NeurIPS 2026 论文列表

We study noisy inductive matrix completion (IMC) in the presence of heavy-tailed and possibly asymmetric noise, where the goal is to recover a rank-$r$ matrix of ambient dimension $d$ given $n$ features as side prior information. So far, there has been a lack of theoretical understanding of the statistical estimation error in noisy IMC, let alone theory under heavy-tailed noise. We first develop an efficient two-stage nonconvex algorithm, called RGDIMC, via robust gradient descent with spectral initialization and feature-aware matrix factorization, based on an adaptive Huber loss to accommodate heavy-tailed noise. We prove that RGDIMC converges locally at a linear rate with sample complexity depending only linearly on $n$ (up to $\log n$) and logarithmically on $d$, and achieves the minimax-optimal statistical error rate $O_p(\sigma\sqrt{dr/p})$ under merely a bounded second-moment condition on the noise, where $r$ is the rank of the unknown core matrix. The theoretical results are strongly supported by extensive experiments.

Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (\ie, Incomplete Views, IV) and cross-view correspondences are mismatched (\ie, Noisy Correspondences, NC) for certain instances. As a remedy, numerous IV- and NC-oriented multi-view clustering (MvC) methods have been proposed, which however require either reliable correspondences or sufficiently complete instances, thus stopping short of addressing the imperfect information problem. In contrast, we observe that both IV and NC challenges originate from the same issue of imperfect cross-view counterpart information, where the counterpart of an anchor instance in another view might be either unavailable or unreliable. Based on the observation, we propose a novel robust MvC framework, termed Posterior-guided Latent Counterpart Inference (PLCI), which could handle both IV and NC in a unified manner. Specifically, PLCI formulates the desired cross-view counterpart of each anchor instance as a latent variable, and integrates both instance-level reliability and prototype-level semantic transport to infer the posterior distribution of the latent counterpart. Extensive experiments on six widely-used multi-view datasets against 10 state-of-the-art MvC methods demonstrate the effectiveness of PLCI for tackling the imperfect information problem. The code will be released upon acceptance.

Latent space Bayesian optimization circumvents the curse of dimensionality over structured inputs by performing Gaussian process surrogate-guided search in a continuous latent space learned by deep generative models. However, existing latent space Bayesian optimization methods overlook both the inherent stochasticity of latent space representations and the geometric properties of the manifold induced by VAE, leading to suboptimal optimization performance. In this work, we propose a marginalized kernel that integrates out the latent variables through the VAE inference posterior, yielding a closed-form kernel on the observation space that naturally accounts for encoding uncertainty. We adopt a quadratic polynomial kernel as the base kernel in the latent space, which captures discrepancies in the first two moments of the latent posteriors while avoiding the bandwidth sensitivity inherent to the RBF kernel. Furthermore, inspired by the Riemannian interpretation of VAEs, we incorporate a geometry-aware sampling scheme that leverages the metric structure revealed by the learned posterior covariances to guide candidate acquisition toward high-information regions of the latent space. Empirical evaluations on molecular design and robot design tasks demonstrate that our method outperforms existing state-of-the-art baselines across the majority of tasks.

A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experimental measurements. While sequence-to-structure diffusion models provide powerful priors, posterior sampling methods steer generation by perturbing atomic coordinates with gradients from experimental likelihoods. However, when the target lies in a low-density region of the prior, these methods require aggressive upweighting of the likelihood that can destabilize sampling and be sensitive to hyperparameters. We propose EmbedOpt, an inference-time steering framework that introduces an orthogonal optimization axis: rather than performing posterior sampling under a fixed prior, EmbedOpt directly optimizes the prior by updating the model's conditional embedding. This embedding space encodes rich coevolutionary signals, so optimizing it shifts the structural prior to align with experimental constraints. Empirically, EmbedOpt matches coordinate-based posterior sampling baselines on sparse distance constraints and outperforms them on cryo-electron microscopy map fitting, including real, noisy experimental ones. Furthermore, EmbedOpt's smooth optimization behavior yields robustness to hyperparameters spanning two orders of magnitude and enables comparable performance with fewer diffusion steps.

We study estimating rare-event probabilities $I = \mathbb{P}(g(\mathbf{X}) > \gamma)$ with $\mathbf{X} \sim \mathcal{N}(\mu, \Sigma)$ and general $g : \mathbb{R}^d \to \mathbb{R}$. We address this problem through importance sampling, and propose a framework that substantially improves efficiency and robustness over baselines such as crude Monte Carlo, adaptive cross-entropy, variational-inference-based methods (including forward- and reverse-KL approaches), as well as Safe-ICE, Subset Simulation, and Sequential Monte Carlo, *drawing* on ideas from both cross-entropy methods for rare-event estimation and cross-entropy methods for optimization. The key contribution has two parts: first, we separate the problem into *coverage*, to overcome the cold-start barrier, and *fitting*, to refine proposals once a meaningful signal is available; second, we constrain the proposal family in a way that provably guarantees finite-variance importance sampling, supported by a theoretical result (since coverage alone is not sufficient --- without safeguards, importance sampling may still suffer from infinite variance). Together, these ingredients yield proposals that are both expressive and stable. Extensive experiments demonstrate significant variance reduction, strong robustness across diverse benchmarks, and favorable cost--efficiency trade-offs, with the proposed approach often outperforming these baselines, particularly in high-dimensional and multimodal settings where competing methods frequently become unstable or fail.

Diffusion models represent a powerful class of generative models known for their solid theoretical foundations and remarkable performance across diverse tasks and domains. While diffusion models have been extensively utilized for generating entire graphs or small-scale graphs, no diffusion-based approaches have been developed to synthesize graph structures within an existing graph, including synthetic nodes and their associated edges. In this study, we introduce the Robust Graph Diffusion Model (RGDM), designed to generate labeled synthetic graph structures consisting of nodes and edges that integrate seamlessly into a given graph. The RGDM consists of a Robust Graph Autoencoder (RGAE) and a Latent Diffusion Model (LDM). Leveraging an edge selection mechanism and an innovative low-rank regularization on the latent feature, the RGDM produces clean and high-quality synthetic graph structures, even when trained on graphs subject to adversarial attacks. Comprehensive experimental evaluations reveal that Graph Neural Networks (GNNs) trained on the augmented graph, which is formed by merging the original attacked graph with the synthetic graph structures, exhibit significantly improved robustness against various graph adversarial attacks in the context of semi-supervised node classification. The code of the RGDM is available at \url{https://anonymous.4open.science/r/RGDM}.

Flow Matching (FM) is a strong framework for generative modeling due to its stable training and efficient sampling. However, FM assumes clean target data and labels, an assumption often violated in practice. We formulate FM training as a time-dependent regression problem over conditional vector fields and analyze how the two corruption types affect the objective: i) target corruption adds a time-dependent residual term that grows with time, ii) label noise causes updates to the wrong class-conditional vector field. Based on this analysis, we propose to improve FM robustness by mapping corruption-specific residual scores to sample weights. For target corruption, we propose the \emph{Temporal Residual Score} (TRS), which emphasizes residual magnitudes at intermediate-to-late FM times, where clean and corrupted samples are more separable. For label noise, we propose the \emph{Comparative Conditional Score} (CCS), which compares the residual of the observed class-conditional vector field against alternative class-conditional vector fields for the same trajectory and velocity target. Experiments across controlled corruption settings and real-world noisy labels show that the proposed methods improve robustness over standard FM and robust-training baselines, supporting corruption-specific residual structure as a practical basis for robust FM.

Frame-wise action-controlled image-to-video generation is a promising paradigm for interactive world simulation, where each control signal should elicit an immediate visual response. However, maintaining visual fidelity and 3D consistency over long autoregressive rollouts remains challenging. Existing 3D-aware methods often suffer from catastrophic drift due to two impediments: information loss from \textit{Latent--RGB Cycling}, where generated latents are repeatedly decoded to RGB and re-encoded for future conditioning, and the training--inference gap induced by the \textit{error-free hypothesis}, where clean training memory fails to match prediction-corrupted inference memory. To address these challenges, we present \textbf{Robust Dreamer}, a memory-augmented framework built around how to design 3D memory and how to use it robustly. First, we introduce \textbf{Latent Gaussian Memory}, which anchors diffusion latents inherited from the generation process to Gaussian primitives and recalls them via latent-space Gaussian splatting. This provides dense, geometry-aware, view-aligned conditioning while avoiding accumulated degradation from repeated VAE conversion. Second, we propose \textbf{Deviation Learning with Dynamic Deviation Archive}, which synthesizes rollout-induced latent deviations through a one-step approximation, stores them by autoregressive stage and denoising timestamp, and injects them into historical memory during training. This exposes the generator to realistic corrupted memory states and teaches internal correction before inference. Experiments on ScanNet, DL3DV, and OmniWorldGame demonstrate state-of-the-art long-horizon performance.

Domain generalization (DG) aims to learn predictive models that can generalize to unseen domains. Most existing DG approaches focus on learning domain-invariant representations under the assumption of conditional distribution shift (i.e., primarily addressing changes in $P(X\mid Y)$ while assuming $P(Y)$ remains stable). However, real-world scenarios with multiple domains often involve compound distribution shifts where both the marginal label distribution $P(Y)$ and the conditional distribution $P(X\mid Y)$ vary simultaneously. To address this, we propose a unified framework for robust domain generalization under divergent marginal and conditional distributions. We derive a novel risk bound for unseen domains by explicitly decomposing the joint distribution into marginal and conditional components and characterizing risk gaps arising from both sources of divergence. To operationalize this bound and prevent it from becoming vacuous, we explicitly enforce feature-space $\ell_2$-normalization. We then design a meta-learning procedure that minimizes and validates the proposed risk bound across seen domains, ensuring strong generalization to unseen ones. Empirical evaluations demonstrate that our method achieves competitive performance not only on conventional DG benchmarks but also in challenging multi-domain long-tailed recognition settings where both marginal and conditional shifts are pronounced.

We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple robust training surrogate that empirically improves performance under data contamination, with small additional computational overhead. The theoretical foundation rests on a local divergence construction: under the Gaussian reverse-kernel structure of DDPM, each per-step likelihood ratio follows a lognormal distribution parameterized by a scalar mismatch, so the conditional f-divergence at each step reduces to a one-dimensional function of the denoising error. Summing these local divergences yields a training objective that unifies diffusion training as divergence-induced weighted denoising, where the derivative of the induced divergence acts as a residual-space influence weight that controls the contribution of each sample. Bounded-influence divergences (Hellinger, negative exponential) suppress large-error samples, with Hellinger yielding an explicit exponential weight, connecting the framework to robust M-estimation. Empirically, on CIFAR-10 under 30% contamination, NED reduces FID from 93.0 (KL) to 77.5, while also outperforming standard robust losses such as Huber and clipped MSE.

Conformal prediction (CP) constructs prediction sets with marginal coverage guarantees under the assumption that the calibration and test distributions are identical. However, under distribution shift, existing approaches primarily align marginal conformal score distributions, which is sufficient to preserve marginal coverage but does not control the conditional coverage error at individual test inputs. As a consequence, CP can remain unreliable in regions where the conditional score distributions are mismatched. In this work, we bound the conditional invalidity of CP under distribution shift in terms of the Wasserstein distance between the calibration and test distributions. This result highlights the role of invertible transport in mitigating conditional coverage degradation. Motivated by this insight, we introduce Branched Normalizing Flow (BNF), a two-branch architecture that normalizes a test input to the calibration distribution and transforms the prediction set of the normalized input back to the test distribution while preserving conditional guarantees. Empirically, BNF consistently improves conditional coverage robustness on nine datasets across a wide range of confidence levels.

Approximate Nearest Neighbor Search (ANN) is an important algorithmic primitive that has found a plethora of applications in machine learning and information retrieval. The classic approach to this problem due to Indyk and Motwani (1998) leverages Locality Sensitive Hashing (LSH) by sampling multiple hash functions that are likely to identify similar data points. This approach has, however, been demonstrated to be vulnerable to adaptive queries and updates which may not be independent of its internal randomness (Kapralov et al. 2024). While differential-privacy-based techniques have yielded robust versions of randomized algorithms and data structures for estimation problems (Hassidim et al., 2022), ANN is a search problem: the algorithm must return an actual dataset point, without obfuscating the output by adding noise or rounding it. Feng et al. (2025) circumvent this limitation by relying on a density assumption that bounds the number of points near each query point. We study a stronger worst-case model in which the adversary chooses both the initial dataset and an adaptive query sequence. For LSH-equipped metric spaces, we give adversarially robust ANN algorithms with sublinear query time whose guarantees do not depend on any structural assumption on the dataset. The main technical challenge is to preserve sublinear search time when the adversary selects a dataset with arbitrarily many points near a query. Rather than applying differential-privacy robustification as a black box, we develop search-specific mechanisms: fairness as a route to adaptive security, a bucketing reduction from search to robust decision, and a concentric annuli construction that improves the query-time exponent. Moreover, for low-dimensional spaces, we give algorithms with a strong ``for-all'' guarantee, which are correct for every possible query.

Recent advances in neural density estimation have enabled amortized Bayesian inference for complex stochastic simulators. However, these methods rely on simulators accurately reflecting the true data-generating process and can degrade significantly under model misspecification. We consider the setting where multiple unlabeled observations are available and introduce robust variational neural posterior estimation (RVNP), an amortized Bayesian inference method that uses an importance-weighted autoencoder to jointly learn a misspecification-robust posterior and an explicit error model that captures the misspecification gap. Our results show that RVNP can recover robust posterior inference in a data-driven and interpretable manner, outperforming previous methods across multiple metrics and misspecified benchmarks.

The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real-world environments. However, current robotic benchmarks primarily emphasize skill-level execution and provide limited insight into such cognitive reasoning capabilities. We introduce RoboWits, a bi-manual robotic benchmark designed to systematically evaluate cognitive reasoning, creative tool use, and robustness to unexpected conditions. To enable scalable construction of high-quality reasoning-centric unexpected scenarios, we propose an automated task generation pipeline formulated as a multi-agent cooperative framework, comprising agents for seed task generation and verification, metric generation, scene generation, and task mutation. Using the pipeline, we curated 30 diverse seed tasks and 208 tasks with mutations and graded difficulty across geometry, material, and assembly-based reasoning. We benchmark popular robot policies, pre-trained VLAs, and oracle-state planners. Our results reveal a significant performance gap: while pre-trained VLAs exhibit preliminary success on seed tasks after single-task fine-tuning, they struggle to perform on mutated tasks, implying their brittleness in manipulation tasks requiring reasoning, strategy adaptation, and robustness to deceptive or constrained environments.

Scaling Vision-Language-Action (VLA) policy training requires diverse robot demonstration data, yet collecting such data remains expensive and labor-intensive. The community has explored ways to accelerate robot data collection: visual augmentation methods synthesize novel samples by editing visual elements in existing demonstrations, while systems such as UMI leverage wrist-mounted cameras to flexibly collect in-the-wild manipulation videos. However, the mismatched observation setups of these two directions leave them largely disconnected, limiting their synergistic potential for scalable robot data generation. To bridge this gap, we propose **RoboExo**, a generative framework for controllable wrist-to-exocentric conversion of robotic demonstrations. We introduce a geometry-motion factorization strategy that reconstructs canonical object-robot geometry and propagates motion states from noisy wrist-view videos, yielding reliable structural priors for cross-view synthesis. These priors guide a conditional video diffusion model to generate target-view exocentric videos that are spatially aligned, temporally coherent, and faithful to the underlying manipulation semantics. We evaluate RoboExo on a wrist-to-exo generation benchmark under both seen and unseen settings, where it consistently outperforms all baselines, reducing LPIPS by **76%** and FVD by **64\%**. Beyond generation quality, RoboExo improves VLA policy learning across six tasks, increasing the average success rate by **+30\%** with generated exocentric views. These results demonstrate RoboExo as an effective and scalable data engine for enriching low-cost UMI demonstrations. The code will be publicly available.

Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly aligned with the capabilities that matter most for robot decision making, including instruction following, manipulation success, and physical plausibility. They also suffer from error accumulation in long-horizon autoregressive prediction. We present RoboAlign-R1, a framework that combines reward-aligned post-training with stabilized long-horizon inference for robot video world models. We construct RobotWorldBench, a benchmark of 10,000 annotated video-instruction pairs collected from four robot data sources, and train a multimodal teacher judge, RoboAlign-Judge, to provide fine-grained six-dimensional evaluation of generated videos. We then distill the teacher into a lightweight student reward model for efficient reinforcement-learning-based post-training. To reduce long-horizon rollout drift, we further introduce Sliding Window Re-encoding (SWR), a training-free inference strategy that periodically refreshes the generation context. Under our in-domain evaluation protocol, RoboAlign-R1 improves the aggregate six-dimension score by 10.1% over the strongest baseline, including gains of 7.5% on Manipulation Accuracy and 4.6% on Instruction Following; these ranking improvements are further supported by an external VLM-based cross-check and a blinded human study. Meanwhile, SWR improves long-horizon prediction quality with only about 1% additional latency, yielding a 2.8% gain in SSIM and a 9.8% reduction in LPIPS. Together, these results show that reward-aligned post-training and stabilized long-horizon decoding improve task consistency, physical realism, and long-horizon prediction quality in robot video world models.

Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass/fail evaluation outcomes, and thus fail to capture long-horizon, multi-target development at real engineering scale. To address this gap, we present ROADMAPBENCH, a benchmark of 115 long-horizon coding tasks grounded in real open-source version upgrades across 17 repositories and 5 programming languages. Each task places the agent on a source-version code snapshot and provides a multi-target roadmap instruction requiring it to implement the functionality introduced in the target version, with a median modification of 3700 lines across 51 files. We conduct a systematic evaluation on thirteen frontier models and find that even the strongest, Claude-Opus-4.7, resolves only 39.1% of tasks, while the weakest achieves merely 5.2%, in stark contrast to the high scores these models attain on existing bug-fix benchmarks, suggesting that long-horizon software development remains a largely unsolved problem.

ARC tasks can be formulated as visual transformation problems, where a model must infer a high-level transformation rule from a small set of demonstrations and apply it consistently across a grid. While dense Vision Transformers (ViTs) perform well in this setting, they entangle local visual state and global transformation logic within a shared token representation, which can hinder consistent reasoning. In this paper, we introduce RoSeViT, a role-separated Vision Transformer that explicitly decouples high-level instruction inference from visual workspace computation. RoSeViT uses controller tokens to aggregate transformation-level information and workspace tokens to represent the visual grid, with a structured attention mechanism that routes nonlocal interactions through the controller. This design is motivated by a formal analysis showing that enforcing a shared instruction representation can reduce the effective complexity of visual transformation modeling. Under the standard VARC training and evaluation protocol for ARC, RoSeViT consistently outperforms dense ViT baselines. In a controlled backbone replacement setting, it improves ARC-1 accuracy from 54.5 to 56.6, and with recurrent refinement reaches 58.8. When integrated into the full system, RoSeViT-Ensemble improves performance from 60.4 to 62.6 on ARC-1. These results demonstrate that explicitly separating instruction and workspace representations leads to more effective visual reasoning on ARC tasks.

Moving an object in a single image requires geometry-consistent spatial rearrangement, including handling occlusions, revealing previously unseen regions, and maintaining coherent shadows and reflections. Existing approaches are not well suited to this setting and often fail to preserve such scene-level consistency. We address this problem by introducing a geometry-aware object motion method that operates directly on the internal representations of diffusion transformers. Our key insight is that rotary positional embeddings (RoPE) define a structured spatial field that can be explicitly manipulated to induce controlled motion. We extend 2D RoPE into a depth-aware formulation that encodes 3D spatial structure, enabling consistent object displacement and scene-aware updates. Our model is trained using synthetic data combined with a small set of real images via parameter-efficient fine-tuning. Despite minimal real supervision, it preserves object identity under large spatial displacements, generates plausible content in newly revealed regions, and consistently updates scene-dependent effects such as shadows and illumination. Experimental results demonstrate the superiority of our method.