NeurIPS 2026 Papers

Goal recognition is the task of inferring an agent's intended goal from its observed behavior. Existing work has largely focused on high-level symbolic recognition, abstracting away the continuous motion through which agents reach their goal. Methods that do reason over real-world trajectories, rely on an explicit dynamics model that is hard to acquire and rarely transfers across settings. Neither approach explicitly learns how agents move toward each candidate goal, limiting accuracy on ambiguous trajectory prefixes where such knowledge is most needed. We present WoRD (Waypoint Recognition with Diffusion), which uses a learned trajectory prior to infer the agent's goal from partial observations. At each step, WoRD runs one warm-started denoising chain per candidate goal, steers each chain toward the observed trajectory through a closed-form log-likelihood gradient, and reweights the resulting samples by Bayes' rule into a calibrated posterior over goals. We evaluate WoRD on simulated robot settings, pedestrian trajectories, and vehicle trajectories using four metrics targeting distinct failure modes. WoRD is the only method that improves all four metrics jointly, including the safety-critical confident-and-wrong rate. WoRD runs under a single inference procedure and preserves calibrated multimodal. beliefs in complex, ambiguous environments where filter- and classifier-based observers collapse to a single mode or grow overconfident.

Compared with deterministic methods that output only point estimates, probabilistic forecasting can characterize both future trends and their uncertainty simultaneously, making it more suitable for decision-making in complex real-world scenarios. In recent years, diffusion models, owing to their powerful generative modeling capability, have been introduced into the field of time series forecasting. However, we find that most existing diffusion-based forecasting methods directly construct the diffusion process in the original future space, which is easily affected by heteroscedastic scale imbalance, causing highly volatile dimensions to dominate the denoising learning and leading to unstable corrections near observation boundaries. Motivated by these issues, we propose VolaRM, a volatility-whitened probabilistic residual modeling framework for long-term time series forecasting. By constructing a conditional probabilistic base distribution, it represents future targets as whitened residuals relative to this base distribution and performs conditional modeling in the normalized residual space, thereby alleviating scale imbalance across variables and forecasting horizons. Meanwhile, a specific gating mechanism is introduced to enhance boundary continuity and the stability of long-term forecasting. Extensive experiments on eight real-world datasets from different application domains demonstrate the effectiveness of VolaRM.

Reconstructing the complete geometry of a scene from a single RGB image remains challenging—especially when inferring hidden structures where visual evidence is incomplete. We introduce VolFill, a generative framework that predicts the 3D structure of the complete scene rather than relying on traditional pixel-aligned regression. Our method utilizes a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into a compact latent space, paired with a latent Diffusion Transformer that denoises this representation to recover the complete scene. We condition the generation on geometry foundation models, leveraging rich spatial priors for robust reasoning. Unlike existing methods limited by per-ray constraints or unstructured point-cloud queries, VolFill provides a structured representation that supports direct surface extraction and occupancy queries at scale. Extensive experiments on the 3D-FRONT and NRGBD datasets demonstrate that our approach significantly outperforms current baselines, providing a robust foundation for holistic spatial understanding.

Accurate contact modeling is fundamental to understanding hand–object interaction, yet existing contact representations are typically restricted to object surfaces and rely on hand‑crafted rules to recover contact details, leading to severe penetrations and implausible results. To better exploit the rich detail in motion‑capture data, we introduce Volumetric Contact (VolCo), a representation that expands surface points to a set of 3D volumetric grids. VolCo encodes 3D contact that allows precise hand part recovery, and is organized in an inherent hierarchy: local contact details within each volume and global hand geometry across all volumes. Our framework, VolCoDiff, employs two modules to capture local and global features following this hierarchy. For local contact details, we use a 3D variational autoencoder to model the possible hand configurations conditioned on the local object signed distance field (SDF). For global hand geometry, we design a prior‑guided diffusion model that learns the distribution of compressed latent features aggregated from the volumetric grids. We evaluate our method on two benchmark datasets and demonstrate state‑of‑the‑art performance in penetration and stability, indicating the capability to generate tight grasps with much less severe penetrations.

Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a position-selection problem: the model must choose not only which predictions are reliable in isolation, but also which positions should be committed together as context for later decoding steps. Existing confidence-based decoding ranks masked positions independently and commits the top-$K$ positions, largely ignoring whether the committed tokens provide complementary visual grounding. We identify a step-level limitation of this strategy in multimodal settings: high-confidence tokens selected in the same step can rely on overlapping visual grounding, introducing visual redundancy among the committed tokens and leaving less complementary visual grounding available for later decoding. To quantify this effect, we introduce the Visual Redundancy Index (VRI), which measures visual grounding overlap among tokens committed in parallel. To control this redundancy during decoding, we propose Visual-Redundancy-Controlled Decoding (VRCD), a training-free inference-time decoding method that uses token-to-image attention to prioritize visually complementary positions. Across diverse multimodal benchmarks, VRCD reduces visual redundancy and remaining-position entropy with modest runtime overhead. In longer decoding experiments, it also achieves relative accuracy gains of up to $18.8\\%$ on M$^3$CoT and $6.9\\%$ on MMBench over confidence-based decoding.Code is included in the supplement.

Neural network-based virtual microphone estimation (neural-VME) synthesizes microphone signals at unobserved spatial locations from a limited set of real microphone (RM) physical recordings. This enables high-resolution arrays that improve spatial processing for downstream tasks such as speech enhancement. However, existing methods rely on supervised training with ground-truth signals captured at virtual locations, requiring datasets that scale with the number of virtual microphones (VM) and leaving open the question of how to design optimal large-array configurations. We propose \textit{Virtual-Flow}, a flow-matching framework that implicitly generates VM signals conditioned on real recordings, without requiring ground-truth targets. We introduce an unsupervised training strategy based on pseudo-VM targets constructed from time-delayed superpositions of real recordings, and show that it improves magnitude estimation in high-frequency bands, yielding richer spatial cues for beamforming. Experiments demonstrate that the proposed Virtual-Flow, combined with reflow distillation, outperforms existing state-of-the-art neural-VME models on downstream neural beamforming and speech enhancement tasks while requiring lower computational load.

Video diffusion models have made rapid progress in perceptual realism and temporal coherence, but they remain primarily optimized for plausible generation rather than verifiable reasoning. This limitation is especially pronounced in tasks where generated videos must satisfy explicit spatial, temporal, or logical constraints. Inspired by the role of reinforcement learning with verifiable rewards (RLVR) in reasoning-oriented language models, we introduce **VideoRLVR**, a practical recipe for optimizing video diffusion models with rule-based feedback. VideoRLVR formulates video reasoning as the generation of verifiable visual trajectories and consists of an SDE-GRPO optimization backbone, dense decomposed rewards, and an *Early-Step Focus* strategy for efficient training. The Early-Step Focus strategy restricts policy optimization to the early denoising phase, reducing training latency by about 40\% while preserving performance. We evaluate VideoRLVR on Maze, FlowFree, and Sokoban, three procedurally generated domains with objective success criteria. Across these tasks, VideoRLVR consistently improves over supervised fine-tuning baselines, with dense decomposed rewards proving especially important in low-success-rate settings. Our RL-optimized model also outperforms the evaluated proprietary and open-source video generation models on these verifiable reasoning benchmarks and out-of-domain benchmarks. These results suggest that verifiable RL can move video models beyond perceptual imitation toward more reliable rule-consistent visual reasoning.

Fluorescent microscopy provides a rich view into how proteins localize within cells, but it remains experimentally infeasible to image human proteins across all of the different factors that can impact localization. We introduce Vermeer, a channel-adaptive autoregressive generative model for in silico generation of microscopy images of protein localization. Vermeer conditions generations on protein sequences and landmark stains showing the morphology of cells, which enables it to generalize to unseen proteins and cell lines. We show that Vermeer, trained on the Human Protein Atlas, can generate images with substantially improved perceptual quality and biological fidelity over previous proposals. Additionally, Vermeer's autoregressive framework enables flexible generation using varying channel subsets and orderings, enabling zero-shot transfer to data collected under different imaging conditions and channel configurations than those used for training. These results position Vermeer to enable scalable modeling of protein localization and is a step towards generative foundation models that can operate over distinct microscopy datasets.

Flow matching trains a continuum of velocity regression problems but usually reports a loss averaged over interpolation time, obscuring which regions are limited by intrinsic ambiguity and which remain data-limited. We introduce the Velocity Ambiguity Profile (VAP), $\mathcal{A}_v(t)$, the time-local Bayes-risk floor of velocity prediction, and decompose time-resolved loss as $L_N(t)=\mathcal{A}_v(t)+R(t;N)$, where only the excess term $R(t;N)$ is reducible by more data or a different estimator. We prove two statistical benchmarks. In an intrinsic local-regression benchmark, $\mathcal{A}_v(t)$ enters the time-dependent noise factor of the standard nonparametric rate. In separated isotropic Gaussian mixtures, the Bayes velocity becomes locally affine, and a label-oracle residual-regression benchmark has parametric excess-risk scale $K\mathcal{A}_v(t)/N$, matched within the same separated residual Gaussian-location subproblem. Controlled Gaussian-mixture experiments validate this diagnostic through a dense transition sweep, calibrated oracle-to-blind and empirical-proxy audits, and a local controlled time-sampler stress test in which a frozen reducible-error proxy reduces reducible loss while cross-cell sensitivity delimits aggressive sampling. VAP turns an averaged flow-matching objective into a time-resolved diagnostic for locating ambiguity-limited and data-limited regions.

We introduce a variational framework for diffusion models with anisotropic noise schedules with a matrix-valued path $M_t(\theta)$ that allocates noise across subspaces. Central to our framework is a trajectory-level objective that jointly trains the score network and learns $M_t(\theta)$, which encompasses general parameterization classes of matrix-valued noise schedules. We further derive an estimator for $\partial_\theta \nabla \log p_t$ that enables efficient optimization of the $M_t(\theta)$ schedule. For inference, we develop an efficiently-implementable reverse-ODE solver that is an anisotropic generalization of the second-order Heun discretization algorithm. Across CIFAR-10, AFHQv2, FFHQ, and ImageNet-64, our method consistently improves upon the baseline EDM model in all NFE regimes.

Many scientific design problems require identifying rare high-fitness objects in vast discrete spaces using costly black-box feedback. Variational active-generation methods, including variational search distributions (VSD) and conditioning by adaptive sampling (CbAS), address this by learning a distribution over high-fitness designs via inference on a level-set posterior. However, these methods rely on generators with explicit likelihoods, most commonly autoregressive models, creating a mismatch with modern non-autoregressive approaches, such as discrete diffusion and flow-based models, which refine designs in parallel and better capture multi-modal structure but have implicit marginal likelihoods. We introduce \textit{Active Flow Matching} (AFM), a variational active-generation framework that removes this likelihood bottleneck by reformulating level-set KL objectives over the conditional endpoint distributions of discrete flow models. AFM yields forward-, reverse-, and symmetric-KL objectives without requiring density evaluation. We show that forward-KL AFM is target-consistent, recovering the level-set posterior without access to marginal likelihoods. Empirically, across protein and small-molecule design tasks, AFM outperforms strong baselines, including autoregressive VSD and CbAS, inference-time guidance, KL-regularised fine-tuning, and Top-$K$ retraining.

Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions. However, this expressiveness also exposes a key challenge in heterogeneous datasets: generative policies can reproduce unreliable action modes whose return distributions exhibit high variance, occasionally yielding high returns by chance but lacking consistency. Consequently, maximizing the expected Q-value alone is insufficient for identifying reliable actions. We propose VAN-Flow (Variance-Averse n-step Flow), a framework that promotes reliable actions in generative offline RL. VAN-Flow combines (i) a categorical distributional critic, (ii) a variance-averse expectation operator that smoothly reweights atom probabilities to favor actions with both high returns and low dispersion, and (iii) a flow-matching generative actor guided via rejection sampling. Unlike CVaR or mean--variance objectives, the operator redistributes probability mass over the categorical return distribution without hard truncation or auxiliary penalty terms. Across more than 40 tasks from D4RL and OGBench, VAN-Flow consistently outperforms strong baselines, with the largest gains in long-horizon and high-variance regimes where reliable action selection becomes critical.

Pretrained diffusion models increasingly serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution. The teacher gradients these pipelines consume are Monte Carlo expectations over noise levels and Gaussian noise; their estimator variance dominates compute cost because each draw requires expensive upstream work, such as rendering, simulation, or encoding. We introduce CARV, a compute-aware variance-accounting framework that motivates a hierarchical Monte Carlo estimator: amortize the expensive upstream computation over cheap diffusion-noise resamples, sharpened by timestep importance sampling and a stratified inverse-CDF construction. Across diffusion-guided workloads, we obtain 2–3× effective compute multipliers, most from amortized reuse and approximately 25% additional gain from importance sampling plus stratification, without changing the objective. We also map regimes where these gains translate into improved downstream metrics versus regimes where they do not, such as DMD.

The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to structural designability. Current state-of-the-art diffusion and flow-based generative models require the protein length to be predetermined before sampling, limiting their flexibility in exploring the feasible protein design space. To bridge this gap, we introduce Generalized Poisson Flow (GPFlow), a novel generative framework that enables variable-length generative modeling by learning the rate function that minimizes the negative log-likelihood of an inhomogeneous generalized Poisson process. We establish theoretical guarantees for recovering the joint multimodal distribution (both continuous and discrete) and for an upper bound on the KL divergence to the generated distribution. We evaluate GPFlow extensively across various protein design tasks, including unconditional structure and sequence generation and conditional motif scaffolding, to validate GPFlow’s effectiveness on both continuous and discrete modalities. Our results demonstrate GPFlow’s superior generative performance and quality, achieving the top designability and distributional fitness on unconditional generation, and ranking first on 10 out of 16 motif scaffolding tasks, with up to a 10-fold improvement in success rates on the most challenging targets.

We aim to address the problem of *Out-of-Distribution (OOD) Mode Averaging*, a critical problem that emerges when distilling Flow Matching (FM) based behavior policies in offline Reinforcement Learning (RL): When learning over multi-modal datasets, standard distillation inadvertently forces the policy to interpolate across different behavioral modes, causing the generated actions to collapse into invalid, OOD regions. To resolve this, we propose **Value-Rectified Distillation (VRD)**, a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem. By employing a dynamic coupling mechanism, VRD actively rectifies flow dynamics to explicitly decouple distinct behavioral modes, cleanly isolating high-value actions. Specifically, we instantiate VRD through two complementary algorithms: **VRD-Q**, which utilizes value-quantile behavior distillation to statistically filter out low-value behaviors, and **VRD-R**, which reorganizes the latent space to geometrically isolate high-value generative trajectories from the FM-based behavior policy. Extensive empirical evaluations demonstrate that VRD improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over those multi-modal, non-expert datasets.

Inference-time reward guidance is a simple way to improve generative models when completed outputs can be checked or scored by an external verifier. Best-of-N sampling is a particularly strong baseline: it is parallelizable and often highly improves with more samples. However, when the reference model assigns low probability to reward-satisfying samples, selection among independent full rollouts becomes sample-inefficient. We introduce MDM-VGB, a reward-guided discrete diffusion sampler that augments masked generation with value-guided re-masking. MDM-VGB extends the autoregressive VGB backtracking chain from fixed left-to-right prefix trees to any-order masked-state graphs, allowing the sampler to reveal and revise tokens at any position. The resulting Markov chain favors local reveal and re-mask moves that lead to higher-value partial masked states, enabling both root-start reward tilting and leaf-start editing of low-reward samples. We further introduce a flow-cancelled momentum lift of MDM-VGB that preserves the projected target law while reducing oscillatory reveal/re-mask dynamics under finite budgets. We prove that the sampler has the correct reward-tilted leaf law, places non-negligible stationary mass on complete outputs, and remains robust to partial-state verifier error. Across structured reward satisfaction and repair tasks, MDM-VGB improves the quality--cost frontier over reference sampling, best-of-N, and forward-only value-guided rollout.

Video fusion integrates complementary information in multiple source video streams into a single sequence for complete scene perception. Existing methods perform frame-wise fusion and lack unified spatio-temporal modeling across sequences, damaging temporal coherence across sequences. When dealing with degradations, they also ignore temporal dynamics and overload a single network with diverse degradations, leading to limited and imbalanced degradation suppression. To address these issues, we propose V2VFusion, the first unified video-to-video fusion framework that directly generates temporally coherent, high-quality fused videos from degraded multi-source inputs. By reformulating video fusion as a video-to-video conditional generation problem in latent diffusion space, our approach holistically models spatio-temporal dependencies across entire sequences and intrinsically ensures sequence-level consistency. On this basis, a text-guided degradation-conditioned ControlNet bridges high-level textual degradation descriptions with low-level visual priors, enabling interpretable and controllable guidance for structure-preserving fusion beyond purely data-driven conditioning. To handle heterogeneous and compound degradations, a state-modulated degradation-aware hierarchical mixture-of-experts module decouples expert selection into diffusion-state-aware filtering and degradation-aware routing. It prevents unreliable routing and fosters expert specialization to mitigate representation conflicts among diverse degradations. Experiments on infrared-visible, multi-exposure, and multi-focus video fusion tasks and complex degradations validate that V2VFusion surpasses existing methods in fusion quality and temporal stability.

We show that a generative model can discover concepts from data in an unsupervised setting while learning to generate. The Dirichlet Concept Diffusion Model (DCDM) embeds concept discovery into diffusion-based generation. DCDM learns concept centers and infers a Dirichlet distribution over them for each input. The resulting weighted center shapes the forward diffusion mean and reverse denoising process, so components are learned through the same evidence lower bound used to model the data. The method uses no class labels, attribute annotations, captions, pretrained text-to-image priors, or semantic concept supervision. Analysis shows how the formulation preserves concept-level information along diffusion paths and reduces denoising ambiguity, creating pressure for concept centers to capture stable modes. Experiments across diverse image domains show that the learned components form coherent prototypes, support interventions, and reflect recurring visual structure.

Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion and flow-based models. Interaction Field Matching (IFM) is a recently proposed framework that generalizes Electrostatic Field Matching (EFM), rooted in Poisson Flow Generative Models (PFGM). While both frameworks define generative dynamics, they start from different objects: CFM specifies a conditional probability path in data space, whereas IFM specifies a physics-inspired interaction field in an augmented data space. This raises a basic question: **are CFM and IFM genuinely different, or are they two descriptions of the same underlying dynamics?** We show that they coincide for a natural subclass of IFM, which we call *forward-only* IFM. Specifically, we construct mappings between CFM and forward-only IFM and show that they induce the same generative dynamics. We further show that general IFM is strictly more expressive: it includes EFM and other interaction fields that cannot be realized within the standard CFM formulation. Our findings suggest developing generative models using both interpretations rather than treating them separately. Moreover, they highlight a novel direction for generative modeling based on backward-oriented field lines, which lies outside the conventional CFM formalism and may lead to new generative properties.