NeurIPS 2026 Papers

Series decomposition aims to separate long-term trajectories and periodic patterns in time series, thereby improving the specificity and interpretability of forecasting models. However, mainstream approaches usually equate this functional separation with an operational split between a smoothed baseline and fluctuating residuals. One line of methods approximates the trend via low-pass smoothing, which assumes long-term trajectories to be purely smooth and may misassign nonlinear evolution, stage-wise transitions, and structural fluctuations to the residual. The other relies on heuristic nonlinear decompositions to capture complex dynamics, but is sensitive to noise and hyperparameters, leading to unstable and mixed component boundaries. To address this issue, we propose a forecasting framework, QWaveNet. It employs a quantum convolutional neural network to learn Dynamic Evolution, a long-term structural representation beyond low-pass smoothing priors, which preserves nonlinear evolution, stage-wise transitions, and structural fluctuations more coherently, while its complementary Evolutionary Residual captures localized variations and periodic dynamics. Guided by these complementary components, QWaveNet further performs adaptive multi-scale decomposition and reconstruction to support forecasting. Experiments on multiple benchmark datasets demonstrate the effectiveness of QWaveNet.

A critical observability gap arises in multiscale chaotic systems when coarse observations smooth out high-frequency extremes. For tropical cyclones, this makes standard reanalysis products insufficient for high-precision intensity prediction, as spectral truncation can mask peak winds by over $30\%$. In this work, we introduce Physics-enhanced Tropical Cyclone forecasting (PhysTC), a physics-enhanced benchmark framework for forecasting tropical cyclone track and intensity from globally consistent coarse observations. We curate a harmonized multi-basin dataset spanning 1950--2023, incorporating scale-robust physical predictors to mitigate resolution-induced bias. Building on this dataset, we propose the Physics-enhanced Tropical Cyclone Network (PhysTCN), a model that integrates synoptic spatial structure, storm-following temporal history, and physical consistency constraints to infer intensity-relevant dynamics beyond direct grid-space readout. PhysTCN significantly outperforms state-of-the-art baselines, with particularly strong gains in recovering extreme wind speeds that are underestimated in reanalysis data. This work provides a scalable framework for physically guided forecasting in data-sparse, cross-scale dynamical systems.

Explicitly modeling periodicity has proven to be an efficient approach for time series forecasting, where the residual, obtained after removing the periodic component from the time series, is typically treated as unstructured noise. However, in this work, we show that such residuals still exhibit structured, \textit{phase-dependent} deviations, as supported by both empirical observations and theoretical analysis. This suggests that residuals should not be treated merely as unstructured noise, but instead as \textit{phase-conditioned signals} whose variations arise in both direction and magnitude. Motivated by this perspective and inspired by phase and amplitude modulation in classical signal processing, we propose \textit{Paloma}, a simple yet effective framework for modeling residual deviations. Specifically, Paloma comprises two complementary modules: \textit{a Phase Rotation module}, which performs phase-conditioned rotations to capture directional changes in the feature space, and \textit{an Amplitude Scaling module}, which applies phase-conditioned affine transformations to model magnitude variations. We further abstract the two modules into a general-purpose plugin, termed the \textit{Paloma technique}, which can be readily integrated into existing forecasting architectures to modulate input sequences in a phase-aware manner, enabling backbone models to capture phase-dependent variations in both direction and magnitude. Extensive experiments on twelve datasets demonstrate that Paloma achieves state-of-the-art and robust forecasting performance. Additional results show that the Paloma technique consistently improves a wide range of baseline models, validating its effectiveness as a general-purpose component.

High-resolution weather prediction is important for resolving local atmospheric patterns missed by coarse global forecasts. Large pretrained weather backbones have improved global forecasting, but adapting them to produce future high-resolution fields from recent coarse states remains challenging and costly. We formulate this setting as Predictive Spatiotemporal Weather Downscaling (\textbf{PSWD}), where the target is a future high-resolution trajectory rather than a same-time refined field. To make this structure explicit, we propose a framework that decomposes prediction into spatial and temporal stages using a shared pretrained backbone, fixed numerical scaffolds, and lightweight residual heads. For backbone adaptation, we introduce \textbf{PROLA} (PRincipal-Orthogonal Low-rank Adaptation), which splits a fixed low-rank budget between pretrained principal directions and their orthogonal complement. PROLA further rescales rank-1 optimizer updates using gradient signals, improving adaptation without increasing trainable rank. Experiments in the PSWD setting show that PROLA outperforms representative low-rank adaptation baselines under matched trainable-parameter budgets. These results support PSWD as a challenging setting for weather backbone adaptation and PROLA as an effective method for this setting.

Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building foundation models in Natural Language Processing and Computer Vision. We study neural scaling in Scientific Machine Learning, focusing on models for weather forecasting. To analyze scaling behavior in as simple a setting as possible, we adopt a minimal, scalable, general-purpose Swin Transformer architecture, and we use continual training with constant learning rates and periodic cooldowns as an efficient training strategy. We show that models trained in this minimalist way follow predictable scaling trends and outperform standard cosine learning rate schedules. Cooldown phases can be re-purposed to improve downstream performance, e.g., enabling accurate multi-step rollouts over longer forecast horizons or sharper predictions through spectral loss adjustments. We also systematically explore a wide range of model and dataset sizes under various compute budgets to construct IsoFLOP curves, and we identify compute-optimal training regimes. Extrapolating these trends to larger scales highlights potential performance limits, demonstrating that neural scaling can serve as an important diagnostic for efficient resource allocation. We open-source our code for reproducibility.

Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains poorly understood. Existing evaluations primarily emphasize clean forecasting accuracy and provide limited evidence about how errors enter, persist, and propagate through the occupancy perception-forecasting chain. This paper introduces OccStress, a robustness stress-testing benchmark for the occupancy forecasting chain. OccStress contains 61 corruption categories and 80k+ anchors. OccStress covers both 3D occupancy perception and 4D occupancy forecasting through two complementary tracks. This design separates realistic pipeline errors from the intrinsic sensitivity of 4D forecasting models to corrupted occupancy states. OccStress further defines temporal injection protocols to test whether errors in the current state, recent history, or earlier history affect future forecasts differently. OccStress provides aggregate metrics to evaluating robustness along occupancy forecasting chain. Experiments on OccStress reveal that current occupancy models are substantially affected by both upstream prediction errors and direct state corruptions, and that clean performance alone is an insufficient indicator of temporal robustness. Dataset: https://hf.co/datasets/OccStress/OccStress. Code: https://anonymous.4open.science/r/OccStress.

Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with time-varying interventions, observability of the latent dynamics from observed data is necessary for identifying dynamic treatment effects, linking control-theoretic observability to causal identifiability, even when hidden confounders affect both treatments and outcomes. We derive a continuous-time adjustment formula expressing potential outcome dis- tributions under treatment trajectories via the measurement model, latent dynamics, and the filtering distribution over latent states given observed histories. We propose Observable Neural ODEs (ObsNODEs), Neural ODE models in ob- servable normal form for causal forecasting. ObsNODEs learn continuous-time dynamics with states reconstructible from observations, enabling outcome predic- tion under alternative treatment paths. Experiments on synthetic cancer data, semi-synthetic data based on MIMIC-IV, and real-world sepsis data show strong performance over recent sequence models.

Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass. We apply Njord globally at 0.25° resolution and regionally to the Baltic Sea at 2 km resolution. To scale to these large ocean grids we introduce K-means cluster meshes that adapt to irregular sea surface geometry. Experiments demonstrate strong performance on both domains compared to deterministic machine learning baselines, while also providing uncertainty estimates from the sampled ensemble forecasts. On the global OceanBench benchmark, Njord achieves the lowest errors on average across upper-ocean variables when evaluated against real-world observations, with the largest improvements in surface temperature prediction.

Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-variable dependencies in historical observations, dependencies among future values are much less explored. Specifically, modern forecasting models largely follow the Direct Forecasting (DF) paradigm, generating multi-step forecasts with point-wise objectives that do not explicitly constrain cross-variable structure. In this work, we show that the DF objective is mismatched in the presence of cross-variable and lagged dependencies, revealing an objective gap. To address this issue, we propose **C**ross-**V**ariable **Loss** (CvLoss), a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph. CvLoss penalizes inconsistent edge-wise residual differences over forecast patches, encouraging consistency across both synchronous and asynchronous interactions. Our experiments show that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones. Code is available at: https://anonymous.4open.science/r/CvLoss.

Time series forecasting underpins critical applications in finance, energy, healthcare, and transportation. Although deep models have achieved strong results, most adopt single-scale modeling or restrict multiscale processing to the input side, causing a misalignment between multiscale inputs and single-scale outputs and limiting predictive power. We introduce the Modular Scale-wise Autoregressive Framework (MSAR), a model-agnostic design that forecasts progressively across multiple temporal resolutions. MSAR offers three advantages: (1) scale-wise aligned modeling, which disentangles heterogeneous temporal patterns by aligning inputs and outputs at each scale; (2) scale-wise autoregression, where coarse-scale predictions guide finer-scale forecasting through hierarchical information flow; and (3) a modular architecture, enabling seamless integration with diverse backbones such as CNNs, MLPs, and Transformers. Extensive experiments across a broad set of datasets and forecasting models demonstrate that MSAR achieves consistent improvements in both accuracy and inference efficiency, validating the effectiveness of scale-aligned autoregression for multiscale time series forecasting.

Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory forecasting has advanced for human and vehicle movement, wildlife trajectories present distinct challenges: they are unconstrained in space, highly stochastic, and influenced by environmental conditions. We introduce MOVEBENCH, the first large-scale benchmark for probabilistic wildlife movement forecasting, containing 2.6M GPS locations from 800+ individuals across 110 species in 127 countries, paired with 1.6B environmental raster tiles capturing 160 covariates known or hypothesized to influence movement. We propose a probabilistic evaluation protocol for movement trajectory forecasts, addressing limitations of point-prediction metrics for inherently stochastic phenomena. Through comprehensive empirical evaluation of four method families across multiple temporal and spatial scales, we reveal that: (1) existing predictive methods generalize better to future timepoints than to unseen individuals, (2) deep learning approaches do not consistently outperform simpler baselines, and (3) environmental covariate selection significantly impacts performance. MOVEBENCH enables standardized evaluation of movement forecasting methods and provides a foundation for methodological advances on this ecologically important task.

Scene flow can capture low-level 3D motion displacements in dynamic scenarios. Early pairwise estimators relying on instantaneous two-frame motion lack long-term temporal correlation and also struggle with poor extrapolation ability in future prediction. Although some recent methods attempt to explore multi-frame scene flow estimation in a sequence-to-sequence manner, they typically suffer from heavy computational overhead with increasing input frames and long-horizon prediction degradation due to ineffective motion propagation. To address these problems, we propose a novel memory-enhanced sequential scene flow pipeline, called MESSENGER. To sufficiently mine long-term temporal dependencies naturally within consecutive sequences, a memory buffer is designed by explicitly storing multiple history flow estimates and latent states. For each input frame, the temporally stored flows and states are correlated and retrieved to predict the current initialized flow in a next-frame forecasting manner. Furthermore, we develop an uncertainty-aware reweighting module to filter unreliable retrievals and mitigate accumulated errors. Extensive experiments on nuscenes and Argoverse 2 demonstrate state-of-the-art performance of our MESSENGER, reducing EPE3D by 71.6% on nuScenes and 67.7% on Argoverse 2 in long-horizon future extrapolation. This superiority can be attributed to our designed autoregressive forecasting paradigm, which naturally forces the network to progressively learn the next-frame distribution based on history observations. Code will be released upon publication.

Non-stationary time series forecasting (NTSF) requires modeling evolving trends and seasonal patterns. Although linear decomposition operators (e.g., FFT) help isolate these patterns, they often introduce operator-dependent biases and idiosyncratic noise. To address this issue, we propose a novel Multi-view Decomposed Kalman Mixture of Experts (MDK-MoE) framework, which \textit{improves forecasting by strategically integrating multiple complementary, yet biased, linear decompositions}. Specifically, a Multi-view Decomposition (MVD) extracts frequency components from three complementary views. To mitigate view-specific noise, a Maximizing Mutual Information (MMI) enhances cross-view consistency, maximizes task-relevant information across diverse views, and suppresses view-dependent task-irrelevant information. To address the conflicts inherent in multi-source observations, the efficient Kalman MoE adaptively fuses distinct multi-view features. Instead of adopting traditional $O(d^3)$ covariance inversion, our learnable Kalman gain design enhances numerical stability and enables reliable state estimation under non-stationary scenarios. Extensive experiments on 13 real-world datasets demonstrate that MDK-MoE consistently achieves state-of-the-art performance.

State-of-the-art machine learning weather prediction systems achieve strong upper-air forecast skill but degrade at longer lead times, with mesoscale structures progressively vanishing and ensemble forecasts becoming under-dispersive. While these limitations are often attributed to the training objective, we find that message-passing processors significantly contribute to this degradation. Repeated neighborhood aggregation at each autoregressive step acts as a smoothing operator that, over rollout steps, suppresses high-frequency information in latent space. This effect is particularly limiting in regional weather prediction, where fine-scale structures are already partially unresolved, further eroding mesoscale variability and hindering long-horizon forecasting. We propose GraphMet, a graph-based weather model motivated by Koopman theory that replaces stacked message-passing layers with a single linear Koopman operator step. GraphMet learns linear evolution in a spectral latent basis while preserving the encoder, decoder, and multi-scale icosahedral mesh structure. A two-term spectral objective jointly learns a stable Koopman basis and preserves latent geometry, enabling efficient long-range propagation without repeated nonlinear aggregation. Empirically, GraphMet reduces the 15-day activity and improves regional RMSE by 11-14% at 5km resolution, and enables efficient probabilistic forecasting with lower inference cost than state-of-the-art methods. Our code available at : https://anonymous.4open.science/w/GraphMet-54ED/.

Traffic flow forecasting plays a pivotal role in intelligent transportation systems. However, most existing methods adopt a black-box learning paradigm, resulting in a lack of interpretability in their decision-making processes. Existing explainable methods mostly follow the feature attribution paradigm, leaving explanations at the level of static or discrete feature evidence and making it difficult to reveal the continuous evolution mechanism of spatio-temporal dependencies during dynamic traffic propagation. Meanwhile, prior methods often decouple spatial and temporal explanations, treating spatial structures and temporal dynamics independently and thus failing to capture the intrinsic coupling between spatial dependencies and temporal contexts in model decision-making. To this end, we propose STGIB, a new spatio-temporal graph information bottleneck theory for characterizing continuously evolving explanations in traffic forecasting. STGIB redefines traffic explanation as a time-indexed spatio-temporal explanation trajectory, and jointly learns dynamic key structures and their temporal representations under predictive sufficiency and information compression constraints. Furthermore, we derive tractable recursive variational bounds to make the theoretical objective optimizable, and instantiate a new explainable traffic flow forecasting model via spatio-temporal explanation trajectory generation. Experiments on real-world traffic datasets demonstrate that STGIB maintains competitive predictive performance while generating more faithful and spatio-temporally consistent explanations.

Modeling Irregular Multivariate Time Series (IMTS) poses significant challenges due to asynchronous sampling and data sparsity. While existing methods focus on handling temporal irregularity after encoding, the tokenization stage itself remains underexplored. We propose IMTS-Tokenizer, a time aware tokenization framework built on the Irregular-time-aware Module (IRAM), which employs learnable temporal anchors and Gaussian-kernel attention to aggregate observations while preserving temporal information. To improve robustness under sparse and asynchronous sampling, the framework further incorporates Relative Time Feature (RTF) for pre-aggregation temporal encoding, Variable-Specific Anchor Initialization (VSA) for data-aligned anchor placement, anchor regularization for stable training, and multi-scale modeling. Experiments on four benchmarks demonstrate state-of-the-art (SOTA) performance, with the best results on seven out of eight metrics and statistically significant aggregate gains.

Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institutions, and analysts. Unlike multivariate time series from other domains, stock time series exhibit industry correlation. Exploiting this kind of correlation can improve forecasting accuracy. However, existing methods based on hypergraphs can only capture industry correlation relatively superficially. These methods face two key limitations: they do not fully consider inter-industry lead-lag interactions, and they do not model multi-scale information within and among industries. This study proposes the \textbf{Hermes} framework for stock time series forecasting that aims to improve the exploitation of industry correlation by addressing these limitations. The framework integrates moving aggregation and multi-scale fusion modules in a hypergraph network. Specifically, to more flexibly capture the lead-lag relationships among industries, Hermes proposes a hyperedge-based moving aggregation module. This module incorporates a sliding window and utilizes dynamic temporal aggregation operations to consider lead-lag dependencies among industries. Additionally, to effectively model multi-scale information, Hermes employs cross-scale, edge-to-edge message passing to integrate information from different scales while maintaining the consistency of each scale. Experimental results on multiple real-world stock datasets show that Hermes outperforms existing state-of-the-art methods.

Current 4D occupancy world models use heavy cross-attention or deep recurrence to disentangle ego-motion from scene dynamics. This requires significant computational capacity. However, this effort is largely redundant since ego-motion is inherently known from odometry. We present FreeOcc, a lightweight continuous-time world model that introduces explicit $SE(2)$ geometric decoupling into a conditional flow-matching framework. By mathematically factoring out self-motion, we create a purely dynamic feature space. This space supports attention-free temporal fusion and allows physics-constrained ODEs to operate strictly on residual agent kinematics. To maximize deployment efficiency, FreeOcc introduces an uncertainty-aware blending mechanism. This routes well-observed voxels to a single-pass decoder, reserving the ODE solver exclusively for ambiguous, high-uncertainty regions. Requiring only 28.3M parameters—roughly 40\% of comparable models—our optimal ensemble mode (FreeOcc-E) runs at 27 FPS. On nuScenes, FreeOcc achieves state-of-the-art forecasting performance with a 0.47 m Near Field Chamfer Distance (NFCD) and 32.1\% mIoU. Furthermore, the model transfers zero-shot to SemanticKITTI. It also proves highly robust, retaining 92.8\% of its ideal mIoU under significant sensor packet loss and timestamp jitter.

Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which training recipe to apply. Making these decisions well requires reliable performance forecasts, yet the two commonly used signals are fundamentally limited. Cross-entropy loss is poorly aligned with downstream capabilities, and direct downstream evaluation is expensive, sparse, and often uninformative at early training stages. Instead, we propose to construct proxy metrics by aggregating token-level statistics, such as entropy, top-k accuracy, and expert token rank, from a candidate model's next token distribution over expert-written solutions. Across three settings, our proxies consistently outperform loss- and compute-based baselines: 1) For cross-family model selection, they rank a heterogeneous population of reasoning models with mean Spearman $\rho = 0.81$ (vs. $\rho = 0.36$ for cross-entropy loss); 2) For pretraining data selection, they reliably rank 25 candidate corpora for a target model at roughly $10{,}000\times$ less compute than direct evaluation, pushing the Pareto frontier beyond existing methods; and 3) for training-time forecasting, they extrapolate downstream accuracy across an $18\times$ compute horizon with roughly half the error of existing alternatives. Together, these results suggest that expert trajectories are a broadly useful source of signal for assessing model capabilities, enabling reliable performance forecasting throughout the model development lifecycle.