NeurIPS 2026 论文列表

Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored to different training steps. OATS leverages valuable training samples as principled guiding signals and dynamically generates high-quality synthetic data conditioned on them. We further design a diffusion-based framework to produce realistic time series. Experiments on TSFMs demonstrate that OATS consistently outperforms regular training and yields substantial performance gains over static data augmentation baselines across six validation datasets and two TSFM architectures. The code is available at the link https://anonymous.4open.science/r/OATS-536E.

Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answers may be more appropriate than binary true or false classifications. In all cases, faithful explanations of the considerations determining the final verdict are crucial. We introduce inference-time argumentation (ITA), a trainable neurosymbolic framework for ternary claim verification in which a formal argumentation semantics giving the strength of claims is used both (i) to guide LLM training as models learn to generate arguments and assign them base scores (representing intrinsic strengths) and (ii) to compute ternary (true/false/uncertain) predictions from generated, scored arguments. As a result, at training time, argument generation and scoring can be optimised according to the quality of the induced argumentative predictions. Moreover, at inference time, the final prediction is faithful, by construction, to the arguments and scores determining the verdict, rather than being justified by a potentially unfaithful post-hoc reasoning trace as in conventional reasoning models. We finally show that, on two datasets for ternary claim verification, ITA improves upon argumentative baselines and can perform competitively against non-argumentative direct-prediction baselines, while providing verdicts that are computed deterministically from explicit, inspectable argumentative structures.

Functional data such as time series, trajectories, and fields are increasingly central in machine learning and are naturally modeled in infinite-dimensional Hilbert spaces. We study Semi-dual Neural Optimal Transport (SNOT) for learning transport maps between probability measures on separable Hilbert spaces. A critical challenge in this setting is the spurious solution problem: a neural map can globally optimize the SNOT objective while failing to recover an optimal transport map. We show that this failure arises from non-regular source measures, which make the inner minimization in the semi-dual objective non-identifiable. We prove that regular source measures, defined via Gaussian null sets, restore inner-minimizer uniqueness and recover the Monge map. For singular sources, we introduce Gaussian smoothing and establish a necessary and sufficient kernel condition for smoothing to restore regularity. We further prove plan-level consistency: as smoothing vanishes, every Wasserstein accumulation point of the induced optimal plans solves the original Kantorovich problem. Experiments on synthetic singular transports and real-world unpaired time-series imputation benchmarks validate the theory, achieving the best performance on several benchmarks and competitive performance on the remaining ones.

Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation loss can follow smooth power-law curves, individual downstream tasks exhibit diverse scaling behaviors: some improve monotonically, others plateau, and some even degrade with scale. We argue that predicting downstream performance from validation loss suffers from two limitations: averaging token-level losses obscures signal, and no simple parametric family can capture the full spectrum of scaling behaviors. To address this, we propose Neural Neural Scaling Laws (NeuNeu), a neural network that frames scaling law prediction as time-series extrapolation. NeuNeu combines temporal context from observed accuracy trajectories with token-level validation losses, learning to predict future performance without the limitations inherent in assuming a specific functional form. Trained entirely on open-source model checkpoints from HuggingFace, NeuNeu achieves 1.99\% mean absolute error in predicting model accuracy on 66 downstream tasks---a 44\% reduction compared to logistic scaling laws (3.56\% MAE). Furthermore, NeuNeu generalizes zero-shot to unseen model families, architectures, parameter counts, and downstream tasks. Our work suggests that predicting downstream scaling directly from data outperforms parametric alternatives.

Fractional differential equations (FDEs) excel at modelling systems with long-range memory, while stochastic differential equations (SDEs) capture inherent randomness. Existing neural differential equation models address either memory (Neural FDEs) or stochasticity (Neural SDEs), but a unified framework combining both with a learnable scalar memory exponent and an efficient adjoint remains absent. We introduce the Neural Fractional Stochastic Differential Equation (Neural FSDE), which learns continuous-time dynamics with both memory and randomness from data via a Caputo FSDE with neural drift, neural diffusion, and a learnable fractional order. To enable scalable training, we derive a discrete adjoint method that computes gradients with respect to the initial state, the drift and diffusion network parameters, and the fractional order, with memory cost independent of the autograd graph depth. We validate the framework on two real-world domains. On the stochastic-fractional COVID-19 model of Bonyah et al., the NeuralFSDE provides the first end-to-end real-data calibration under a rolling-origin forecasting protocol, recovering a fractional order less than 1. On S&P~500 index option pricing, we recast rough Bergomi, rough Heston, Neural SDE, and NeuralFSDE as Caputo-FSDE special cases under a single calibration pipeline; the NeuralFSDE achieves the lowest implied-volatility error across schedules and instruments, with a matched-architecture comparison against the memoryless Neural SDE, isolating the contribution of fractional memory. Code is available at https://anonymous.4open.science/r/NeuralFSDE.

Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet existing approaches typically parameterize the reverse rate matrix monolithically---through proxies such as concrete scores (SEDD) or clean-data predictions (MDLM, GIDD)---rather than aligning the parameterization with the intrinsic CTMC decomposition into jump timing and jump direction. We propose \textbf{Neural CTMC}, which exploits the underlying Poisson structure of CTMC dynamics by separately parameterizing the reverse process through an \emph{exit rate} (when to jump) and a \emph{jump distribution} (where to jump) via two dedicated network heads. We show that the evidence lower bound (ELBO) reduces to a path-space KL divergence between the true and learned reverse processes that factorizes into a Poisson KL for timing and a categorical KL for direction, and admits a tractable, gradient-equivalent and consistent loss. Experimentally, scored by Gemma2-9B, our pure-uniform Neural CTMC achieves $16.36$ generative perplexity on TinyStories (vs.\ GIDD $37.60$ and MDLM $42.66$). On OpenWebText, it attains the best perplexity at the same training-token budget across 16--128 sampling steps among the methods we compare (e.g., at 128 steps: Neural CTMC $183.6$ vs.\ MDLM $210.5$ and GIDD $249.8$).

We study the adversarial kernel bandit problem, in which the loss at each time step is induced by some bounded (but otherwise arbitrary) element of an RKHS. We propose an exponential-weights algorithm with a regularized importance-weighted estimator and an explicit correction term that controls the estimator's regularization bias. Our main result bounds the regret in terms of a widely-adopted notion of effective dimension that captures the complexity of the kernel. Notably, when the kernel satisfies a polynomial eigendecay condition with exponent $\beta>1$, our regret scales as $T^{(\beta+1) /(2 \beta)}$ up to logarithmic factors. This matches a lower bound from existing work (Chatterji, Pacchiano, and Bartlett, ICML 2019) and strictly improves on the upper bound derived in that work (with dependence $T^{\beta /(2 (\beta-1))}$), while also having the benefit of dropping their rank-one adversary assumption and instead allowing arbitrary RKHS elements at each step.

We present GenusSink, a new class of approximate generalized Sinkhorn algorithms with shortest-path-distance costs for bounded genus (e.g. planar) graphs, providing near-linear time: (1) pre-processing, (2) iteration step, (3) final transport plan matrix querying and near-linear memory. Graphs handled by GenusSink include in particular planar graphs and bounded-genus meshes approximating 3D objects. GenusSink addresses total quadratic time complexity of its brute-force counterpart by leveraging separator-based decomposition of graphs, computational geometry techniques, and new results on fast matrix-vector multiplications with generalized distance matrices, using in particular Fourier analysis and low displacement rank theory. It is inspired by recent breakthroughs in graph theory on approximating bounded genus metrics with small treewidth metrics minor. The graph-centric approach enables us to target optimal transport problem with the corresponding distributions defined on the manifolds approximated by weighted graphs and with cost functions given by geodesic distances. We conduct rigorous theoretical analysis of GenusSink, provide practical implementations, leveraging newly introduced in this paper separation graph field integrators (S-GFIs) data structures and present empirical verification. GenusSink provides orders of magnitude more accurate computations than other efficient Sinkhorn algorithms, while still guaranteeing significant computational improvements, as compared to the baseline. As a by-product of the developed methods, we show that GenusSink is $\textbf{numerically equivalent}$ to the brute-force geodesic Sinkhorn algorithm on n-vertex graphs with treewidth $O(\log \log (n))$ (e.g. on trees).

We present NPUsper, a live transcription system that makes Whisper efficient on mobile NPUs by eliminating redundant computation. To avoid the heavy padding used by prior streaming systems, NPUsper detects hallucinated tokens online from temporal patterns in decoder cross-attention, allowing each inference round to process short audio inputs with minimal carryover. For efficient mobile-NPU execution, we propose controlled unrolling, which executes autoregressive decoding as K-step chunk graphs, removing unnecessary KV-cache computation and reducing graph-dispatch overhead. NPUsper achieves up to 4.84x lower per-word latency, up to 33.2x lower time-to-first-token (TTFT), and up to 11.36% lower average power consumption compared with baselines, while maintaining comparable transcription accuracy. The code is available at https://github.com/npusper/NPUsper.

Learning effective representations from battery operational history is essential for predictive health management and control-oriented applications. Existing approaches rely on response-dependent engineered features or short early-life windows, limiting generalisability and precluding use in prospective settings where future responses are unavailable. We propose $\texttt{NOCE-Net}$, a deep nested sequence model that learns control-enabling representations directly from raw cyclic operational data. Our data pipeline, $\texttt{re-BatteryData}$, standardises heterogeneous aging datasets into semantically equivalent Full Equivalent Cycles (FECs) defined by absolute charge throughput, enabling end-to-end learning across diverse operational regimes. $\texttt{NOCE-Net}$ combines multi-patch transformer encoders for intra-cycle dynamics with a GRU backbone for inter-cycle state evolution, operating on approximately $700$ batteries across ten public datasets spanning over $300$ operational profiles. In zero-shot evaluation on unseen battery chemistries and usage patterns, $\texttt{NOCE-Net}$ achieves $13.7$% MAPE in voltage response prediction, outperforming strong long-sequence baselines by over $43$% while using $40 \times$ fewer parameters. Shape and component diagnostics of the learned hidden states reveal that the GRU's principal direction of evolution achieves Spearman $|\rho| \geq 0.95$ with the capacity fade trajectory on average across all datasets with monotone degradation profiles, including zero-shot test batteries, providing evidence that the representations encode degradation dynamics transferable to downstream tasks beyond battery response modelling.

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.

We revisit the problem of computing mean-field equilibria (MFEs) in discrete-time, monotone, finite-horizon mean-field games (MFGs). We show that, when the transition kernel is independent of the state-measure term and the reward function satisfies the usual weak monotonicity condition and is Lipschitz continuous, anchored proximal gradient descent methods can be used to compute a monotone MFE. We also establish last-iterate convergence results for these methods. Our approach relies on formulating the computation problem as an optimization problem over the space of occupation measures. Using this formulation, we show that the problem is equivalent to a class of constrained Lipschitz monotone inclusion problems. We then apply iterative methods for this monotone inclusion formulation to derive a tractable algorithm. The resulting algorithm achieves a convergence rate of $O(1/\sqrt{T})$ after $T$ iterations, without requiring any regularization. This rate holds even in the absence of a uniqueness assumption for the corresponding MFE.

Flow matching has emerged as a powerful alternative to diffusion models for generative modeling, but standard conditional flow matching is fundamentally misaligned with partially observed time series: starting from an uninformed Gaussian source forces the model to transport even already-observed entries from noise to the target, wasting capacity on trivially known regions. We propose MissPath-FM, a partial-observation-aware flow matching framework that redesigns the source prior and probability path for incomplete time series. MissPath-FM employs structured source priors (interpolation priors and Gaussian process posterior priors) to initialize the flow close to the observed signal, paired with a missingness-aware smooth path that allocates transport asymmetrically between observed and missing regions. We evaluate systematically across four datasets, four missingness mechanisms (MCAR, block, MNAR, irregular), two missing ratios, and three sequence lengths ($T \in \{24, 48, 96\}$), totaling 48 settings against eight baselines. MissPath-FM wins 29 of 32 settings at $T{=}48$ and 45 of 48 overall (93.8\%). Our ablations reveal a regime-dependent design hierarchy in which no single component suffices: under random missingness the source prior yields a large initial gain, but under structured block missingness it can fail entirely and the learned flow becomes indispensable; the missingness-aware path contributes a further $12\%$--$26\%$ across all regimes. Only the full pipeline achieves robust performance across all 48 settings. Mechanistically, structured priors shorten observed-region transport by up to $15\times$, concentrating the velocity field on genuinely uncertain entries.

Spiking neural networks (SNNs) with learnable membrane time constants can improve temporal processing by adapting neuronal integration timescales, but they also turn the decay factor $\beta$ into an optimized dynamical parameter that is fragile under hardware-induced parameter mismatch. We develop a perturbation-based account of how time-constant variation affects trained SNNs. In controlled software simulations, this fragility is strongly task-dependent: Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) settings lose $7\text{--}20$ percentage points of accuracy under $20$% coefficient-of-variation (CV) perturbations in $\beta$, whereas DVS-Gesture and CIFAR-10 lose less than $3$ pp. We show that, under a surrogate-linearized first-order analysis, the failure mode is governed by the membrane sensitivity $\Psi$. This quantity admits a closed-form online recursion requiring only one additional state per layer and no extra temporal storage. The analysis identifies time-averaged sensitivity energy as the controllable term in deployment fragility, leading to $\textbf{MARS}$ ($\textbf{M}$embrane-$\textbf{A}$ware $\textbf{R}$obustness through $\textbf{S}ensitivity$ Regularization), a training objective derived from perturbation analysis rather than heuristic noise augmentation. We prove a conservative worst-case output-perturbation bound and introduce a typical-case diagnostic explaining why sensitivity remains predictive when worst-case constants are vacuous. At $20$% CV perturbation, MARS reduces the accuracy drop in the sensitive settings to at most $0.5$ pp while preserving nominal accuracy, with minimal effect on DVS-Gesture and CIFAR-10.

Biological visual systems compute reliably under tight metabolic and wiring constraints despite noisy inputs and stochastic circuit components. Artificial vision systems tend to invert both properties: They consume vastly more energy and can be sensitive to adversarial perturbations. Whether artificial networks with structural properties of real brain wiring are more robust at lower computational cost is unclear. We address this with FlyNet (Lappalainen et al. 2024), a task-optimized, continuous-time neural network model of the fly visual system constrained by its synaptic connectome. We benchmark it against CNN, RAFT (Teed and Deng, 2020), and a sparse continuous-time recurrent neural network without connectome constraints (CTRNN) on a looming-based collision-detection task within a differentiable 3D rendering pipeline. We find that FlyNet is more robust than CNN controls to flicker, unstructured noise, and motion jitter. Compared with RAFT controls, FlyNet is more robust to flicker and motion jitter but less robust to all gradient-based attacks. Yet, FlyNet uses up to five orders of magnitude fewer FLOPs than RAFT, achieving higher robustness on common video corruptions at far lower cost. FlyNet is less robust than CTRNN across all tested perturbations, suggesting that FlyNet's connectome constraints contribute to cost, not robustness. To probe FlyNet's failure modes, we use counterfactual restoration: textures equally far from training as adversarial textures that recover correct classification also recover position and velocity representations, ruling out unfamiliar textures as the cause of failure. Classical reverse-phi illusions degrade the same downstream position and velocity readouts, suggesting that both perturbations may exploit similar computational vulnerabilities. Together, these results show that FlyNet is robust for its computational cost on an ecologically important task, and indicate that different architectures specialized on the same task have specific robustness profiles across perturbation types. Our differentiable pipeline enables computational tests of this hypothesis and generates targeted adversarial stimuli for in vivo experiments.

Recent progress in multimodal, high-dimensional learning has enabled foundation models to process heterogeneous, large-scale data. However, at test time, acquiring all features or modalities can be prohibitively costly and often redundant. Sequentially selecting informative modalities is therefore critical, yet challenging when the downstream task or prediction target is unknown. To this end, we introduce ECHO-$k$, a task-agnostic and self-supervised learning principle for modality acquisition: we use a deep model’s internal pretrained representations (e.g., from a foundation model) as proxy targets that summarize cross-modal information. We provide theoretical guarantees in a linear model setting that motivate a reinforcement learning (RL) policy for sequential modality selection. Across benchmarks, the resulting acquisition strategies transfer reliably and achieve state-of-the-art performance on downstream tasks that are entirely unseen during selection. Our method provides a principled route to cost-aware test-time deployment, with implications for any multimodal system where measurements are expensive or time-constrained, and downstream tasks unknown a priori.

Existing unified 4D reconstruction and point tracking approaches typically rely on heuristic interpolations or just predict at integer timestamps, lacking kinematic coherence and failing to model dynamics at any arbitrary timestamp. In this paper, we propose Uni4R, a framework that unifies these tasks by learning continuous velocity fields through the synergy of Optimal Transport (OT) and Ordinary Differential Equation (ODE). Importantly, this continuous velocity field acts as a kinematic prior that mutually benefits both 4D reconstruction and point tracking. Specifically, we propose the Flow Matching Guided Decoder (FMGD). A global velocity branch first extracts anchor features that capture the global dynamic state of the sequence. Then, FMGD leverages Flow Matching (FM) theory to formulate a probability path defined by OT on the anchor feature manifold, instantiating it as FM-guided velocity features for velocity prediction. This establishes a robust kinematic inductive bias. Meanwhile, a point reconstruction branch provides geometric features. The local velocity prediction module then joint above features and time embeddings, to decode velocities at arbitrary timestamps. To overcome the absence of high-quality ground-truth velocities in fractional frames, we propose an integral-consistency training strategy. This strategy uses an ODE solver to integrate velocities to recover target pointmaps, enabling the model to be supervised end-to-end directly from integer timestamps. Experimental results demonstrate that Uni4R achieves SOTA performance in both 4D reconstruction and point tracking, and achieves SOTA in our new kinematics-aware benchmark at continuous time.

Spatial interpolation from sparse irregular observations is a recurring task across the environmental and health sciences. Neural processes amortise this inference into a single forward pass at test time. Joint-predictive variants capture correlations across query locations but typically rely on self-attention over context and query points, incurring $O(K^2)$ compute in the combined point count $K$ and capping practical use at a few thousand points. State-space models offer a linear-time alternative, but existing state-space NPs compress the context into a single hidden state before reading queries, reducing the information available to the decoder. We propose MambaFlowNP, a joint-predictive NP with linear compute trained with a conditional flow-matching objective. A multi-directional Mamba-2 scan reads context and query tokens in a single shared sequence, so each query's velocity is read from its own scan position rather than from a compressed context summary. MambaFlowNP is competitive with the strongest attention-based joint-predictive baselines on synthetic Gaussian-process benchmarks, and across regional and continental-scale NOAA temperature interpolation tasks it attains the lowest RMSE on every tier and the lowest CRPS at the California and continental US tiers, while running $170$--$760{\times}$ faster than the strongest joint-predictive baseline. A single forward pass scales to $10^6$ points on one GPU in under a second. This scalability unlocks amortised joint-predictive inference for continental-scale, irregularly sampled sensor networks, spanning climate monitoring to disease vector surveillance.

Mamba, a recently proposed linear-time sequence model, has attracted significant attention for its computational efficiency and strong empirical performance. However, a rigorous theoretical understanding of its underlying mechanisms remains limited. In this work, we provide a theoretical analysis of Mamba's in-context learning (ICL) capability by focusing on tasks defined by low-dimensional nonlinear target functions. Specifically, we study in-context learning of a single-index model $y \approx g_*(\langle \boldsymbol{\beta}, \boldsymbol{x} \rangle)$, which depends on only a single relevant direction $\boldsymbol{\beta}$, referred to as *feature*. We prove that Mamba, pretrained by gradient-based methods, can achieve efficient ICL via *test-time feature learning*, extracting the relevant direction directly from context examples. Consequently, we establish a test-time sample complexity that improves upon linear Transformers---analyzed to behave like kernel methods---and is comparable to nonlinear Transformers, which have been shown to surpass the Correlational Statistical Query (CSQ) lower bound and achieve near information-theoretically optimal rate in previous works. Our analysis reveals the crucial role of the *nonlinear gating* mechanism in Mamba for feature extraction, highlighting it as the fundamental driver behind Mamba’s ability to achieve both computational efficiency and high performance.