NeurIPS 2026 论文列表

While Large Language Models (LLMs) have significantly advanced radiology report generation (RRG) with strong generative priors, standard autoregressive decoders still suffer from sequential error accumulation when they must infer all clinical structures directly from images. Diffusion Language Models (DLMs) have recently emerged as a non-autoregressive paradigm with iterative masking and denoising mechanisms. Instead of replacing the mature language decoder with a DLM, we reveal that the key value of DLMs for RRG lies in controllable clinical planning, where the model predicts globally consistent anchor points before free-text realization. To harness this complementary strength, we propose ANCHOR-GEN, the first framework that uses a DLM as an explicit diffusion language planner for RRG. Rather than forcing the autoregressive decoder to infer every critical structure from visual tokens alone, ANCHOR-GEN predicts a structured clinical canvas that serves as clinically grounded anchor points. An autoregressive LLM then leverages these anchors to complete report generation with both fluency and clinical faithfulness. By systematically combining non-autoregressive planning and autoregressive decoding, our unified pipeline is trained with structured denoising, hierarchical coarse-to-fine supervision, and planner-decoder binding objectives. Extensive experiments on the public \textsc{MIMIC-CXR} benchmark demonstrate that ANCHOR-GEN improves clinical faithfulness over prior works. Comprehensive analyses and ablations further show that diffusion planning and causal decoding offer complementary strengths for RRG, and that integrating them in one pipeline provides an effective route toward more faithful medical text generation.

Inspired by recent work on discrete diffusion for anchored unmasking, we introduce a new training-free framework for protein engineering that can generate higher-order variants that improve one or more phenotypes while exhibiting favorable epistatic interactions. Prior approaches require mutation sites to be specified in advance and can drive sequences away from the natural protein manifold. We address these limitations with **A**nchored **P**rotein **E**ngineering via quantized e**X**pectation (**APEX**), a decoding-time method consisting of anchor selection, tilted decoding, and iterative refinement. APEX selects anchor residues using a reward-gradient score with an explicit correction for pairwise epistasis, decodes substitutions for anchors by tilting the base model's per-site predictive distributions along reward gradients, and revisits low-scoring anchors iteratively for optional test-time scaling. All phases query the base model only through masked-conditional logits, a primitive shared by masked language models, discrete-diffusion models, and inverse-folding networks; as a result, APEX works out of the box across commonly-used model families. Across various protein-engineering benchmarks, including thermostability and solubility, APEX achieves state-of-the-art results and avoids the predictor-gaming artifacts that reward-search-only baselines tend to produce.

Denoising generative models predict clean data ($x$), noise ($\epsilon$), or velocity ($v$), and are trained with a squared loss that can be computed in any of these three spaces. Prior work treats these choices as interchangeable up to loss reweighting, but practitioners observe sharp qualitative differences that remain unexplained. We systematically decouple the \emph{prediction space}, what the network outputs, from the \emph{loss space}, where supervision is applied, creating a $3{\times}3$ design matrix. We show that loss-space changes only reweight how much each noise level contributes to training, while prediction-space changes alter what the network is asked to learn: two fundamentally different mechanisms. This decoupling exposes two regimes. Without a representational bottleneck, the loss choice primarily controls denoising quality (MSE) while the prediction choice primarily controls sample quality (FID). The reason is that ODE sampling must convert the network's output back into a clean image, and this conversion amplifies errors differently across prediction types, turning sub-one-percent gaps in training error into 22-fold gaps in FID that are invisible to standard training diagnostics. Under a representational bottleneck, the prediction choice instead dominates both metrics: recovering noise or velocity requires reconstructing high-dimensional information that the bottleneck has destroyed, while recovering the clean image remains feasible because real images lie on a low-dimensional manifold. We validate these findings on a U-Net and the full 130M-parameter JiT architecture across CIFAR-10 and Imagenette, and give architecture-dependent recommendations for both regimes.

3D brain MRI generation has made significant advances for medical imaging, simulation, and controllable anatomical analysis. However, existing generative models typically synthesize 3D volumes monolithically, often overlooking regional anatomical structures and limiting local controllability. To address these limitations, we introduce AnaDiffusion, an anatomically compositional latent diffusion framework that factorizes the generation process into distinct, anatomically meaningful regions followed by part-to-whole assembly and global refinement. Our approach trains part diffusion models first to capture local structural priors. Then, we inject assembled anatomical composite of parts into whole-brain latent and continue denoising. This mechanism enables the model to resolve global context while preserving the injected anatomy. As a result, AnaDiffusion outputs both explicit part assets and a globally coherent volume, enabling controllable part editing without requiring additional dense segmentation masks at inference time while maintaining coherent part-to-whole brain structure. On ADNI, AnaDiffusion achieves the lowest FID across the whole brain, left/right hemispheres, cerebellar-brainstem complex, and seam regions, while also reducing Cohen's d for ventricles, cerebellum, and brainstem. In localized editing experiments, paired MS-SSIM shows high target transfer and off-target preservation, supporting controllable part replacement with limited non-target anatomical drift.

How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, instead of memorizing it, remains a fundamental mystery. To address this, we introduce analytically tractable Bayesian information restricted diffusion (BIRD) models, in which each pixel observes restricted information about noisy data. A BIRD model time-reverses diffusion by inferring which past training sample produced its current restricted observation using the Bayesian posterior. This model class generalizes existing analytical diffusion models that use spatially local information restriction. We show that spatially local BIRD models closely approximate trained diffusion models early in training, across different architectures such as UNets and DiTs. Under minimal assumptions on the data distribution, we identify an information-theoretic phase boundary between memorization and generalization in the joint space of amount of training data, time in the reverse generative process, and amount of information restriction: a BIRD model memorizes when the mutual information between its restricted noisy observations and the training data exceeds the log number of training points, and it generalizes otherwise. Experiments across a range of datasets confirm our theoretically predicted location for the transition. We find that generation proceeds near the edge of memorization: both spatially local BIRD models and early-training diffusion models track the memorization-generalization phase boundary by increasingly restricting information over time. Overall, our results reveal a fundamental role for information restriction in generative AI to circumvent the curse of dimensionality.

We propose a novel amortized optimization method for predicting optimal transport (OT) plans across multiple pairs of measures by leveraging Kantorovich potentials derived from sliced OT. We introduce two amortization strategies: regression-based amortization (RA-OT) and objective-based amortization (OA-OT). In RA-OT, we formulate a functional regression model that treats Kantorovich potentials from the original OT problem as responses and those obtained from sliced OT as predictors, and estimate these models via least-squares methods. In OA-OT, we estimate the parameters of the functional model by optimizing the Kantorovich dual objective. In both approaches, the predicted OT plan is subsequently recovered from the estimated potentials. As amortized OT methods, both RA-OT and OA-OT enable efficient solutions to repeated OT problems across different measure pairs by reusing information learned from prior instances to rapidly approximate new solutions. Moreover, by exploiting the structure provided by sliced OT, the proposed models are more parsimonious, independent of specific structures of the measures, such as the number of atoms in the discrete case, while achieving high accuracy. We demonstrate the effectiveness of our approaches on tasks including MNIST digit transport, color transfer, supply-demand transportation on spherical data, and mini-batch OT conditional flow matching.

We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrained diffusion model separately for each masked image at deployment. We introduce a middle-ground model, termed Amortized Inpainting with Diffusion (AID), which keeps a pretrained diffusion backbone fixed, trains a small reusable guidance module offline, and then reuses it across masked images without per-instance optimization. We formulate it as a deterministic guidance problem with a supervised terminal objective. To make this problem learnable in high dimensions, we derive an auxiliary Gaussian formulation and prove that solving this randomized problem recovers the optimal deterministic guidance field. This bridge yields a principled continuous-time actor--critic algorithm for learning the guidance module in a fully data-driven manner. Empirically, on AFHQv2 and FFHQ under the pixel EDM pipeline and on ImageNet under the latent EDM2 pipeline, AID consistently improves the quality--speed trade-off over strong fixed-backbone and amortized inpainting baselines across multiple mask types, while adding less than one percent trainable overhead.

Diffusion models have achieved state-of-the-art (SoTA) performance in generative tasks through their iterative denoising mechanism, yet they remain computationally intensive and energy-prohibitive. Spiking Neural Networks (SNNs) represent a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Their binary spike encoding enables the conversion of multiplication operations into addition operations, which is a key attribute for reducing energy consumption. Integrating the strong generative capabilities of diffusion models with the energy efficiency of SNNs thus forms a highly promising research direction. In adherence to the design goal of all-additive computation, this paper proposes a novel spiking diffusion model with attention enhancement. Specifically, we adopt non-negative ternary spiking neurons (NNTSN) to construct the U-Net backbone, which helps mitigate information loss during feature processing. To align NNTSN with the requirement of using only additive operations, we further design two core components: a membrane potential attention mechanism and an all-additive shortcut module. Leveraging a reparameterization technique, the proposed diffusion model is developed to retain only additive operations during inference, ensuring strict compliance with energy-saving design principles. Extensive experiments on relevant benchmarks demonstrate that our method achieves SoTA performance in generative tasks. It also substantially outperforms other SNN-based generative models while using fewer time steps, which validates both its effectiveness and efficiency.

To advance the development of embodied intelligence, Open-Environment Active 3D Mapping has attracted increasing attention, aiming to perform a long-horizon and shortest trajectory exploration for reconstructing unseen scenarios. Since only limited information about unseen environments is available, methods built on the closed-set assumption, i.e., assuming that the test environments are similar to those seen during training, cannot generalize satisfactorily. In existing active mapping methods, long-horizon exploration is often guided by predicting a coarse long-range goal and then converting it into an executable path. However, this stage is usually formulated as single-point prediction. Under partial observability, the same local observation may correspond to multiple plausible exploration directions, making such deterministic prediction prone to brittle decisions and degraded performance in unseen scenarios. Our experiments further verify that this is a key factor underlying their weak generalization. To address this issue, we reformulate long-horizon target prediction as conditional multimodal anchor generation using Conditional Flow Matching. Instead of predicting a single goal, our method learns a conditional distribution over coarse exploration anchors from the current mapping state. These anchors are first converted into executable candidate paths through obstacle-aware planning. We then apply exploration-mode clustering to compress geometrically similar trajectories and reduce candidate redundancy. Finally, a hierarchical selection module selects the most promising mode and reranks paths within it to produce the final executable trajectory. Experiments show that our method improves generalization and reconstruction efficiency in open environments.

Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly multimodal action distributions. Their expressivity, however, comes at a significant inference cost: generating each action typically requires simulating many steps of the generative process, compounding latency across sequential decision-making rollouts. We introduce flow map policies, a novel class of generative policies designed for fast action generation by learning to take arbitrary-size jumps---including one-step jumps---across the generative dynamics of existing flow-based policies. We instantiate flow map policies for offline-to-online reinforcement learning and formulate online adaptation as a trust-region optimization problem that improves the critic's $Q$-value while remaining close to the offline policy. We theoretically derive Flow Map $Q$-Guidance Training (FMQ), a principled closed-form learning target that is optimal for adapting offline flow map policies under a critic-guided trust-region constraint. We further introduce $Q$-Guided Beam Search (QGBS), a stochastic flow-map sampler that combines renoising with beam search to enable iterative inference-time refinement. Across $12$ challenging robotic manipulation and locomotion tasks from OGBench and RoboMimic, FMQ achieves state-of-the-art performance in offline-to-online RL, outperforming the previous one-step policy MVP by a relative improvement of $21.3$% on the average success rate.

Spatial resolved inference (SRI) is a critical technique in biomedical research, which aims to measure RNA sequence abundances by associating them with whole-tissue images at a fine-grained molecular scale. However, the extremely low throughput and distributional discrepancies involved pose significant challenges to cellular morphologies captured in hematoxylin and eosin (H&E) connection benchmarking, and existing SRI frameworks suffer from coverage gaps. In this paper, we propose MollFlow, a novel generative Mollified Flow matching model for spatial resolution inference, which is designed to learn the probabilistic dependency between the distributions of whole-slide images and expression profiles. We innovatively reframe the anchors of flow matching as a sequential informative prior derived from whole-slide images rather than isotropic Gaussian noise. During inference, MollFlow progressively refines the prior trajectory, ultimately converging to biologically plausible inferences. For the challenging coupling, which requires enabling intermediate states to explore pathologically aligned subspaces, we carry out a boundary mollified strategy to stabilize the velocity field, rather than being constrained by rigid trajectories. Endowed by this, our unique modulation generation perspective allows the model to retain coupling alignment for manifold incommensurability and prevent coverage gaps. Empirically, we find that MollFlow is capable of accurately inferring gene expression while exhibiting excellent performance in a variety of application scenarios.

Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encoders has recently been shown to provide a powerful inductive bias, improving both convergence and sample quality. In this work, we extend this idea to inverse problems, where pretrained generative models are employed as priors. We propose applying representation alignment (Repa) between diffusion or flow-based models and a DINOv2 visual encoder, to guide the reconstruction process at inference time. Although ground-truth signals are unavailable in inverse problems, we empirically show that aligning model representations of approximate target features can substantially enhance reconstruction quality and perceptual realism. We provide theoretical results showing (a) that Repa regularization can be viewed as a variational approach for minimizing a divergence measure in the DINOv2 space, and (b) how under certain regularity assumptions Repa updates steer the latent diffusion states toward those of the clean image. We integrate Repa into multiple state-of-the-art inverse problem solvers, and provide extensive experiments on super-resolution, box inpainting, Gaussian deblurring, and motion deblurring confirming that our method consistently improves reconstruction quality, while also reducing the number of discretization steps required to reach the same performance level as the underlying solver.

Pretrained generative models, such as diffusion and flow matching models, have demonstrated exceptional capabilities in synthesizing high-quality data. However, steering their generative trajectories to satisfy strict constraints—such as identity preservation in image editing or obstacle avoidance in robotics—remains fundamentally challenging. While recent training-free guidance methods enforce path-safety, they rely heavily on min-norm controllers that only account for immediate constraint satisfaction. These myopic approaches often yield suboptimal trajectories that hover dangerously close to the boundary, severely restricting the model's ability to optimize toward its target. In this paper, we propose AegisFlow, a training-free, non-myopic path-safe guidance framework that recasts guided generation as a safety-critical optimal control problem with global scope. By integrating a time-varying barrier function directly into the global cost functional, AegisFlow naturally enforces constraints along the entire generative path. To compute the global optimal control, we derive a tractable, mathematically grounded approximation of the Hamilton-Jacobi-Bellman (HJB) equation. Comprehensive experiments demonstrate that AegisFlow outperforms state-of-the-art methods across text-guided image manipulation and robotic planning, establishing a significantly better trade-off between target alignment and constraint satisfaction.

Diffusion Transformers (DiTs) have achieved strong performance in visual generation, but their dense token processing leads to slow training convergence and high computational cost. Recent token routing methods such as TREAD accelerate training by allowing a random subset of tokens to bypass intermediate layers. It can be expected that carefully tuning the routing ratios over time steps or layers can achieve more significant accelerations. However, the theoretical mechanisms underlying stochastic routing remain underexplored, making the tuning of routing ratios intractable. In this paper, we first theoretically show that stochastic token routing can be interpreted as an implicit route-sensitivity regularizer. Under this view, the routing ratio determines the strength of the induced regularization: larger routing ratios introduce stronger route-induced perturbations and therefore stronger regularization pressure. This further motivates a noise-conditioned routing strategy. In diffusion training, samples at larger timesteps contain heavier noise and often provide less stable gradient signals, suggesting that they may require stronger regularization. We therefore assign larger routing ratios to higher-noise samples, allowing the routing-induced regularization strength to adapt to the noise condition. Based on this insight, we propose \emph{Noise-Aware Routing}, which partitions samples within each mini-batch into low-, middle-, and high-noise regions and assigns progressively larger routing ratios to them. This simple noise-conditioned allocation synergistically bridges computational savings with enhanced model priors. Extensive experiments on class-conditional ImageNet generation show that our method accelerates training convergence and improves generation quality. Noise-Aware Routing achieves up to \textbf{15.6}$\times$ and \textbf{20}$\times$ convergence speedups over DiT baselines—and outperforms TREAD—on ImageNet-256 and ImageNet-512, respectively, measured by the training iterations required to match our 400K-step FID. In the guided setting, our method achieves an FID of \(2.67\) with a substantially shorter training iterations without architectural changes.

Offline goal-conditioned reinforcement learning (GCRL) aims to learn a single policy that adapts its behavior to arbitrary goals from a fixed dataset, making injection of the goal information into the policy a central design question. While recent works increasingly leverage expressive conditional generative modeling architectures, which have demonstrated remarkable success in computer vision, architectural choices for goal conditioning in offline GCRL remain under-explored. We identify that goals in GCRL differ from typical conditioning signals in two ways: they are often noisy or redundant, and they are consumed within a time-sequential episode where the relevant context of a fixed goal shifts as the agent's state evolves. Motivated by these observations, we propose State-Anchored Goal Conditioning (SAGC), a modulation-based goal conditioning scheme that derives its scale and shift parameters by jointly processing state and goal representations in a learned latent space. We further introduce AdaptFlow, an end-to-end trainable offline GCRL architecture that integrates conditional flow matching with SAGC. AdaptFlow outperforms matches all baselines on 23 out of 27 OGBench tasks.

Autoregressive video diffusion models generate streaming video by producing frames sequentially, conditioning each chunk on previously generated content. These models are structurally anchored to the first frame: its key-value representation occupies a privileged position in the attention cache and serves as the primary scene reference throughout generation. As the cleanest and most error-free position in the cache, this anchor draws disproportionate attention, suppressing video dynamics, and locking scene composition to the initial viewpoint even as the scene naturally evolves. The result is a temporally shallow video in which motion, camera movement, and scene progression are dampened in favor of static consistency. To address this, we replace the static anchor with an adaptive state, a hidden latent that the model denoises alongside content at every chunk but never generates. Rather than referencing a frozen first frame, the model generates its own scene anchor at each step by attending to both the previous state and the current content, producing a reference that evolves with the generated content. Unlike standard video generation, which encodes an absolute notion of time, our formulation treats time as relative: every generation step sees the same positional structure regardless of how far generation has progressed, and the state transition is identical at every chunk. Together, these properties introduce a recurrence into the generation process, where denoising serves as the transition function, and the KV cache serves as the carrier, requiring no external module. Experiments demonstrate that the adaptive state substantially improves video dynamics, enabling richer motion and natural scene progression within generated videos.

Low-Rank Adaptation (LoRA) reparameterizes a weight update as a product of two low-rank factors, but the Jacobian $J\_\mathcal{G}$ of the generator mapping the factors to the weight matrix is rank-deficient, so the factor-space preconditioner $J\_\mathcal{G}^\* \mathcal{F}\_t J\_\mathcal{G}$ induced by any ${W}$-space preconditioner $\mathcal{F}\_t$ is singular, and consequently the standard chain rule cannot be uniquely inverted to map a preconditioned ${W}$-space direction back to a factor-space update. We cast existing LoRA optimizers in a unified framework parameterized by two choices: (i) which invertible surrogate for $J\_\mathcal{G}^\* \mathcal{F}\_t J\_\mathcal{G}$ to use, and (ii) which $\mathcal{F}\_t$ on ${W}$ to use. Existing methods occupy four families along these axes: factor-space adaptive updates, block-diagonal surrogates for $J\_\mathcal{G}^\* J\_\mathcal{G}$, Frobenius-residual pseudoinverse methods, and Riemannian manifold constraint. Within this design space, a gradient-statistics-aware $\mathcal{F}\_t$ paired with a closed-form factor-space solve at $\mathcal{O}((m+n)r)$ memory remains underexplored. We propose AdaPreLoRA, which fills this gap by adopting the Adafactor diagonal Kronecker preconditioner $\mathcal{H}\_t$ on ${W}$ and selecting from the resulting factor-space solution family the element minimizing an $\mathcal{H}\_t$-weighted imbalance between the two factor contributions; by construction, the resulting factor update is the closest LoRA approximation to the preconditioned ${W}$-space direction under the $\mathcal{H}\_t$-weighted norm. Across GPT-2 (E2E), Mistral-7B and Qwen2-7B (GLUE, ARC, GSM8K), and diffusion-model personalization, AdaPreLoRA is competitive with or improves over a representative set of LoRA optimizers while keeping peak GPU memory at the LoRA optimizer level.

Many high-throughput scientific experiments produce distributional outputs, requiring conditional generative models to interpret the data, and to predict outcomes at new inputs. Acquiring experimental data at new conditions (e.g., for a new antigen or a new cell type) is often expensive, motivating the need for automated experimental design methods. However, existing active learning frameworks are limited as they assume scalar or vector-valued outputs. We first show that the Wasserstein test risk of a conditional generative task decomposes into a mean term and a shape term, demonstrating why mean-based active learning is blind to distributional shape. To capture both, we propose the transport Neural Tangent Kernel (tNTK), a tractable kernel that measures how sensitive a generative model's predicted distribution is with respect to its parameters. The tNTK recovers the standard NTK in the deterministic limit and upper-bounds the posterior Fréchet variance of the prediction under small parameter perturbations. It admits a closed form for affine Gaussian transport and tractable Monte Carlo estimators otherwise. Treating the tNTK as a Gaussian process covariance function, we greedily select inputs that minimize the total GP posterior variance across the pool, yielding a distribution-aware active learning strategy. We evaluate this strategy across four classes of conditional generative models, including conditional flow matching and conditional Monge gap, on synthetic distributions, intent-labeled sentences, and single-cell perturbation responses. In these experiments, the tNTK outperforms the baseline NTK computed from the conditional mean, demonstrating the value of distribution-aware active learning for generative-modeling tasks.

Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In generative discovery, however, one aims to sample valid new-to-nature designs, assigned negligible probability under, and thus inaccessible to, standard models fitted to the observed data. To overcome this limitation, we depart from data distribution matching and view a generative model through its generable set: the region it covers with non-negligible probability. This allows to introduce a new learning principle for out-of-distribution flow modeling: enlarging a model’s generable set to increase coverage of the valid design space. We propose Active Flow Expansion (ActFlow), a continued pre-training method that employs verifier feedback to expand a pre-trained model over new valid regions by iteratively adapting to synthetic data generated through active exploration in the learned flow representation. Theoretically, we establish to our knowledge first-of-their-kind statistical learning guarantees for out-of-distribution flow modeling, analyzing generable set expansion as a local-to-global reachability process over a learned representation. Empirically, we assess ActFlow with suitable out-of-distribution generative modeling metrics across small organic molecules, mid-sized drug-like molecules, therapeutic peptides, and protein sequence design tasks. Results show that ActFlow expands valid coverage far beyond the region modeled by the initial pre-trained model, significantly outperforming widely adopted synthetic flow pre-training methods.