NeurIPS 2026 Papers

Post-hoc Graph Neural Networks (GNN) explainers typically extract a compact subgraph to preserve the model’s decision rationale. However, this extraction breaks topological integrity and induces a distribution shift, making predictions on subgraphs unreliable and consequently misleading explainer optimization under OOD settings. Existing attempts to build in-distribution proxy graphs often assume independence between explanation and background subgraphs and rely on hard splicing, which causes semantic mismatch and boundary discontinuities that degrade explanation reliability. We propose SSDGExplainer, a Structure–Semantic Dual-Guided explainer that formulates proxy-graph generation as conditional modeling under semantic consistency constraints to achieve deep semantic alignment between the explanation subgraph and the generated background, introduces a topology boundary optimization network to smooth structural fractures at the explanation–background interface, and enforces contrastive semantic constraints to prevent semantic drift. Extensive experiments on synthetic and real-world benchmarks demonstrate consistent improvements over state-of-the-art methods in both explanation quality and fidelity.

We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly predicts depth, ego-motion, and intrinsics in a single forward pass and is trained/evaluated as a coherent end-to-end 3D estimator. To stabilize joint learning, we use an intrinsics-first two-stage schedule and a unified single-checkpoint evaluation protocol. Scaling SfM self-supervision to unconstrained web video is challenging due to weak multi-view observability and strong corpus heterogeneity; we address these with a multi-view signal proxy (MVS) used for filtering and curriculum sampling, and with expert training distilled into a single student. Pretraining on YouTube-8M (~ 100M frames after filtering) yields strong cross-domain zero-shot transfer and improved fine-tuning performance over prior self-supervised baselines. We release the pretrained checkpoint and code.

Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneous robots, sparse workspaces, simplified geometry, offline computation, or handcrafted parameters to make the problem tractable, which limits their deployment in dense crowd scenarios. Toward this end, we propose Shape-Aware Reinforcement Learned Model Predictive Control (SRL-MPC), a method for safe, efficient, and adaptive navigation in crowds with heterogeneous shapes without geometry simplification. To encode shape-aware safety, we formulate high-order control barrier function (HOCBF) constraints from geometric separation features (GSFs) based on support function transformation. A reinforcement learning (RL) framework then learns a neural policy that reads GSFs and outputs real-time MPC parameter updates, enabling the MPC solver to adapt to neighboring crowd geometries. The key advantage of SRL-MPC is that it preserves the safety structure and generalizability of MPC while integrating the adaptability and intelligence of RL. Experiments in randomized crowd scenarios with arbitrary shaped robot fleets demonstrate the effectiveness, scalability, and robustness of SRL-MPC. The results show that SRL-MPC substantially outperforms representative baselines in safety and adaptability.

AI agents are increasingly used to diagnose and mitigate failures of production systems, known as agentic Site Reliability Engineering (SRE). Current SRE benchmarks are limited to oversimplistic SRE tasks and are unfortunately hard to extend due to bespoke designs. We present SREGym, a high-fidelity, interactive benchmark for SRE agents. SREGym exposes a live system environment built atop real-world cloud-native system stacks, where high-fidelity failure scenarios are emulated through fault injectors. SREGym models the complexity of production environments by simulating (1) a wide range of faults across the stack, (2) various ambient noises, and (3) diverse failure modes such as metastable failures and concurrent failures. SREGym is architected as a modular, extensible framework that orchestrates fault injectors and event emulators across stacks; this framework is key to curating high-fidelity, challenging SRE problems. SREGym shows that the effectiveness of frontier models and agents varies significantly (40+%) in mitigating different kinds of problems. SREGym is actively maintained as an open-source project and has been used by many researchers and practitioners.

Modern image super-resolution methods generate detailed, visually appealing results, but they often introduce visual artifacts: unnatural patterns and texture distortions that degrade perceived quality. These defects vary widely in perceptual impact—some are barely noticeable, while others are highly disturbing—yet existing detection methods treat them equally. We propose artifact prominence as an evaluative target, defined as the fraction of viewers who judge a highlighted region to contain a noticeable artifact. We design a crowdsourced annotation protocol and construct SR-Prominence, a dataset suite containing 3,935 artifact masks from DeSRA, Open Images, Urban100, and a realistic no-ground-truth Urban100-HR setting, annotated with prominence. Re-annotating DeSRA reveals that 48.2% of its in-lab binary artifacts are not noticed by a majority of viewers. Across the suite, we audit SR artifact detectors, image-quality metrics, and SR methods. We find that classical full-reference metrics, especially SSIM and DISTS, provide surprisingly strong localized prominence signals, whereas no-reference IQA methods and specialized artifact detectors often fail to generalize across datasets and reference settings. SR-Prominence is released with an objective scoring protocol that allows new metrics to be benchmarked on our suite without further crowdsourcing. Together, the data and protocols enable SR artifact evaluation to move from binary defect presence toward perceptual impact.

Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human annotations are scarce and subjective, reward models are vulnerable to reward hacking, and self-evaluation methods suffer from prompt sensitivity and biases. In this work, we propose **stable rank**, an intrinsic, annotation-free quality signal derived from model representations. Stable rank measures the effective dimensionality of hidden states by computing the ratio of total variance to dominant-direction variance, capturing quality through how information distributes across representation dimensions. Empirically, stable rank achieves 84.04\% accuracy on RewardBench and improves task accuracy by an average of 14.1\% relative to greedy decoding via Best-of-N sampling. Leveraging this insight, we introduce **Stable Rank Group Relative Policy Optimization (SR-GRPO)**, which uses stable rank as a reward signal for reinforcement learning. Without external supervision, SR-GRPO improves Qwen2.5-1.5B-Instruct by 10\% on STEM and 19\% on mathematical reasoning, outperforming both learned reward models and self-evaluation baselines. Our findings demonstrate that quality signals can be extracted from internal model geometry, offering a path toward scalable alignment without external supervision.

Logical reasoning remains a major challenge for large language models (LLMs), particularly on structured problems that require precise constraint tracking, consistency preservation, and multi-step deduction. This challenge is especially acute for small-scale LLMs, which are more prone to producing inconsistent, redundant, or brittle reasoning trajectories. Existing approaches for improving logical reasoning largely optimize for final-answer correctness, providing only weak supervision over the intermediate reasoning process. In this work, we propose SPRING, a solver-guided reinforcement learning framework for logical reasoning that uses an SMT solver as a training-time verifier of intermediate reasoning steps to provide process-level supervision. SPRING introduces the notion of a novel reasoning step, namely, a step that is logically valid, consistent with the evolving reasoning state, and not already implied by previously accepted non-contradictory deductions. Based on this solver-based assessment, we design process rewards that encourage novel inferential progress while penalizing contradictory and uninformative reasoning steps. Evaluation results on two logical reasoning benchmarks, ZebraLogic and AR-LSAT, show that SPRING consistently outperforms baseline LLMs and outcome-only reward baselines. On ZebraLogic, SPRING improves puzzle accuracy by up to 49.71 and 15.43 points over the base LLM and outcome-only reward baseline, respectively. On AR-LSAT, SPRING improves the overall average score by up to 64.93 and 12.14 points over the base LLM and best-performing outcome-only reward baseline, respectively.

Self-speculative decoding offers a plug-and-play route to accelerating autoregressive large language model (LLM) inference, but its practical success hinges on identifying effective layer-skipping configurations online without retraining or offline profiling. Existing methods mainly optimize agreement-based proxies that capture draft-target consistency, yet overlook intrinsic runtime differences across draft configurations. Consequently, configurations with similar acceptance can still yield substantially different realized decode throughput. We present SPIRIT, a speed-driven online adaptation framework for self-speculative decoding that directly optimizes realized decode throughput, measured as generated tokens per second, using window-level runtime feedback. Our key insight is that speed is not determined by agreement alone: it is jointly shaped by draft-target consistency and the intrinsic speed advantage of the draft configuration. Empirically, the skip ratio serves as a first-order control variable, capturing the dominant consistency-speed trade-off while sharply shrinking the search space over layer-skipping patterns. SPIRIT therefore first identifies an effective skip ratio and then refines the layer-skipping pattern within it. Combined with length-aware normalization and shift-aware profile-based adaptation, this design enables rapid adaptation to task shifts in non-stationary request streams. Across diverse tasks and model scales, SPIRIT consistently outperforms strong training-free baselines and achieves up to 1.74x speedup while preserving the target model's output distribution, establishing a practical foundation for plug-and-play, high-throughput LLM inference.

Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large language backbones make edge deployment difficult due to high memory footprint and memory-bound autoregressive decoding. Weight-only post-training quantization is a practical solution, but pushing VLMs to extreme low bit-widths remains challenging: existing rotation-free methods suffer from outliers at 2--3 bits, while rotation-based methods improve accuracy at the cost of additional runtime overhead. We propose SPHQuant, a rotation-free spherical weight-only quantization framework for VLMs. Instead of quantizing weights directly in Cartesian coordinates, SPHQuant decomposes each 8D weight vector into coordinate signs, radius, and a positive unit direction. This representation isolates outlier magnitude into the radius while keeping directions bounded and statistically regular. Based on this insight, SPHQuant allocates extra precision to the radius to mitigate accuracy degradation induced by outliers. It further uses a compact positive-direction codebook and fine-tunes codebook entries through angular parameterization to preserve the unit-sphere constraint. We also design a hardware-friendly GEMV kernel that keeps the direction codebook small enough for shared-memory lookup and packs radial bits efficiently. Experiments show that SPHQuant matches the performance of state-of-the-art extreme low-bit quantization methods while improving decode throughput by \textbf{30.3\%}, providing a new step toward efficient edge deployment of VLMs.

A fundamental open question in self-supervised learning (SSL) is the explicit characterization of the optimal geometry of the learned representations. Recently, LeJEPA identified isotropic Gaussian embeddings as optimal for minimizing downstream prediction risk in Euclidean spaces. However, the corresponding problem for distributions supported on lower-dimensional manifolds, such as the hypersphere, remains unexplored. In this work, we demonstrate that extending this minimax analysis to smooth distributions on Riemannian manifolds fundamentally changes the optimal solution. We show that, under a worst-case formulation, both \(k\)-nearest neighbors and kernel ridge regression induce hyperspherical uniformity. More precisely, we show that uniform distributions on manifolds are optimal for \(k\)-nearest neighbors, and that the uniform distribution on the sphere is optimal for kernel ridge regression with both the exponential dot-product kernel and the linear kernel. This theoretical insight reveals a fundamental limitation of Gaussian embeddings: their non-uniform density induces anisotropic $k$-NN neighborhoods, severely biasing the estimator. To correct this, we introduce \textbf{SPHERE-JEPA}, a theoretically grounded SSL framework. We adapt LeJEPA's Cramér--Wold projection mechanism to enforce hyperspherical uniformity rather than a Gaussian prior. Empirically, SPHERE-JEPA yields significant improvements, boosting texture retrieval mAP by over 6\%, while consistently matching or outperforming LeJEPA on standard benchmarks—including a $+1.8\%$ linear probing gain on ImageNet-1K (ViT-B/14).

Vision-Language Models for Earth Observation are bottlenecked at the visual encoder. Existing pipelines either compress multi-spectral signals into RGB pixels or collapse them into a single CLIP-style global vector, losing spectral information or spatial structure respectively. We show this is a consequence of coupling contrastive supervision to the dense feature stream, which degrades spectral fidelity. SPEXT resolves this through architectural decoupling. A wavelength-parameterized encoder produces multi-spectral patch tokens for dense tasks and VLM grounding, while a dedicated alignment token absorbs the contrastive gradient for retrieval. SPEXT simultaneously leads the PANGAEA segmentation benchmark (+2.51 mIoU), tops zero-shot retrieval among multi-spectral CLIP baselines (+4.6 mAP@100), and enables a frozen VLM, paired with a small projection adapter and LoRA, to answer free-form Earth-Observation questions directly from its tokens, substantially outperforming an RGB-pixel baseline on a downstream EO-RAG benchmark.

Scaling test-time compute has driven the recent advances in the reasoning capabilities of large language models (LLMs). However, increased compute often comes at the expense of higher user-facing latency, directly impacting user experience. Current test-time scaling methods primarily optimize for accuracy based on total compute resources (FLOPs), often overlooking latency constraints. To address this gap, we propose SPECS, a latency-aware test-time scaling method. SPECS uses a smaller, faster model to generate multiple candidates for each step of reasoning in parallel, and evaluates these candidates using a larger model and a quality critic. We design a theoretically grounded soft verification strategy to select a high-quality candidate to continue the generation. SPECS also employs a dynamic switching mechanism to use speculative drafts only for easier steps to maintain reasoning accuracy. Empirical results on a diverse and challenging set of reasoning and alignment benchmarks show that SPECS matches or surpasses the accuracy of SOTA test-time scaling methods while reducing latency by up to ~26%. Our theoretical analysis shows that as the amount of parallel compute scales, SPECS converges to an optimum of a KL-regularized reinforcement learning problem, a common objective for aligning LLM generation given a reward signal.

Stochastic Partial Differential Equations (SPDEs) driven by random noise play a central role in modeling physical processes with rough spatio-temporal dynamics, such as turbulence flows, superconductors, and quantum dynamics. Although machine learning (ML)-based surrogate models have shown promise for efficiently approximating such dynamics, progress remains limited by the lack of a unified benchmark with controlled data generation and comprehensive evaluation. This gap is particularly significant for singular SPDEs, for which benchmark datasets are largely unavailable and reliable simulation requires numerically delicate schemes based on renormalization. Moreover, subtle differences in data-generation procedures, such as noise approximation, basis choice, and the inclusion of renormalization, can significantly affect the resulting datasets and, consequently, model evaluation. We introduce SPDEBench, the first unified benchmark for ML-based SPDE learning. SPDEBench provides ready-to-use datasets for physically and mathematically significant SPDEs on 1-3D domains with periodic or Dirichlet boundary condition. Both regular and singular SPDEs are taken into consideration. SPDEBench also incorporates representative ML baselines in operator learning, together with 7 evaluation metrics, including Sobolev and distributional metrics beyond the standard $L^2$-error. Supported by SPDEBench, we conduct systematic evaluations of model accuracy, robustness, and out-of-distribution generalization under controlled data variations. Our numerical results show that SPDE-aware architectures generally achieve stronger performance than generic operator-learning baselines. These findings establish SPDEBench as a reproducible and extensible resource, paving pathway for principled benchmarking and architecture design for stochastic spatio-temporal dynamics.

Accurate epidemic forecasting is crucial for public health response, resource allocation, and outbreak intervention, but remains difficult with sparse, noisy, and highly non-stationary data. Because epidemics unfold across interacting regions, spatiotemporal methods are natural candidates for improving forecasts. Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple chronological train-test splits that do not reflect real-time forecasting practice. We address this gap with SpatialEpiBench, a challenging benchmark for spatiotemporal epidemic forecasting in realistic public-health settings. SpatialEpiBench includes 11 epidemic datasets with standardized rolling evaluations and outbreak-specific metrics. We evaluate adjacency-informed forecasting models with widely used epidemic priors that adapt general models to epidemiology, but find that most methods underperform a simple last-value baseline from 1 day to 1 month ahead, even during outbreaks and with these priors. We identify three major failure modes: (1) poor outbreak anticipation, (2) difficulty handling sparsity and noise, and (3) limited utility of common geographic adjacency for epidemiological spatial information. We release benchmark data, code, and instructions at https://github.com/Rachel-Lyu/SpatialEpiBench to support development of operationally useful epidemic forecasting models.

Generalist neural routing solvers have shown great potential in solving diverse vehicle routing problems (VRPs) with a unified model. However, existing solvers are typically limited to symmetric settings or degrade in performance when switching to asymmetric settings due to input inconsistencies or inherent structural differences, substantially limiting their practicality in real-world scenarios that encompass both scenarios. To address this limitation, we define the spatial position of each node based on the relative distances to a specific set of pivots and further propose a Spatial Pivot-Aligned Coordinate-free Embedding (SPACE) framework that unifies node representation and solution generation across symmetric and asymmetric VRPs. Specifically, we construct a bidirectional Fréchet representation using a novel furthest pivot sampling strategy to enable invariant node representations across distinct problem settings. Furthermore, we introduce a weight-decomposed adaptive decoding mechanism that decouples geometric perception from problem representations, mitigating the overfitting of constraint decisions to a specific geometry setting. Extensive experiments on 110 VRP variants, comprising 55 symmetric problems and their asymmetric counterparts, demonstrate that SPACE achieves promising zero-shot generalization in both symmetric and asymmetric VRPs.

Monocular depth estimation (MDE) has advanced rapidly with the emergence of foundation models such as Depth Anything. Their transformer-based architectures provide strong generalization across diverse scenes and domains, but also incur high computational and memory cost, making efficient deployment challenging. Post-Training Quantization (PTQ) provides an efficient and practical solution for model compression, yet low-bit PTQ remains challenging for MDE models. When applying PTQ to Depth Anything, we identify two key challenges: (i) independently minimizing the quantization error of the query and key projections fails to preserve the attention maps induced by their interaction, and (ii) quantization errors accumulate across layers, resulting in intermediate feature distribution shifts. To address these issues, we propose SPA-Q, a Structure-Preserving Adaptive PTQ framework for MDE, consisting of two main components. First, Attention-Preserving Calibration (APC) determines query and key quantization parameters by matching the full-precision attention distribution. Second, Channel-Wise Distribution Alignment (CWDA) learns channel-wise affine transformations to mitigate quantization-induced distribution shifts, and the learned parameters are absorbed into the weights after training. Experimental results show that SPA-Q consistently outperforms existing PTQ methods under 4-bit quantization, achieving an average 25.9\% reduction in AbsRel and a 17.3\% improvement in $\delta_1$ across NYUv2 and KITTI datasets.

Collaborative learning is sustainable only if it benefits each participant; yet, standard Federated Learning (FL) optimizes a global average that often fails to serve individual clients. In heterogeneous settings, a client may rationally prefer training alone rather than contributing to a global model that targets "average-case" optimality but under performs locally. In this work, we address the problem of \emph{Selfish Personalization (SP)}: How can a target client leverage peer data to minimize its own risk? While current techniques often rely on heuristic performance proxies or clustering that lack sharp theoretical support, we propose \emph{SP-Convergence-Aware Client Weighting (SP-CACW)}. This novel framework determines the contribution of peer clients by explicitly minimizing a theoretical convergence bound for the target. By doing so, our approach efficiently separates useful signal from imported bias on-the-fly during the training process. We provide convergence guarantees that establish the theoretical superiority of \emph{SP-CACW}, alongside empirical results on the MNIST CIFAR datasets and LEAF Shakespeare.

In this paper, we introduce SP$^3$, a novel Plug-and-Play algorithm that accelerates Maximum \emph{a posteriori} image restoration by replacing denoisers with Spherical Encoders (SE) as generative priors. SP$^3$ approximates the intractable proximal prior step by utilizing the SE tightly structured latent space as a robust projection onto the natural image manifold. Alternating this projection with a closed-form data-consistency step, via Half-Quadratic Splitting, guarantees stable convergence without requiring gradient computation during inference. This unique formulation unlocks ``anytime'' restoration capabilities, producing sharp, plausible images from the first iteration. Evaluations across a variety of image restoration tasks demonstrate that SP$^3$ achieves perceptual quality comparable to state-of-the-art zero-shot diffusion and flow methods while being $3$-$630\times$ faster.

Speculative Decoding (SD) has shown strong success in accelerating large language model inference, but its efficiency remains limited for Vision-Language Models (VLMs). The diverse input modalities in VLMs make it difficult for a small trained drafter to achieve consistently high throughput across modalities. We address this limitation with an adaptive self-speculative decoding method. In particular, we adaptively select the skipped layers of VLMs to form the draft model that best fits the given query. Our selection algorithm SP$^2$ec utilizes the single-peak structure of the throughput with respect to the number of selected layers. Our algorithmic design explicitly minimizes the novel notion of wall-time regret for the given query, corresponding to the empirical wall-time latency. Theoretical guarantees on the wall-time regret show that SP$^2$ec is adaptive to the per-prompt single-peak structure. We conduct extensive experiments with Qwen3-VL and LLaVA-1.5 models across visual and textual tasks, which demonstrates the efficacy of the proposed SP$^2$ec.