NeurIPS 2026 论文列表

Despite impressive progress, existing feed-forward stereo 3D reconstruction models produce a single, irrevocable prediction. When these models fail, there is no mechanism to correct their mistakes. We introduce Click3R, a framework for interactive geometric correction of feed-forward models using sparse human correspondence clicks. Given an image pair and a small set of cross-view point correspondences provided by a user, Click3R injects geometric constraints into the 3D reconstruction networks through a lightweight point correspondence adapter, enabling the model to resolve ambiguities in challenging scenarios such as repetitive textures, symmetric structures, large viewpoint changes, or textureless regions. To evaluate interactive reconstruction, we curate a challenging benchmark specifically designed to expose failure modes of existing feed-forward stereo 3D reconstruction methods. Experiments show that Click3R dramatically reduces reconstruction error with as few as a single correspondence click, improving performance on both standard benchmarks and our curated dataset. Our results demonstrate that sparse human interaction provides an effective and practical mechanism for correcting geometric reconstruction errors.

Many prediction problems involve a hidden predictive state: a latent task, environment, rule, or data-generating mechanism that changes the query-optimal act. For a fixed query, recovering the full hidden predictive state is often stronger than necessary. The relevant target is the Bayes-act distinction among hidden predictive states that preserves oracle-gain adaptation. This question is central for in-context learning (ICL): a prompt may reveal part of an unseen task or rule, but an ICL system needs only to recover the prediction-relevant quotient state label for the query. We formalize this target through the canonical predictive quotient (CPQ), a problem-relative Bayes-act quotient of hidden predictive states. The theory characterizes the value of adaptation, the quotient that preserves full oracle gain, the sharp zero/positive-gain boundary, and a Bayes barrier showing why context-only prediction cannot recover a positive oracle gain at the risk level. We give two exact examples. A finite-state example makes the quotient structure, sharp positivity boundary, and a strict-loss mechanism explicit. A linear-Gaussian prior-family witness gives closed forms for a collapsed Gaussian baseline and a state-aware oracle, a restricted zero-gain boundary, and an exact prediction-level disagreement identity for the restricted gain. Together, the results give a decision-theoretic target for ICL: not full latent complexity, but the prediction-relevant quotient state label. We support the theory with exact checks, realized bridge experiments over prediction-relevant quotient state labels, and external validity tests on released pretrained in-context predictors.

Irregularly sampled multivariate time series arise in many real-world domains such as healthcare, climate science, and large-scale monitoring systems. Characterized by asynchronous observations and heterogeneity across variables, such time series pose significant challenges for forecasting. In this work, we introduce Cross-Variable Temporal Attention (CVTA), a short-term, continuous-time forecasting framework that learns prediction functions from a compact, interpretable Markov state summarizing recent temporal dynamics across variables. CVTA uses Variable-Conditioned Embeddings to project heterogeneous variables into variable-specific latent spaces, and applies temporal attention to capture cross-variable dependencies from this state. Furthermore, we demonstrate that CVTA can be integrated as an auxiliary module into existing state-of-the-art prediction models through a dynamic meta-decision model. Extensive experiments on multiple real-world benchmarks show that CVTA achieves strong short-term predictive accuracy, supports efficient online inference, improves existing prediction models when used as an auxiliary module, and learns attention patterns that align well with domain knowledge.

Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer, and a slide-level aggregator. While encoders and aggregators have been extensively studied, the projection layer remains a largely morphology-only bottleneck. This limits endpoints such as biomarker status and survival, which are governed by a molecular state that is not fully captured by H\&E morphology. We introduce Molecularly Informed Staining Transform (MIST), a plug-in replacement for the MIL projection layer that uses paired spatial transcriptomics only during training to construct virtual molecular stains. MIST clusters gene expression profiles into cross-modal prototypes, anchors them in the frozen foundation model feature space, and uses them to reorganize H\&E patch features along molecularly guided axes. It requires no transcriptomics at inference and can be inserted before standard MIL aggregators. We evaluate MIST across 23 downstream tasks and 8 MIL aggregators. MIST improves 240 of 256 configurations over the standard projection layer, with an average gain of +3.5\%, observed consistently across endpoint types: +5.2\% on survival prediction, +3.3\% on tissue subtyping, and +2.6\% on biomarker prediction. Ablations confirm that gene-derived prototypes are the primary source of the gains, while spatial, biological, and pathological analyses show that cross-modal prototype affinities capture spatially coherent molecular programs from H\&E alone.

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.

Owing to enhanced capabilities to capture a broad range of higher-order interactions, simplicial neural networks (SNNs) have emerged as a new powerful methodology for graph learning. However, prevailing SNNs are often limited in efficient exploration of heterogeneous graphs, thereby restricting the SNN utility on downstream tasks. To address this challenge, we introduce the concept of L\'{e}vy flights over simplices, offering a new efficient alternative to learning higher-order graph (sub)structures under heterogeneous scenarios. Specifically, we develop a new Fractional Hodge Laplacian Simplicial Neural Network (FHL-SNN), a novel approach leveraging fractional powers of the Hodge-Laplacian and allowing for a more efficient exploration of the underlying higher-order graph organization in conjunction with the link prediction. We establish a theoretical connection between a fractional diffusion process and graph exploration over simplices. In particular, we prove that the L\'{e}vy flight over simplices yields a lower mixing time than that of its non-fractional counterpart. Our extensive experiments on both directed and undirected link prediction tasks illuminate the power of non-local search of graph space via fractional dynamics, yielding relative gains of up to 9\%. Finally, we show that the idea of L\'{e}vy flights over simplices is versatile and that the integration of fractional dynamics to existing SNNs has the potential to further boost the model performance.

Predictive coding (PC) is a local-learning alternative to backpropagation (BP), training deep networks via a local energy-minimization dynamics rather than a global backward pass. We introduce Augmented-Lagrangian Predictive Coding (PC-ALM), which maintains PC's inference budget but aligns each weight update toward BP by accumulating per-layer constraint errors into a layer-local Lagrange multiplier. In linear PC networks, PC-ALM converges to an equilibrium with exact BP gradients distributed across the network via only layer-local updates. We analyze PC-ALM in nonlinear PC networks up to depth 128 and show that it matches BP performance across all width-depth regimes, notably in deep narrow networks where PC underperforms. PC-ALM introduces recurrent dynamics in each layer's activations. Compared to PC's heat flow on a scalar energy, PC-ALM dynamics are driven by dual ascent on the Augmented Lagrangian. We observe ballistic credit propagation across especially deep networks, with prediction errors evenly distributed across layers, compared to PC's diffusive credit propagation. Beyond the algorithm itself, the augmented Lagrangian framework offers a generalization of PC, and may yield insights on how distributed systems could compute and propagate BP-like credit signals through purely local dynamics.

Link prediction (LP) is a widely applied task in graph learning. Conventional LP approaches typically follow a dataset-specific paradigm, requiring independent training for each graph and incurring high computational and maintenance costs in large-scale applications. Motivated by these limitations, recent work explores Universal Link Prediction (ULP), aiming to enable training-free inference on arbitrary unseen graphs. However, existing methods primarily rely on subgraph sampling strategies to construct universal representations, which are unable to capture higher-order information due to the exponential growth of higher-order neighborhoods. As a result, they often under-perform on graphs where long-range signals are essential, such as biological networks. Moreover, their prediction models are typically domain-agnostic and lack the ability to adapt to different graph domains, leading to limited generalization. To address these challenges, we propose an Atlas-based Universal Link Prediction framework (AtlasULP), which introduces the relation atlas to capture structural associations between node pairs from both global and local perspectives, enabling a comprehensive characterization of connectivity signals across diverse graph domains. Building upon this representation, we develop a domain-guided prompting ULP model, which generates domain-aware prompt tokens from contextual structures and performs prompt-guided in-context prediction for adaptive link inference. Extensive experiments demonstrate that AtlasULP consistently outperforms state-of-the-art methods across diverse graph domains.

Deep click-through rate (CTR) models commonly consist of sparse embedding blocks and feature-interaction blocks, which jointly learn predictive signals from high-dimensional categorical features. This naturally raises the question of whether different blocks in an end-to-end trained CTR model play the same role in generalization. To address this question, we investigate the generalization behavior of deep CTR models from a block-wise diagnostic perspective. Specifically, we formulate deep CTR models as two-block systems comprising an embedding block and a feature-interaction block, and introduce a retuning-based diagnostic protocol to separately assess the transferability of different blocks. Experiments across multiple datasets and architectures reveal a consistent asymmetric generalization pattern: the embedding block exhibits stronger training-set specificity, whereas the feature-interaction block preserves comparatively more transferable structure. Motivated by this diagnosis, we further develop a component-selective embedding aggregation method that averages multiple independently retuned embedding tables while keeping the feature-interaction block fixed. The resulting model retains the original single-path inference architecture and incurs no additional online inference cost. Experiments on Avazu, Criteo, and Taobao show that the proposed method improves representative deep CTR models over standard training and retuning baselines.

Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient prediction regions when environments undergo structural shifts. To address this, we propose \textbf{AdaptNC}, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi-agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.

Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as it can model holistic 3D spaces by encoding geometric occupancy along with semantic categories. However, existing occupancy prediction methods struggle to meet practical deployment requirements, such as adapting to varying computing budgets, sensor setups, and observation views. In this paper, we propose a point-based Adaptive 3D Occupancy Prediction method, called AdaOcc, tailored for embodied scenarios. To accommodate heterogeneous sensor inputs, AdaOcc uses an adaptive geometry-guided dual-branch encoder that can support RGB images in various numbers of views with (estimated) depth maps or LiDAR scans. AdaOcc represents occupied regions via sparse semantic points trained with a progressive query learning strategy, allowing the prediction computational budget to be flexibly adjusted through query point numbers and decoder layers. To facilitate high-fidelity geometric modeling for lightweight point-based occupancy learning, we further propose a novel containment loss that regularizes predicted points to reside within valid occupied regions. Extensive experiments show that our method achieves a new state-of-the-art on Occ-ScanNet with considerable performance improvements over previous methods. Moreover, our framework demonstrates strong practical applicability as an adaptive 3D perception module in real-world embodied systems.

Pre-training Large Language Models requires immense computational resources, making optimizer efficiency essential. The optimization landscape is highly anisotropic, with loss reduction driven predominantly by progress along flat directions. While matrix-based optimizers such as Muon and SOAP use fine-grained curvature information to outperform AdamW, their updates tend toward isotropy, which is relatively conservative along flat directions yet potentially aggressive along sharp ones. To address this limitation, we first establish a unified Riemannian Ordinary Differential Equation (ODE) framework that clarifies the mechanism of adaptive algorithms: the preconditioner induces a Riemannian geometry that mitigates ill-conditioning, while momentum serves as a Riemannian damping term that promotes convergence. Guided by these insights, we propose LITE, a generalized acceleration strategy that enhances training dynamics by applying larger Hessian damping coefficients and learning rates along flat trajectories. Extensive experiments demonstrate that LITE significantly accelerates both Muon and SOAP across diverse architectures (Dense, MoE), parameter scales (130M to 1.3B), datasets (C4, Pile), and learning-rate schedules (cosine, wsd). Theoretical analysis confirms that LITE facilitates faster convergence along flat directions in anisotropic landscapes, providing a principled approach to efficient LLM pre-training.

Predictive models have the potential to transform drug development by increasing success rates across all stages of development pipelines. Evaluating this potential needs to include the whole pipeline, from initial screens of thousands of compounds to the expensive assays, such as toxicity assessment, focused on a few compounds in the later stages. Genetic medicines, such as antisense oligonucleotides (ASOs), could benefit millions affected by individually rare genetic disorders, if economic limitations imposed by the need to screen hundreds of sequences at considerable expense can be overcome. To enable systematic analysis of this problem, we present ASO Atlas 2.0, the largest public multi-endpoint ASO preclinical dataset, spanning 295,007 data points, including both in vitro efficacy and in vivo toxicology, across 430. Using this resource, we introduce a cost-based evaluation framework that translates per-stage classifier enrichment into projected cost savings under a representative preclinical pipeline model. We find that incorporating model predictions across all stages reduces projected pipeline cost by 71%. This saving is largely attributable to modest enrichment at costly late-stage in vivo studies rather than strong enrichment at lower-cost in vitro assays, reversing the model ranking suggested by accuracy alone and highlighting the need for improved toxicology modelling.

Autoregressive language models (ARMs) suffer from the reversal curse: after learning "$A$ is $B$," they often fail on the reverse query "$B$ is $A$." Masked diffusion language models (MDMs) exhibit this failure in a much weaker form, but the underlying reason has remained unclear. A common explanation attributes this mitigation to their any-order masked training objective. However, observing "$[\textnormal{\textbf{M}}]$ is $B$" during training teaches recovery of $A$ from $B$ in one positional configuration, and does not by itself explain why the learned evidence should transfer to the reverse prompt "$B$ is $[\textnormal{\textbf{M}}]$." We provide a theoretical analysis showing that this transfer arises from a parameter-level coupling between forward and reverse positional conditionals: shared Transformer parameters store token-pair evidence, while relative positional encodings route attention through queries and keys without changing the value-side evidence being retrieved. In a one-layer MDM, we prove that forward masked training strengthens evidence that is reusable in reverse queries, induces correlated forward--reverse attention routes, and yields a positively aligned shared-storage gradient component that decreases the reverse loss to first order. Controlled one-layer experiments and large-scale LLaDA/Dream experiments verify these signatures and show that they translate into improved reverse prediction.

Flexible continuous-time survival modeling is critical for capturing complex temporal dynamics in high-dimensional data; however, training such models remains challenging due to the intractable integral required for likelihood estimation. We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation. Furthermore, to effectively capture non-stationary dynamics in complex architectures, we introduce time-conditioned low-rank adaptation, a mechanism that conditions general neural backbones on time by dynamically modulating weights via low-rank updates. We provide theoretical analysis establishing approximation error bounds for cumulative-hazard evaluation. Comprehensive experiments across synthetic benchmarks, large-scale real-world tabular datasets, and high-dimensional medical imaging tasks demonstrate that QSurv achieves competitive predictive performance with advantages in instantaneous hazard function estimation, enabling more interpretable characterization of time-varying risk patterns.

Multivariate conformal prediction requires nonconformity scores that compress residual vectors into scalars while preserving certain implicit geometric structure of the residual distribution. We introduce a Multivariate Kernel Score (MKS) that produces prediction regions that explicitly adapt to this geometry. We show that the proposed score resembles the Gaussian process posterior variance, unifying Bayesian uncertainty quantification with the coverage guarantees of frequentist-type. Moreover, the MKS can be decomposed into an anisotropic Maximum Mean Discrepancy (MMD) that interpolates between kernel density estimation and covariance-weighted distance. We prove finite-sample coverage guarantees and establish convergence rates that depend on the effective rank of the kernel-based covariance operator rather than the ambient dimension, enabling dimension-free adaptation. On regression tasks, the MKS reduces the volume of prediction regions significantly, compared to ellipsoidal baselines while maintaining nominal coverage, with larger gains at higher dimensions and tighter coverage levels.

We view fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predictions change under perturbations that modify the composition of protected groups. A predictor is fair if it remains stable under such shifts. Under this perspective, several classical notions of fairness arise as stability with respect to specific perturbations, with the associated unfairness gap given by a Lipschitz constant of a prediction-rate functional. This formulation also yields guarantees that hold uniformly over a range of demographic compositions at test time, without requiring knowledge of the deployment distribution. It leads to a learning procedure based on convex combinations of reweighted predictors, formulated as a second-order cone program, for which we establish generalization bounds. Experiments on standard benchmarks illustrate the approach.