NeurIPS 2026 Papers

Statistical modeling and inference for temporal networks are increasingly important across modern applications, yet remain challenging in the sparse regime with evolving network sizes and temporal dependence. We propose a tractable model that captures these key features in a unified framework. Under this model, we establish geometric ergodicity and characterize the asymptotic behavior of cycle counts. These counts provide informative low-order summaries of temporal network dynamics. Moreover, we develop method-of-moments estimators based on cycle counts. We prove identifiability and uniqueness of the parameter estimates, and establish the strong consistency and asymptotic normality. To support uncertainty quantification, we further propose a simulation-based plug-in estimator of the asymptotic variance. Extensive simulations and a real data example demonstrate that our methods are accurate and effective for sparse, size-varying temporal networks.

Diffusion models have recently emerged as a powerful paradigm for generating 3D molecules. Despite showing remarkable promise, they learn atomic distributions implicitly from data, without explicitly encoding physicochemical rules into their generative dynamics. Consequently, generated samples can be statistically plausible yet chemically invalid. To address this problem, we propose Sequential Neuro-Symbolic Constrained Diffusion (SensDiff), a framework that integrates symbolic rules into generative modeling, enabling the model to internalize scientific priors during training. Central to SensDiff is the observation that the diffusion trajectory of 3D molecule generation exhibits a coarse-to-fine evolution: valid molecules arise by first mitigating macroscopic geometric conflicts and then refining structures toward microscopic chemical consistency through iterative denoising. Accordingly, SensDiff mirrors this dynamic by sequentially enforcing constraints with adaptive weighting during training, progressively steering generation toward valid molecular geometries. Moreover, SensDiff translates domain knowledge into interpretable generative controls, grounding opaque neural denoising in scientific principles. Experiments on molecular benchmarks and real-world constrained generation tasks confirm that SensDiff consistently improves chemical validity while adhering to physicochemical rules and user-specified targets.

We present a system for learning generalizable hand-object tracking controllers purely from synthetic data, without requiring any human demonstrations. Our approach makes two key contributions: (1) HOP, a Hand-Object Planner, which can synthesize diverse hand-object trajectories; and (2) HOT, a Hand-Object Tracker that bridges synthetic-to-physical transfer through reinforcement learning and interaction imitation learning, delivering a generalizable controller conditioned on target hand-object states. Our method extends to diverse object shapes and hand morphologies. Through extensive evaluations, we show that our approach enables dexterous hands to track challenging, long-horizon sequences including object re-arrangement and agile in-hand reorientation. These results represent a significant step toward scalable foundation controllers for manipulation that can learn entirely from synthetic data, breaking the data bottleneck that has long constrained progress in dexterous manipulation.

Reinforcement learning has achieved remarkable success in robot learning. However, under challenging exploration and contact-rich dynamics, early-stage training is frequently dominated by premature terminations such as collisions and falls. As a result, learning is overwhelmed by short-horizon, low-return trajectories, which hinder convergence and limit long-horizon exploration. To alleviate this issue, we propose a technique called Failure Episodic Memory Alert (FEMA). FEMA explicitly stores short-horizon failure experiences through an episodic memory module. During interactions, it retrieves similar failure experiences and prevents the robot from recurrently relapsing into unstable states, guiding the policy toward long-horizon trajectories with greater long-term value. FEMA can be combined easily with model-free reinforcement learning algorithms, and yields a substantial sample-efficiency improvement of 33.11\% on MuJoCo tasks across several classical RL algorithms. Furthermore, integrating FEMA into a parallelized PPO training pipeline demonstrates its effectiveness on a real-world bipedal robot task.

Vision-Language Pre-trained (VLP) models such as CLIP learn strong representations from large-scale image–text pairs and demonstrate impressive zero-shot transfer. However, by learning to align visual and textual features only at a global level, they often suffer from limited interpretability and weak fine-grained perception. This issue stems from pre-training data that overlooks key visual details of object parts, which are often important for distinguishing between subordinate categories (e.g., species of birds or models of cars). Multimodal Large Language Models (MLLMs) built on CLIP-style vision encoders inherit this weakness, limiting both accuracy and trustworthiness. To address this, we propose Part-Aware CLIP (*PA-CLIP*), a framework that improves fine-grained perception while enhancing interpretability. First, we leverage MLLMs to construct a new dataset,*FG-Part*, containing about one million part-level image–text pairs that explicitly describe discriminative components (e.g., beak shape or wing patterns). Second, we introduce a part-aware training strategy that encourages explicit grounding of fine-grained textual descriptions to corresponding image regions, strengthening part-level cross-modal alignment. Extensive experiments show that PA-CLIP achieves state-of-the-art results on multiple fine-grained visual recognition benchmarks, validating the benefit of part-level captions for capturing subtle details. Moreover, evaluations on general tasks such as cross-modal retrieval indicate that these improvements do not compromise the model's core generalist capabilities.

A point source in a PDE does not occupy an entire grid. A weather station reports one value at one site; it does not observe the temperature between stations. A pickup event in a city occurs at a coordinate and a time; it is not born as an image. Yet many neural-operator pipelines begin by turning such sparse observations into dense surrogate fields before learning. This interpolation step is often treated as harmless preprocessing, but in sparse regimes it changes the object on which the operator is asked to act. We introduce the Dirac Neural Operator (DIRAC-NO), a measure-native neural operator for sparse event-to-field learning without interpolation. DIRAC-NO keeps observations as atomic event measures until query time and constructs the output field through adaptive Dirac-kernel aggregation. Because this aggregation is implemented by direct summation, the front end preserves source additivity at the aggregation stage and admits a learned Green-kernel view of source-to-field response. Across controlled point-source PDE benchmarks and real sparse reconstruction tasks, DIRAC-NO exposes the cost of interpolation-first learning. On MeasureBench-1D, it achieves relative $L^2$ error 0.015 at $n=128$, compared with 0.49 for FNO, and it better preserves superposition structure in trained models. In 2D, it is strongest under parameter and density shift, while ERA5 reconstruction shows that the representation advantage persists beyond analytic PDEs at high observation budgets. Boundary cases and negative-control event streams further show that the benefit appears where the representation argument predicts: sparse, event-native tasks with source-response structure. These results suggest that sparse operator learning should be organized not only by the neural backbone, but by the native space in which the input is represented.

Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-based diffusion models, use stochastic interpolants to corrupt data samples at different noise levels indexed by a time variable. This defines a joint density over both the data space and time, and most methods learn its energy through either spatial or temporal differences. We identify distinct failure modes for both of these approaches. To solve them, we propose Spatiotemporal Noise-Contrastive Estimation (stNCE), a framework for learning the energy through joint spatiotemporal differences. stNCE unifies many existing methods and leads to new training objectives. Experiments on images and molecules demonstrate performance competitive with state-of-the-art density estimation methods.

Large distribution shifts remain a primary obstacle for domain generalization and unsupervised adaptation. Although gradual domain adaptation alleviates this difficulty by introducing ordered intermediate domains, most existing approaches still operate in a discrete, step-wise manner, which can accumulate errors and often relies on unstable Min--Max optimization. We propose \textbf{FlowGDA}, a flow-matching framework that models gradual adaptation as \emph{continuous} transport on a statistical manifold in a representation space. FlowGDA learns time-dependent velocity fields that induce smooth domain trajectories, using a simulation-free regression objective rather than adversarial training. To prevent semantic drift, we couple left-to-right transport between consecutive domains with a source-anchored transport and a semantic consistency loss, and we further stabilize learning via a two-stage sequential optimization scheme. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques, validating flow matching as an effective alternative for domain adaptation.

Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples. The goal is to learn an unknown distribution $p$ on a finite domain $[n]$. The learner is given a fixed family of queryable sets $\mathcal S \subseteq 2^{[n]}$, and each query to $S \in \mathcal S$ returns an independent sample from the conditional distribution $p(\cdot \mid S)$. Learnability is governed by the \emph{co-occurrence graph} associated with $\mathcal S$: two domain elements are connected if they appear together in some queryable set. Pointwise consistency is achievable when this graph is connected on the target support. PAC learning requires more: it is possible when the co-occurrence graph is complete. The sample complexity of PAC learning ranges from nearly linear to quadratic. Every query family with complete co-occurrence graph admits sample complexity $\widetilde O(n^2/\epsilon^2)$, and this bound is tight in the worst case. On the other hand, if every subset is queryable, the optimal worst-case complexity improves to $\Theta(n/\epsilon^2)$. More generally, we identify \emph{hierarchical comparability} as a sufficient structural condition on $\mathcal S$ under which the optimal complexity is nearly linear, $\widetilde \Theta(n/\epsilon^2)$, with pairwise query families as a canonical example. Finally, the full range of polynomial rates between linear and quadratic is attainable: for every $\alpha \in (1,2)$, there exists a query family with optimal PAC rate $\widetilde \Theta(n^\alpha/\epsilon^2)$.

We study online identification of linear-in-parameter dynamical systems whose true parameters drift over time. Measuring non-stationarity through the path variation $V_T = \sum_{t=1}^{T-1}\|\theta_{t+1}^\star - \theta_t^\star\|$, we make two contributions. First, we give a clean blockwise dynamic-regret analysis for continuous projected online gradient descent, yielding $E[R_T] = O(T/\sqrt{\tilde W} + \tilde W V_T)$ and hence the oracle-tuned rate $O(T^{2/3}V_T^{1/3})$ . Second, we propose a residual-based adaptive step size and prove an oracle-proxy comparison theorem: when the residual proxy tracks the local prediction-error signal with lower-order overhead, the adaptive algorithm inherits the oracle scaling. Experiments on synthetic systems and real turbofan-engine degradation data (NASA C-MAPSS) support the matched-regime slope prediction on synthetic data, diagnose the failure regime when proxy overhead dominates, and show a clear unsupervised health-indicator trend in the residual proxy on FD001. A separate fast-rate guarantee under persistent excitation and a tube MPC integration are deferred to the appendix.

Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental challenge: outcome levels drift over time, causing naive models that fit raw outcomes to conflate treatment effects with shifting baseline conditions. Even when the one-step response is identifiable, unrestricted cross-time generalization remains fundamentally ill-posed. Thus, we introduce a constrained decision target based on a causal action geometry with anchored link-scale morphologies. Our approach constructs anchored contrasts over a family of link functions, isolating the action-dependent component of the response that can be shared across periods while allowing flexible, period-specific calibration. This yields a morphology-restricted oracle target for continuous decision making under temporal drift. Building on this formulation, we develop a two-stage orthogonal estimator that first obtains cross-fitted pilot estimates of anchored responses and then performs honest profiled selection over morphology and representation classes. The resulting estimator admits oracle guarantees for the restricted target and, under approximate persistence, produces decision rules that remain near optimal in future periods. Experiments on synthetic data and a large-scale e-commerce pricing application support the proposed target and its decision benefits under temporal shift.

Universal adversarial perturbations (UAPs) aim to learn a single input-agnostic perturbation that consistently induces misclassification across diverse inputs, exposing systemic vulnerabilities of deep models. To enhance practical applicability, data-free UAP methods replace real data with samples drawn from handcrafted synthetic priors and optimize UAPs via activation maximization. However, existing approaches rely on a single synthetic prior throughout training, which biases optimization toward a narrow feature subspace and necessitates costly per-prior tuning. Moreover, the commonly used stochastic gradient ascent (SGA) updates are inherently unstable and tend to converge to sharp regions of the loss landscape, weakening black-box transferability. To overcome these limitations, we propose HPSR-UAP, a data-free framework that couples hybrid-prior reweighting with gradient-guided sharpness regularization. Specifically, we develop a dynamic reweighting mechanism over multiple synthetic priors to mitigate prior bias and avoid overfitting to any single distribution. Building upon this, we introduce a gradient-guided sharpness regularization that smooths the prior-weighted activation loss landscape within a local perturbation neighborhood, promoting updates towards flatter and more transferable directions. Extensive experiments on ImageNet demonstrate that HPSR-UAP outperforms state-of-the-art methods in both white-box and black-box data-free attack settings. We further introduce a multi-granularity block-wise similarity metric that quantifies spatial repetitiveness within UAPs, revealing a strong correlation between structural regularity and attack performance.

Current text-to-image diffusion models remain unreliable for culturally grounded generation, often producing incorrect or stereotyped depictions, particularly for underrepresented cultures. While prior work has largely focused on evaluation and benchmarking, improving cultural generation remains underexplored. In this work, we study cross-cultural image generation through cultural vectors, defined as the difference between fine-tuned and pretrained model parameters. We show that cultural vectors enable fine-grained control over cultural alignment and failure modes through simple scaling at inference time. We further find that deeper U-Net layers encode stronger cultural information, while cultural vectors across countries occupy largely independent directions in parameter space. However, unlike prior work on task arithmetic, naively combining cultural vectors fails to produce meaningful multi-cultural behavior due to strong interference across cultures. Motivated by this limitation, we propose Descriptor-Guided Merging (DGM), a culturally-aware merging approach that estimates cultural vectors for unseen countries using descriptor-based similarities across cultures. Experiments on two benchmarks across 16/10 countries show that cultural vectors improve generation over pretrained models by 18.9%, while DGM improves cross-cultural generalization by 4.2% over naive merging.

Prefill-Decode (PD) disaggregation improves LLM serving by isolating compute-bound prefilling and memory-bound decoding onto specialized GPU pools. However, dynamically rebalancing these pools is challenging because request arrival rates, prompt lengths, and generation lengths change over time. We formulate this autoscaling problem as latency-constrained cost minimization: the controller must minimize GPU usage while keeping P99 tail latency within TTFT and TPOT targets. Existing threshold-based heuristics require extensive parameter tuning, yet still tend to over-provision. To address this, we propose a reinforcement learning (RL) framework that learns a cost-efficient autoscaling policy. The policy uses a recurrent network to track multi-timescale workload trends and pod lifecycle states, enabling decisions that account for delayed pod readiness and delayed SLO feedback. It also restricts the action space with hard masks and anti-oscillation cooldowns, enforcing feasible scaling actions while reducing wasteful GPU transitions. We train the policy in a high-fidelity PD-disaggregated serving simulator calibrated from profiling data and validated against a physical testbed. Simulator experiments on ShareGPT and Azure Conversational traces show that the learned policy satisfies the same latency constraints as grid-searched heuristic baselines while reducing average GPU occupancy. Real-cluster deployment on ShareGPT further confirms sim-to-real transfer while satisfying the latency targets.

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.

Efficient exploration in sparse-reward environments, where informative extrinsic feedback is scarce, remains a significant challenge in reinforcement learning. A key limitation of existing exploration strategies is that they often assume densely coupled environment dynamics, overlooking the underlying causal mechanisms of the environment, particularly the fact that causal relationships may vary across contexts. To address this issue, we propose contextual causal dynamics model-based intrinsic motivation (CIM), a causality-aware exploration framework that explicitly captures context-dependent sparse causal structures and derives intrinsic motivation signals, specifically causal action influence and curiosity. These intrinsic rewards encourage interventions on causally relevant state factors rather than merely promoting observation novelty-seeking behavior, thereby facilitating robust exploration and active skill acquisition. Moreover, by learning contextual causal mechanisms, CIM identifies and filters out task-irrelevant state variables, improving generalization under distribution shifts. Empirical results demonstrate that our method achieves superior sample efficiency, training robustness, and out-of-distribution generalization performance across multiple robotic manipulation tasks.

Mobile sensing for real-time anomaly detection couples routing, sampling, and stopping: dispatch determines which locations are observed, while observations update the evidence used for future dispatch. We study this problem for a fleet of unmanned aerial vehicles (UAVs), where each UAV observes only its current location and anomalies may occur at unknown locations and times. The goal is to detect anomalies quickly while controlling false alarms under decision-dependent partial observations, local mobility, and collision-avoidance constraints. Existing quickest-detection methods typically ignore mobility and collision constraints, whereas learning-based routing methods scale to large fleets but are not designed for statistically calibrated detection. We formulate route-wise monitoring as a partially observable Markov decision process (POMDP) and propose DispatchPPO, a deep reinforcement learning method for long-horizon detection-aware dispatch. Its policy architecture, DispatchNet, uses autoregressive decoding with dynamic feasibility masking to generate collision-free joint UAV moves without enumerating the exponential joint action space. We establish theoretical properties for persistent coverage, evidence-based focusing, and detection delay through a resource-allocation information-rate bound. Experiments show that DispatchPPO reduces detection delay relative to heuristic and optimization-based baselines, while transferring effectively to real-robot and wildfire monitoring case studies.

Safe reinforcement learning (RL) commonly enforces expected-cost constraints, but such expectation safety may fail to control the probability of rare high-cost trajectories. Chance-constrained MDPs (CCMDPs) impose a stronger probability-level requirement, but are widely viewed as harder because the chance constraint is nonconvex and depends on the full trajectory rather than a Bellman-linear expectation. In this paper, we reveal that this computational difficulty does not imply a higher statistical price. In tabular discounted CCMDPs, we give a Bellman-certified model-based algorithm whose sample complexity matches the \emph{minimax optimal} primary dependence of classical CMDP learning, and prove a matching lower bound. Technically, our key idea is the \emph{Bellman distributional certificate}, which constructs a Bellman recursion for constraint violation probabilities before policy selection. The certificate can be reused across candidate policies, shifting concentration analysis from the number of possible policies to a finite Bellman certificate table. Building on this certificate, we establish high-probability safety and near-optimality guarantees for deterministic policy learning in discounted CCMDPs. To our knowledge, we also provide the first model-free sample complexity guarantee for stochastic policy learning in CCMDPs via variance-reduced policy gradient. Numerical experiments on synthetic CCMDPs and IEEE 14-bus energy-storage control benchmark illustrate the safety and behavior of the proposed algorithms.

In-context learning for tabular data sets strong predictive standards in observational settings; it however primarily relies on correlational structure, which becomes unreliable under distribution shift or intervention. While established methods to discover causal structure exist, they are often focused on structure identifiability and decoupled from the predictive architectures that could benefit from them. To bridge these perspectives, we study how to simultaneously infer and enforce causal structure in the form of topological variable orderings into tabular prediction. Unlike standard architectures, our model TabOrder uses causal order-constrained attention, basing predictions only on features that precede a target under a learned causal order. Similar to causal discovery methods, TabOrder learns the optimal variable ordering in an unsupervised manner through a likelihood-based objective. We justify this choice under standard functional model classes and also study how sample missingness, a common challenge in tabular data, interacts with causal direction identification. Empirically, we confirm that TabOrder recovers accurate variable orderings while addressing prediction and imputation tasks, as well as gives insight into real-world biological data under intervention.