NeurIPS 2026 论文列表

Semi-supervised anomaly detection (SSAD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such point- or pair-centric view not only overlooks the contextual nature of anomalies, which are defined by their deviation from a collective group, but also fails to exploit the rich supervisory signals that can be generated from the combinatorial composition of sets. Consequently, such models struggle to exploit the high-order interactions within the data, which are critical for learning discriminative representations. To address these limitations, we propose SetAD, a novel framework that reframes SSAD as a set scoring problem. SetAD employs an attention-based set encoder trained via a graded learning objective, where the model learns to quantify the degree of anomalousness within an entire set. This approach directly models the complex group-level interactions that define anomalies. Furthermore, to enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism, which assesses a point's anomaly score by aggregating its normalized deviations relative to multiple randomly sampled reference contexts. Extensive experiments on 10 real-world datasets demonstrate that SetAD significantly outperforms state-of-the-art models.

Graph-level anomaly detection (GLAD) is a critical task in domains like social networks and bioinformatics. However, current unsupervised contrastive learning-based methods rely on semantically homogeneous augmentations, leading to catastrophic informational overlap that trivializes the learning objective and effectively masks anomalous signals in GLAD. To overcome this, we propose Sima (Semantically Complementary Spectral Views), a novel contrastive framework that learns from semantically complementary spectral views rather than redundant homogeneous views. Sima first constructs a pair of spectral views to separately capture global community structure and local heterophilous variations, which together cover the full graph spectrum of anomalous patterns. Inspired by the Multi-View Information Bottleneck principle, we further design a soft subgraph extraction module with Score Reconstruction Attention (SRA) and an anchor-guided condensation mechanism to distill the essential shared information from each view into a compact representation. We then optimize a contrastive objective across these distilled views to enforce cross-view alignment and amplify subtle structural deviations. Extensive experiments on challenging GLAD and graph-level out-of-distribution detection (GLOD) benchmarks demonstrate that Sima achieves substantial and consistent gains in average performance over state-of-the-art baselines.

Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; and at training time, through self-training methods. Both are gated by the same bottleneck: the verifier. V-R loops stall when verifier scores inflate over rounds while accuracy stagnates, and when feedback is too generic to act on; self-training fails similarly when bad self-generated data are added to training. Better verifiers would unlock both, but the capability we want to train, i.e., catching errors the generator cannot detect in its own work, has neither finetuning data nor a verifiable reward. To address this challenge, we propose *self-trained verification* (STV). Our key observation is that, while a model cannot catch these errors alone, it can when shown the reference solution. We turn this asymmetry into a supervision target and train the verifier to imitate a more informed version of itself. At test time, STV substantially improves V-R loops on hard problems, while standard alternatives (e.g., SFT, RL on verifier scores, and even meta-verifiers) do not. STV roughly doubles accuracy on hard DAPO math and lifts it 14x on the hardest SciKnowEval problems (1.5% to 21%). At training time, starting from an RL-converged generator, putting the STV verifier in the loop yields a further 33% relative gain in test-time pass@1. More notably, the generator's standalone pass@1, with no verifier at inference time, climbs 30% relative past where continued RL had converged. Hence, the next frontier in reasoning may lie in how we train verifiers.

In time-series systems, safe active learning seeks to collect informative data while maintaining safety during data collection. A key challenge is that one-step safety does not guarantee future viability: an input can be immediately safe yet lead the system into a state from which no safe continuation exists. For example, in pressure regulation, an input may keep the current pressure below its limit but still drive the system toward an unsafe pressure spike over the next few steps. We study this problem in nonlinear time-series systems with unknown dynamics and safety constraints. We introduce the notion of $m$-step dead-end inputs and propose a future-aware safe active learning framework (FA-SAL) that enforces finite-horizon safety via margin-certified rollout constraints. Our approach constructs a conservative approximation of the true future-safe set by combining Gaussian process uncertainty with a recursive error bound over predicted trajectories. We show that FA-SAL provides high-probability safety guarantees, recovers interior future-safe inputs as uncertainty decreases, and provably excludes separated dead-end inputs. Experiments on synthetic and real-world benchmarks demonstrate that FA-SAL reduces safety violations and dead-end selections while maintaining competitive model learning performance.

We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transitions such as degradation or task completion. Our approach uses a self-supervised contrastive objective to learn a low-dimensional latent space where progression manifests along manifolds anchored by fixed prototype vectors. This structure yields latent-space indicators that quantify progression in a human-meaningful way without proxy labels. We evaluate the approach against the state of the art on diverse domains, including industrial degradation, robotic tasks, and neural activity, validating three key capabilities: (1) end-state prediction, (2) multi-step forecasting, and (3) interpretable phase separation. Our method matches or improves over black-box counterparts on all of these while providing transparency about the underlying mechanisms. A simple linear regressor on top of the learned indicators is competitive with deep architectures, providing direct quantitative evidence that the underlying state is encoded in a geometrically accessible form. Code is available at https://anonymous.4open.science/r/LRPTS-9300/README.md.

Existing Time Series Foundation Models (TSFMs) for Multivariate Time Series Anomaly Detection often overlook discrete *state variables* that describe system status and treat them uniformly with *numerical variables*. This inappropriate modeling approach prevents the model from fully leveraging state information and even leads to significant performance degradation when state variables are integrated. To address this limitation, this paper proposes a novel **ST**ate-aware **A**dapte**R** (STAR). Specifically, STAR comprises three core innovative components: (1) an *Identity-guided State Encoder* that effectively captures the complex semantics of state variables; (2) a *Conditional Bottleneck Adapter* that dynamically injects state influence into TSFMs; and (3) a *Numeral-State Matching* module that effectively detects anomalies inherent to the state variables themselves. Extensive experiments on real-world datasets demonstrate that STAR significantly improves the performance of existing TSFMs.

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.

Accurate time series forecasting with exogenous inputs is critical across domains, including energy and retail, yet modern deep learning models often overfit to correlations that do not generalize under shifts in these inputs. We propose SLiDE, a Koopman-inspired architecture for forecasting with exogenous inputs that imposes structure on latent temporal dynamics. SLiDE approximates nonlinear system evolution using a learned linear operator in a latent space, combining (i) a history encoder that reconstructs the current latent state from past targets and inputs with (ii) a shared linear rollout driven by future exogenous variables. By enforcing a common linear recurrence across encoding and prediction, SLiDE aligns how past information is encoded with how future states evolve, reducing parameterization and preventing reliance on spurious correlations in the input history. Empirically, SLiDE achieves state-of-the-art accuracy on real-world benchmarks, including electricity price forecasting and retail demand, while maintaining a computational footprint comparable to lightweight MLP architectures. SLiDE is especially effective at generalizing to shifted exogenous inputs, reducing MSE by 20% on electricity price samples with out-of-range exogenous values and MAE by 30% in a synthetic transfer setting where the same dynamical system is driven by an unseen exogenous process. Ablations confirm the importance of both the structured history encoder and the linear latent rollout, suggesting that structured latent linear dynamics provide a useful inductive bias for forecasting with exogenous inputs.

Autoregressive language models generate under a fixed left-to-right order, whereas diffusion language models (dLLMs) denoise masked tokens through flexible any-order trajectories that may commit multiple positions per step. This makes dLLMs a distinctive setting for test-time scaling: additional inference compute can diversify both token choices and the order in which positions are committed. Existing samplers do not explicitly allocate these two sources of diversity. Confidence-based decoding is reliable but often redundant, while global temperature sampling and random remasking inject stochasticity without distinguishing useful exploration from unreliable commitments. We propose SIGMA, a training-free sampler that formulates each denoising step as a constrained allocation problem over decoding efficiency, token-level diversity, trajectory-level diversity, and model-internal commitment uncertainty. The resulting objective decomposes joint sampling entropy into token and trajectory terms, yielding a sigmoid-form stochastic gate for position selection and an adaptive per-position temperature for token sampling. Experiments on LLaDA and Dream across math and code benchmarks show that SIGMA improves the accuracy--cost Pareto frontier and can be integrated into existing dLLM test-time scaling pipelines.

Conventional real-valued neural networks struggle to explicitly capture phase-frequency coupling, while long-horizon time series forecasting requires models to characterize non-stationary amplitude variations, phase shifts, and frequency-dependent temporal evolution. This mismatch limits the stability and expressiveness of neural models on complex forecasting tasks. To address this challenge, we propose SDHilb, a structure-preserving forecasting framework that lifts real-valued observations into an adaptive complex state space and models temporal evolution as a process jointly governed by amplitude and phase information. Through the Hilbert transform, phase-related structures are explicitly encoded, leading to a coupled evolution of real and quadrature components that motivates Schrödinger-Hamiltonian structured dynamics in the lifted complex space. Based on this formulation, SDHilb integrates Schrödinger-guided linear dynamics with an adaptive multi-scale Hilbert transform and introduces three key components: (1) a structure-preserving linear dynamic module with symplectic discretization, (2) an adaptive multi-scale Hilbert module for constructing data-dependent quadrature components and refined time-frequency decomposition, and (3) a theoretically grounded parameter reuse mechanism derived from the closed-form recurrence structure. Extensive experiments demonstrate that SDHilb achieves competitive or superior performance on both short- and long-term forecasting tasks while reducing horizon-specific parameterization through recurrence-guided parameter reuse.

Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. However, such models are fundamentally limited by exposure bias: during inference, errors can accumulate across sequential predictions, leading to pronounced quality degradation in long-horizon generation. To address this, we propose **SDFlow** (**S**imilarity-**D**riven **Flow** Matching), a non-autoregressive framework that operates entirely in the frozen VQ latent space and enables parallel sequence generation via flow matching. We tackle three key challenges in making this transition: (1) eliminating exposure bias by replacing step-wise token prediction with a global transport map; (2) mitigating the high-dimensionality of VQ token spaces via a low-rank manifold decomposition with a learned anchor prior over the latent manifold; and (3) incorporating discrete supervision into continuous transport dynamics by introducing a categorical posterior over codebook indices within a variational flow-matching formulation. Extensive experiments show that SDFlow achieves state-of-the-art performance, improving Discriminative Score and substantially reducing Context-FID, particularly for challenging long-sequence generation. Moreover, SDFlow provides significant inference speedups over autoregressive baselines, offering both high fidelity and computational efficiency. Code is available at *https://anonymous.4open.science/r/SDFlow-D6F3/*

Continuous-time dynamic graphs model evolving systems with irregularly timed interactions, where predicting future links requires reasoning over temporal evolution, local topology, and historical relation patterns. Existing memory-based and sequence-based methods capture temporal dependencies effectively, but they often organize historical interactions as node memories or serialized neighbor sequences. As a result, they lack explicit analysis of the interaction structure around candidate nodes which prevents them from explicitly modeling local topological patterns of candidate nodes at inference time. They also lack explicit causal statistical memory for activity, recency, and repeated pairwise interactions. Motivated by these drawbacks, we propose S3Former, a temporal graph framework that jointly models sequential dynamics, shared pair-centered structure, and online statistical memory. For each candidate interaction, S3Former encodes long-term and short-term historical sequences, constructs a history-induced shared pair subgraph around the two endpoints, and computes node-level and pair-level statistics from past events only. A two-level context-aware fusion mechanism further combines these signals: NodeCCF learns reliable endpoint representations, while PairCCF performs candidate-specific relation correction using explicit pair memory. Experiments on 10 dynamic graph benchmarks show that S3Former achieves state-of-the-art performance on most datasets in both transductive and inductive link prediction. Ablation studies confirm the effectiveness of shared pair-subgraph modeling, online statistical memory, and hierarchical fusion.

Genetic Programming (GP) is a search paradigm inspired from natural evolution for the automated discovery of expressions, functions, and computer programs. Cartesian GP (CGP) is a flavor of GP that uses a graph-based model to encode candidate programs, as contrasted to the conventional Tree-based GP (TGP). Since its inception two decades ago, CGP has predominantly been analyzed empirically, thus little is known regarding its theoretical performance guarantees. This paper analyzes CGP for the MAX problem, which has been only rigorously studied for TGP with a proven expected runtime superlinear in the optimal program length $n$. We prove that the expected time for the (1+1) CGP employing common mutation operators to evolve an equivalent optimal of the same output is polylogarithmic in n, specifically at most $O(\log^9{n})$. This is remarkable, as it shows an exponential speedup by switching to CGP. Our results shed light on the benefit and the compactness of the graph-based representation for programs, and on how CGP can navigate its search and fitness spaces efficiently. This is a first proven performance guarantee for CGP on an established benchmark problem. Experiments complement our theoretical findings.

Irregular multivariate time series (IMTS) are sampled at non-uniform timestamps and asynchronous across channels, their dependencies are shaped by time gaps, local ordering, and channel-specific sampling patterns. Building a faithful attention mechanism for IMTS exposes two limitations of existing work. First, current irregular-time attention methods typically regularize raw inputs into interpolated, aligned, or continuous-time-embedded forms before applying attention, which blurs exactly these irregular cues that determine how two observations relate. Second, beyond representation, standard dot-product attention scores pairs through learned token projections with no intrinsic tie to the shape of the underlying series, and an attention that does reflect this shape becomes costly to compute in high-channel IMTS. We propose Rubato, which reads two views directly from the raw irregular input: per-channel local dynamics, and a path signature that summarizes the joint multi-channel series. Attention in Rubato then scores pairs by a signature-kernel measure of series similarity rather than by a generic dot product, and a structured random projection (SORF) keeps this comparison affordable when the channel count is large. We prove that the structured projection introduces only a controlled bias that vanishes as the number of channels grows, and experiments on multiple IMTS benchmarks show that Rubato consistently outperforms strong baselines.

Data Streams are characterised as a potentially infinite source of data subject to non-stationarity. This non-stationarity, also called Concept Drift, often results in deployed predictive models having to be perpetually retrained as the underlying data generating process of the stream changes over time. Drift Detectors are a class of methods used to identify these changes, triggering these retraining procedures. Historically, Stream Learning has operated under the assumption that instances are only seen once, and cannot be stored for later use. Contemporary work relaxes this assumption yet drift detection and simple resetting procedures are still ubiquitous. Building on related work which has shown that traditional Batch Learning algorithms often outperform Stream Learning algorithms when instances can be stored, we introduce Time Neutralising Trees (TNT), a Decision Tree architecture that enables robust stream classification in settings with Concept Drift. During training, TNT will filter out ("neutralise") old training samples that are no longer relevant given the current context of the stream. If those instances become relevant again, TNT will then re-include them. We evaluate TNT, and related algorithms on 24 real and semi-real classification data streams from the USP DS repository. Results show strong evidence that TNT achieves state-of-the-art performance on a wide-range of data streams with varying concept drift types.

Post-hoc residual correction can reduce forecasting error, but always-on updates often fail to generalize: when residual signals are weak or noisy, expressive correctors can overfit and even increase the overall forecasting errors. We introduce CRC, a robust residual correction framework that couples a hybrid corrector with a validation-calibrated selective deployment rule. CRC proposes corrections via a conservative ridge "floor" plus conditional nonlinear refinement, but deploys them only when held-out evidence indicates net error reduction over a frozen baseline forecaster. This reframes correction as a decision problem: when to revise a forecast, not only how to fit residuals. Across standard long-sequence benchmarks and multiple backbones, CRC improves horizon-averaged MSE and MAE in most dataset-backbone pairs, with particularly large gains on complex datasets (e.g., over 20% relative MSE reduction on Traffic). We additionally report the non-degradation rate (NDR) as a stability diagnostic of harmful updates and ablate the validation-calibrated "firewall" that stabilizes MSE gains. Formal analysis motivating the design appears in Appendix A under idealized assumptions.

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

Learning directed causal graphs from time-series data poses significant challenges, especially in fMRI where slow sampling rate obscures fast neural interactions. This temporal mismatch leads to undersampling, which can make multiple graphs equally plausible. We address this problem by explicitly modeling undersampling effects when recovering causal graphs. Our approach employs answer set programming (ASP) to enforce domain-specific constraints and optimize soft observational constraints, thereby identifying a Markov equivalence class for the resulting graph solutions. By customizing an ASP solver to collect multiple near-optimal solutions, we obtain not only the single best-fitting graph but an equivalence class of high-scoring graphs for expert consideration. This method, called Real-world noisy RASL (RnR), can also act as a meta-solver: it refines the output of other causal discovery algorithms by accounting for undersampling biases. In synthetic data and empirical brain network data, RnR produces more accurate causal graphs than state-of-the-art methods. When applied as a meta-solver (refining outputs of existing algorithms), it improves F1 scores by an average of 46\% over baseline methods; on standalone synthetic data benchmarks, it achieves a 64\% improvement. We demonstrate that RnR is robust to varying undersampling rates, maintaining high precision and recall even as sampling becomes more sparse, whereas baseline methods degrade significantly. Finally, we test RnR on real-world settings where ground truth connectivity is unknown such as human brain fMRI data, showing that incorporating undersampling-aware constraints via ASP yields more reliable and interpretable brain connectivity estimates from fMRI time series, bridging the gap between neural dynamics and observational data.

Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Despite the large number of anomaly detection methods proposed in the literature, recent benchmark studies have shown that no single detector performs best across highly heterogeneous time series. Therefore, a practical and scalable solution is to develop a model-selection method that, for a given time series, selects the anomaly detector most likely to perform well. Nevertheless, the model selection approach proposed in the literature suffers from a significant drop in accuracy when applied in Out-of-Distribution (OOD) settings. In this paper, we tackle the aforementioned limitation and propose \textbf{\textsc{RAMSAD}}, a train-free, retrieval-based framework for model selection in time-series anomaly detection. Given a new time series, our method queries a knowledge base of previously observed instances, retrieves the top-${k}$ most similar series, and transfers detector recommendations from their performance profiles. The framework can operate with standard similarity measures as well as embedding-based representations. Overall, we demonstrate that similarity-based retrieval constitutes a strong and efficient foundation for model selection in time-series anomaly detection.