoint-conditioned bridges into Markov velocity fields, but no existing diagnostic measures how much information this compression discards. We introduce the \emph{Markovization gap}---the integrated conditional variance of the bridge velocity given the Markov state---which quantifies this loss before any neural network is trained. To make the gap well-defined across model families, we define \emph{Bridge Graphical Models} (BGMs), separating endpoint coupling, bridge law, Markovian projection, and dynamics representation as independent design choices. This decomposition also formalizes Poisson and electrostatic models as field-line bridge kernels with their own caustic gap. Across synthetic, latent, and pixel-space pilots on CIFAR-10 and Fashion-MNIST, a proxy gap estimated in minutes on CPU consistently ranks coupling/bridge choices in the same direction as training loss and FID measured after hours of GPU training.

Mixture-of-Experts (MoE), popularized by large language models, is a promising paradigm for scaling visual generative models. However, conventional token-wise MoE routes tokens independently within a homogeneous expert pool and regularizes expert usage toward uniformity, making it poorly matched to video data that is spatiotemporally redundant and semantically long-tailed. We show that existing visual MoEs fall into a uniformity trap: semantically under-organized routing, compounded by uniform expert-usage regularization, scatters coherent patches across disparate experts, causing routing fragmentation and structural distortion. To address this, we propose SplitMoE, a split-role sparse architecture that breaks the shackles of uniformity. To accommodate the inherent semantic imbalance, we explicitly bifurcate the expert pool into semantic experts and generic experts, with semantic experts capturing high-level semantic abstraction and generic experts preserving residual visual information and flexible generative capacity. Leveraging prototype-guided routing and push-pull regularization, SplitMoE enables tokens to cluster naturally by semantic attributes rather than arbitrary balancing constraints. Extensive results show that under an equivalent activated-parameter budget, SplitMoE outperforms traditional load-balanced MoEs in convergence speed, routing coherence, and video generation quality across standard benchmarks. By revealing an emergent coarse-to-fine denoising logic, SplitMoE provides the community with a modality-aware scaling path, serving as a critical reference for building large-scale video world models.

Diffusion and flow matching approaches to generative modeling have shown promise in domains where the number of elements in a state is fixed in advance (e.g. images), but require *ad hoc* solutions when, for example, the length of a response from a large language model, the number of atoms in a molecule, or the number of amino acids in a protein chain is not known *a priori*. Here we propose Branching Flows, a generative modeling framework that, like diffusion and flow matching approaches, transports a simple distribution to the data distribution. But in Branching Flows, the elements in the state evolve over a forest of binary trees, branching and dying stochastically with rates that are learned by the model. This allows the model to control, during generation, the number of elements in the sequence. We show that Branching Flows can compose with any flow matching base process on discrete sets, continuous Euclidean spaces, Riemannian manifolds, and "multimodal" product spaces that mix these components, and we demonstrate distribution matching on small molecules and antibody sequences, and that this scales to complicated domains such as protein structures.

Text-to-image diffusion models can synthesize high-quality images, yet the outcome is notoriously sensitive to the random seed: different initial seeds often yield large variations in image quality and prompt–image alignment. We revisit this “seed effect” and show that attention dynamics over prompt core tokens, the content-bearing words, measured during the first few denoising steps, strongly predict final generation quality. Building on this observation, we introduce Attention-Based Seed Selection (ABSS), a training-free, plug-and-play method that ranks seeds for a given prompt by leveraging cross-attention to core tokens during the denoising process. ABSS requires no finetuning and does not alter the initial noise; it scores and ranks all candidate seeds, keeps only the top-k for full generation, and discards the rest, without relying on a fixed accept/reject threshold. Operating purely at inference time, ABSS can serve as a lightweight pre-selection add-on for existing seed-optimization pipelines, enabling additional gains. Across three benchmarks, extensive experiments show that ABSS enables consistent improvements in text–image alignment and visual quality for Stable Diffusion variants, as corroborated by human preference and alignment metrics.

Recently several methods have shown promise for purely in silico “de novo” design of antibodies which bind to drug targets. The methods which have shown success in vitro propose candidates via either a generative diffusion model or hallucination-based sequence optimization, and then filter these candidates with a structure predictor model. Hallucination methods rely on compute-intensive backpropagation through a structure predictor model for each candidate, and the generative methods require expensive structure re-prediction and filtering of many (often mostly non-passing) candidates, limiting their application to test-time generation of large libraries. Given the highly variable per-drug-target hit rates of current methods, screening large libraries is a well established route to improved antibody candidate discovery. Thus, we propose an alternative approach, Bonobo, which instead formulates this problem as black-box optimization of a per-drug-target generative model, amortizing candidate generation into training. This is accomplished by using a structure predictor as a reward signal to directly train a GFlowNet generative model. We show that this approach can match or exceed the in silico metrics of state-of-the-art approaches, while allowing for dramatically more efficient generation of diverse and arbitrarily large numbers of antibody binder candidates.

Diffusion models have shown promise in learning to solve constraint optimization problems. However, they are mostly restricted to problems with binary variables and rely on graph neural networks, hindering their application to a broader range of problems such as those with general discrete variables or constraint structures that necessitate global rather than local reasoning. We investigate the use of Diffusion Transformers to address the aforementioned limitations. A naive implementation performs poorly due to a fundamental mismatch between the standard diffusion process and constraint solving: while the former applies *small, incremental denoising* across *all* variables, the latter requires *substantially altering specific subsets of variables* to attain feasibility or optimality. Our method, **Blo**cked **G**ibbs **Di**ffusion **T**ransformer (BloGDiT), is the first to address this limitation by replacing standard joint Gaussian denoising with Blocked Gaussian denoising. BloGDiT uses iterative block resampling and anneals the block size over time to facilitate large, targeted edits within a block of variables. Across Sudoku, Graph Coloring, Maximum Independent Set, and MaxCut, BloGDiT matches or outperforms existing methods, demonstrating that blocked Gibbs-style diffusion provides a highly effective inductive bias for Transformer-based constraint satisfaction and optimization.

Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models operate at the patch scale, producing fixed-size tiles of at most 1024$\times$1024 pixels which encompass only a small fraction of the WSI. In contrast, text reports for clinical diagnoses and slide-level diagnostic labels -- the supervision that exists at scale -- operate at the whole-slide level. We present BigCell, the first approach to generate coherent whole-slide histopathology images at gigapixel resolution. BigCell trains a flow-matching transformer at 2048$\times$2048 pixels on over 120,000 publicly available WSIs spanning multiple organ types. Conditioning on embeddings from pathology foundation models and using an inference-time sliding-window pipeline, BigCell generates coherent images of up to 32,768$\times$32,768 pixels, outperforming prior tile generators on both tile-level and slide-level fidelity metrics. To make gigapixel synthesis practical, we introduce an adaptive scheduling strategy that prioritizes important slide regions, speeding up image generation by over 3\ttimes with minimal quality degradation. Furthermore, we propose the first text-to-image synthesis at 8k resolution by training a separate model that produces dense conditioning grids directly from pathology text reports. We demonstrate that augmenting with synthetic samples improves slide-level few-shot classification performance by up to $18 \\%$ over baselines trained on real data alone.

Video diffusion Transformer (DiT) models excel in generative quality but hit major computational bottlenecks when producing high-resolution, long-duration videos. Full attention requires computing dot products between all pairs of queries and keys, resulting in a quadratic computational complexity with respect to the sequence length ((O(L^2))), leading to high training and inference costs.To overcome this limitation, we propose a Bidirectional Sparse Attention (BSA) framework that sparsification from both the query and key–value directions to reduce the quadratic cost of full attention.Specifically, the sparsification of queries is achieved by pruning tokens that are locally redundant or semantically similar within the 3D spatiotemporal domain of a video. For the key–value pairs, only those that are highly correlated with the query at a global level are selected for attention computation. Furthermore, we design a dynamic threshold adjustment mechanism to adaptively regulate the selected number of key–value pairs, thereby maintaining quality without degradation. Extensive experiments demonstrate that BSA significantly accelerates DiT training across long sequences, reducing FLOPs by up to 20× and achieving 17.79× faster attention training, while preserving or even surpassing the generative quality of full attention.

Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bidirectional dependencies between language and motion, allowing early prediction errors to persist as fixed context and degrade both temporal coherence and cross-modal consistency. Masked discrete diffusion, which models sequences through iterative bidirectional prediction, offers a natural remedy. We therefore propose BiMoGen (Bidirectional Motion-text Generation), a unified masked discrete diffusion framework for bidirectional motion-text modeling. To stabilize training, we design Decoupled Uni- and Cross-Modal Training, in which masked pretraining first establishes cross-modal correspondence on paired motion-text sequences, after which supervised fine-tuning specializes the model for bidirectional generation. Masked diffusion nonetheless introduces its own source of error, as the model is trained on clean ground-truth context yet encounters self-generated and potentially erroneous context at inference, with errors committed under heavily masked states propagating through subsequent steps. We further introduce Generation-Aware Self-Correction that exposes the model to its own predictions during training and applies correction passes at early sampling steps to revise unreliably committed tokens. Extensive experiments on HumanML3D and KIT-ML demonstrate competitive performance on both tasks, validating the effectiveness of the proposed two-stage training and self-correction designs.

Post-training quantization (PTQ) to FP4 has emerged as a key technique for reducing large language model inference cost, especially on Blackwell GPUs which offer native FP4 tensor core support. Direct PTQ to FP4, however, still leaves a noticeable accuracy gap. Recent work on SVDQuant mitigates the errors introduced through FP4 quantization for diffusion models by adding high-precision low-rank corrections obtained via SVD. However, this high-precision low-rank correction requires many bits per rank, and moreover competes on tensor core utilization with the main FP4 GEMM. We introduce BiLoCo, a correction whose left and right factors are stored as binary ${\{\pm 1\}}$ sign vectors rather than as BF16 numbers. The signed format uses $16{\times}$ less storage per rank-one term, and since the correction now consists of additions/subtractions, this can be run on CUDA cores concurrently with the FP4 tensor-core GEMM. On 4--32B LLMs, BiLoCo matches or improves memory-matched SVDQuant for PTQ. We implement BiLoCo on B200 with a correctness-preserving CUDA schedule that runs end-to-end decode at $1.13$--$1.15\times$ FP4-only, with the residual gap coming from the final output combine rather than from one-bit arithmetic.

Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain shifts, and costly dense annotations. Existing ViT-based pathology foundation models rely on patch tokenization, which can disrupt spatial continuity and weaken local morphological details needed for cell-level prediction. To address this, we propose Masked-Diffusion Convolutional Foundation Models, termed ConvNeXt Masked-Diffusion (CMD), a self-supervised convolutional generative pretraining framework for dense pathology representation learning. CMD uses a fully convolutional ConvNeXt-UNet backbone, performs masked-diffusion pretraining in pixel space, and incorporates frozen pathology foundation model features through adaptive normalization. Experimental results demonstrate that CMD consistently outperforms existing ViT-based pathology foundation models and even surpasses state-of-the-art end-to-end segmentation methods while fine-tuning only a small number of task-specific parameters across multiple pathology dense prediction tasks. The advantage is particularly pronounced under limited annotation settings, where CMD exhibits stronger robustness and generalization ability. Our findings suggest that purely convolutional architectures can also serve as competitive pathology foundation models for cell-level dense prediction, achieving leading performance within the current ViT-dominated paradigm and providing a scalable, high-performance solution that better preserves histological structural priors for fine-grained pathology understanding. Project page: https://anonymous.4open.science/r/ConvNeXt-Masked-Diffusion-5BC6.

Conditional diffusion models have emerged as a powerful paradigm for image restoration, leveraging pre-trained generative priors to recover high-fidelity details from degraded inputs. However, existing methods typically follow a static inference protocol, treating the degraded observation as a fixed control signal throughout sampling. This overlooks the potential of inference-time scaling, where guidance can be refined on the fly to improve output quality. Recent test-time optimization approaches that manipulate noise variables or denoising trajectories often compromise structural fidelity, while those targeting text embeddings lack the spatial precision needed for restoration. To address this, we propose Condition-Aware Test-Time Optimization (CATTO), a training-free method that iteratively refines the visual conditioning signal at inference time. CATTO performs reward-aligned condition refinement under the pre-trained prior to enhance perceptual quality. To avoid complex backpropagation, we design a gradient-free optimization strategy guided by a joint objective: maximizing a perceptual reward while enforcing trajectory- and condition-level consistency to explicitly preserve structural fidelity. This process is accelerated by optimizing within a low-dimensional frequency subspace and reusing reliable updates across nearby denoising steps. Experiments on image restoration benchmarks show that CATTO improves perceptual quality over strong diffusion-based baselines without updating model parameters.