NeurIPS 2026 论文列表

Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, but unlike LLMs they cannot consume multimodal, non-numerical context that often shape real-world trajectories. In this work, we bridge this gap and argue for a multimodal time-series forecasting approach that post-trains LLMs to act as context-guided revisors over strong numerical TSFM priors. We introduce PostTime, a post-training recipe combining Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), along with a methodology to generate automated reasoning traces for forecast revisions. PostTime teaches an LLM to generate context-conditioned forecast interventions—decisions to revise, preserve, or ignore the TSFM prior based on the multimodal context. We evaluate this approach on the TimesX multimodal forecasting benchmark using a Gemma-3-4B LLM and TimesFM-2.5 TSFM, and show that it significantly outperforms standalone TSFMs, LLM-only baselines, and existing multimodal forecasting approaches. Our post-training recipe including code and dataset, can currently be accessed for review purposes at \url{https://anonymous.4open.science/status/posttime-21BF/}.

Test-Time Training (TTT) enables long-context processing via continuous weight updates during inference, but current methods struggle to balance the expressivity of per-token update dynamics with the hardware efficiency of chunk-wise approximations. We propose E$^2$-TTT (Expressive and Efficient TTT) to bridge this gap. By deriving a closed-form state transition that exactly aggregates per-token momentum and decay coefficients within a chunk, E$^2$-TTT enables fully parallelized chunk-level training while preserving the temporal structure of the update rule that prior chunk-wise methods discard. We validate E$^2$-TTT by training models up to 1.3B parameters from scratch. Extensive experiments demonstrate that our method consistently outperforms previous TTT and hybrid attention baselines in language modeling and retrieval, while achieving significantly better extrapolation on the standard ``Needle in a Haystack'' test, maintaining $>90$ % accuracy on passkey retrieval at $8\times$ the training context length. Meanwhile, E$^2$-TTT can match the training throughput of efficient chunk-wise methods, demonstrating that it effectively reconciles expressivity with efficiency.

Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that all labelers share the same underlying preferences, ignoring the fact that real-world labelers are highly heterogeneous and usually anonymous. Consequently, relying solely on binary choice data fundamentally distorts the learned policy, making the true population-average preference unidentifiable. To overcome this critical limitation, we demonstrate that augmenting preference datasets with a simple, secondary signal—the user's response time—can restore the identifiability of the population's average preference. By modeling each decision as a Drift-Diffusion Model (DDM), we introduce a novel, consistent estimator of heterogeneous preferences that successfully corrects the distortions of standard choice-only labels. We prove that our estimator asymptotically converges to the true average preference even in extreme cases where each anonymous labeler contributes only a single choice. Empirically, across both synthetic and real-world datasets, our method consistently outperforms standard baselines that otherwise fail and plateau at a bias floor. Because response times are essentially free to record and require zero user tracking or identification, our results bring promises and open up new opportunities for future data-collection pipelines to improve the social benefit without requiring user-level identifiers or repeated elicitations.

Ridge crossings, where two components share the same instantaneous frequency (IF) at a given time, are a fundamental bottleneck in multi-component time-frequency (TF) mode decomposition. We trace this difficulty to \emph{insufficient representational dimensionality} and resolve it by lifting signals into the 3-D time-frequency-frequency-rate (TFFR) space $(t,f,\dot{f})$, where crossing ridges separate because they typically differ in the instantaneous frequency rate (IFR). We formalize this via a separability proposition and confirm it empirically through Monte Carlo simulation (crossing rate: 78.6\% in 2-D $\to$ 0.9\% in 3-D). Based on this principle, we propose \textbf{TFFR-Net}, a query-based masked transformer that jointly predicts per-ridge masks and dense IFR maps. An IFR feedback loop injects the estimated frequency rate back into each query embedding, enabling the decoder to perform instance discrimination in TFFR space rather than the TF plane. On a synthetic benchmark with up to six chirp components under broadband noise (0--25\,dB SNR), TFFR-Net consistently reduces IF MAE by at least 5.8\% and Dice loss by at least 5.4\% relative to competing methods. Evaluation on real-world signals (bat echolocation, power-system oscillation, earthquake vibration) demonstrates effective generalization beyond synthetic training data. The source code is available at \url{https://anonymous.4open.science/r/TFFR-92D2/}.

Empirical Risk Minimization treats training samples uniformly, which can degrade performance in noisy or heterogeneous settings. We introduce a reference-guided objective that uses per-sample error comparisons with a fixed reference model to reweight gradient contributions without relying on prediction imitation, while preserving gradient direction and ensuring bounded scaling. Theoretical analysis shows that the formulation induces adaptive weighting with controlled amplification. Experiments on image classification with synthetic label noise and on time-series forecasting demonstrate broad improvements across architectures, particularly in moderate-to-high noise and heterogeneous regimes. Results further indicate that the method is most effective when the error-ranking alignment between the baseline model and the reference model is low, suggesting that gains arise primarily from the diversity of the reference error ranking relative to the learner, rather than the reference's absolute performance.

Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup that underfits heterogeneous regimes. Replacing the head with a mixture of experts is the standard upgrade, but on instance-normalized backbones (the dominant TSFM design class) it fails: routing entropy collapses to zero and one expert absorbs every input, a failure we call *normalization-induced routing collapse*. Standard MoE rescue mechanisms do not repair it, because the cause is in the router's input, not its optimization. Pre-encoder normalization strips the statistics a router would need to tell regimes apart. A mutual-information decomposition makes this precise and yields a signal-ratio that, computed before training, predicts dataset vulnerability (Spearman $\rho = -0.88$). Eight causal controls, including a vision-modality replication, isolate instance normalization as the cause. The prescription is a minimal causal intervention: *Raw-Routed Mixture of Adapters* (RR-MoA), which routes on the raw, pre-normalization input. Under a strictly frozen backbone, RR-MoA wins 54/54 comparisons against the strongest fixed adapter and significantly outperforms LoRA, TRACE, AdaMix, and full fine-tuning. The effect generalizes across six backbones and an imputation task. Frozen RR-MoA also beats full fine-tuning by 12–79% (the *Frozen Paradox*); two architecturally distinct variants confirm the principle generalizes beyond this specific router. Code is provided in the supplementary material.

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

Noise search at inference time improves text-to-image diffusion quality but requires many multi-step teacher rollouts per query. Distilled few-step models are now routinely released for efficient inference, but their potential as proxies inside noise search is uncharacterised. We measure proxy--teacher rank agreement across three distillation regimes and three backbones spanning DiT, MMDiT, and UNet, and find a sharp asymmetry: eight closed-form image statistics preserve rank from the distilled proxy to the teacher (Spearman $\rho$ up to $0.80$), while learned semantic scorers decorrelate ($\rho \le 0.12$). Null controls localise the transfer to distillation training rather than few-step denoising or initial noise characteristics. We therefore reformulate noise search as a \emph{joint constraint} and propose ProxySearch: the proxy filters candidates by statistical match in $1$ step, the teacher argmaxes a user-chosen semantic reward over the survivors without a tunable weight trading semantic reward against the statistical target. \textsc{ProxySearch} matches or beats teacher-only argmax on compositional accuracy and human preference, while tightening the statistical target by $\sim 23\%$ in paired statistical $\ell_1$ relative to teacher-only argmax at matched compute on FLUX-$1024$.

Beam-search–based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early, leading to significantly improved reasoning efficiency and more favorable test-time cost scaling. Despite strong empirical success, the theoretical understanding of beam search remains limited. In this paper, we study the test-time compute guarantee of the commonly used beam search framework that uses the model's internal log-likelihood for intermediate scoring, while relying on an external reward model only after a complete response is generated. We first establish a lower bound for vanilla beam search, showing that at least $\Omega(C^\star(x)^2)$ samples are required for the optimal response to survive, where $C^\star(x)$ is the token-level coverage coefficient for prompt $x$. This motivates our modified confidence-filtered beam search (CF-Beam), which provably reduces the sample size by a factor of $C^\star(x)$. We then show that the regret of CF-Beam is upper-bounded by the probability of rare failure events and the reward estimation error scaled by a path-level coverage coefficient, where the rare-failure term vanishes as per-step sampling increases. Our results highlight a fundamental advantage of beam search over sequence-level inference methods such as Best-of-N and Best-of-Majority. While the guarantees of these approaches typically involve coverage coefficients that grow exponentially with the horizon $L$, CF-Beam controls the dominant search-induced term through a token-level coverage coefficient that scales polynomially with $L$. Our numerical experiments further confirm that beam search is more robust on hard instances and under increasing reasoning horizons.

Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors. We ask whether this failure mode can be addressed by making the bottom-up input to each layer prospective, replacing instantaneous layer inputs with local look-ahead signals that compensate for integration-induced lag. We develop Recursive Quadrature Filters (RQFs), a biologically motivated class of complex-valued temporal filters that remain equivalent to single-channel diagonal state-space models (SSMs). This equivalence makes RQF layers trainable with the same parallel scan and convolutional algorithms used for diagonal SSMs. At the implementation level, this prospective input amounts to a lightweight two-tap input update, making it a drop-in modification for RQFs and SSMs. For the resulting discrete-time network, we prove that spatial-only backpropagation in a deep RQF network with instantaneous bottom-up inputs yields error signals that decay geometrically with depth, whereas prospective-input coding restores order-one gradient flow. We evaluate prospective-input coding on the Speech Commands dataset using standalone RQF recurrent stacks across two feature representations, mel-frequency cepstral coefficients (MFCCs) and raw audio, and local and non-local credit-assignment strategies. Under spatial-only backpropagation, prospective-input coding improves validation accuracy from 65.7% to 84.9% on MFCC features and from 46.4% to 61.0% on raw audio. Under full backpropagation through time, the gains persist, improving accuracy from 89.4% to 93.2% on MFCC features and from 80.1% to 82.7% on raw audio. Taken together, our results identify prospective-input coding as a local, broadly applicable mechanism for improving credit assignment in multi-layer recurrent networks with a continuous-time substrate.

We study the problem of computationally efficient **proper agnostic learning** of high-dimensional concept classes under the Gaussian distribution. In this setting, given i.i.d.\ labeled samples from an unknown distribution over $\mathbb{R}^d \times \{\pm 1\}$ whose marginal on $\mathbb{R}^d$ is Gaussian, the goal is to output a hypothesis from a target class $\mathcal{F}$ whose 0-1 loss is within $\varepsilon$ of that of the best classifier in $\mathcal{F}$. We give the first efficient proper agnostic learning algorithm for arbitrary Boolean functions of $K$ halfspaces under Gaussian marginals. Our algorithm runs in time $d^{O(K^2 \log(1/\varepsilon)/\varepsilon^2)} + (K/\varepsilon)^{O(K^3/\varepsilon^{2.5})}$. Prior to our work, the only known algorithm for $K \geq 2$ was brute-force search, with runtime exponential in $d$. Moreover, the dependence of our runtime on the dimension $d$ matches that of the best known \emph{improper} learning algorithm, namely $d^{\widetilde{O}(K^2/\varepsilon^2)}$. For the special case of a single halfspace ($K=1$), the best previous runtime was $d^{O(1/\varepsilon^4)} + (1/\varepsilon)^{O(1/\varepsilon^6)}$.\ Our algorithm improves this to $d^{O(1/\varepsilon^2)} + (1/\varepsilon)^{O(1/\varepsilon^{2.5})}$. Once again, the dependence on $d$ matches that of the best known improper algorithm, namely $d^{O(1/\varepsilon^2)}$. Furthermore, the dependence of our runtime on the dimension $d$ is essentially optimal in the statistical query model.

Activation steering controls language model behavior by adding directions to internal representations at inference time, but standard residual-stream steering can fail in stateful dialogue. We identify KV-cache contamination as a key failure mode: steered token states are stored and repeatedly reused, turning a local perturbation into cumulative coherence degradation. Motivated by the stability of prompt-based control, we propose Gated Cropped Attention-Delta steering (GCAD), which extracts steering signals from system-prompt contributions to self-attention and applies them with token-level gating. Across persona-steering experiments, GCAD preserves trait control while substantially improving long-horizon coherence. On the main multi-turn benchmark, GCAD improves average coherence drift from -18.6 to -1.9 and raises turn-10 trait expression from 78.0 to 93.1. These results suggest that activation steering becomes more reliable when interventions follow the prompt-mediated pathways that models already use for behavioral control.

Time-series carry structure simultaneously at multiple scales (fine-grained variation, mid-range motifs, and global properties) and downstream tasks operate at correspondingly different scales. Most existing self-supervised learning approaches supervise representations globally via instance-level contrastive losses and limited temporal neighborhood supervision, but do not explicitly exploit the structural hierarchy. We propose a learning framework that explicitly enforces a structural hierarchy across three scales independently: a local objective for token continuity, a mid-range objective for window-level motifs, and a global objective for sequence-level agreement. Realizing this framework requires the backbone to expose a representation at each scale; we introduce \textbf{Progressive Memory Transformer} (PMT), which augments a transformer with writable, window-aligned memory that exposes the mid-range scale alongside the token and sequence-level representations conventional transformers already provide. Across seven UCR/UEA/UCI classification benchmarks, a cue-retention probe, and two forecasting benchmarks, PMT learns representations that probe well at the global, mid-range, and local scales---strong low-label classification (1--5\% labels), competitive forecasting performance across multiple horizons, and quantitative and qualitative evidence that memory states capture mid-range motifs.

Many online learning systems now have access to several gradient predictors at once, such as simulators, replay buffers, neural surrogates, or time-series forecasters, and the quality of these predictors varies from round to round. We study online convex optimization under one-gradient feedback against an arbitrary moving comparator, with a user-supplied finite class $\Pi$ of gradient-field predictors, and ask whether a single algorithm can compete with the best predictor in $\Pi$ at a logarithmic cost, fall back to a small-loss bound when none of the predictors is useful, and still retain a path-length-adaptive worst-case guarantee when the predictors are adversarial. We give a positive answer through PH-Sword (Predictable-Hybrid Sword.optimism), which combines an exp-concave Hedge over $\Pi$, optimistic projected-gradient base learners on a geometric learning-rate grid, and a correction-term optimistic-entropy meta learner with prediction-error doubling. For any comparator sequence $\boldsymbol u$, PH-Sword attains dynamic regret $\widetilde O\bigl(R\_0\sqrt{(1+P\_T/D)(1+P\_T/D+\min\{\mathcal E\_\*/G^2,\,2LF\_T(\boldsymbol u)/G^2\}+\log|\Pi|)}\bigr)$ up to logarithmic factors, where $R\_0=DG+LD^2$, $P\_T$ is the path length, $F\_T(\boldsymbol u)$ the cumulative comparator loss, and $\mathcal E\_\*$ the best-in-class field error. The bound merges the field-controlled, small-loss, and worst-case regimes into one deterministic guarantee that improves over prior one-gradient dynamic-regret algorithms. On six controlled benchmarks the five predicted scaling laws hold simultaneously: PH-Sword reaches 17% of OGD's final dynamic regret and 54% of Ader's on a slow-drift benchmark, and we observe that a single noisy predictor without model selection can actually do worse than using no predictor at all.

Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performance. Existing diffusion-based methods typically condition the reverse process using local contextual information from the current or neighbouring windows. Meanwhile, global dataset-level structure often remains implicit, limiting performance when local observations are sparse, noisy, or unrepresentative. To address this issue, we propose ProCTI, a diffusion-imputation framework that augments local conditioning with retrieved global dataset-level priors through learned prototypes. A hybrid conditioning mechanism integrates this global context with local signals during reverse diffusion, enabling more accurate reconstruction under varying missingness scenarios. Experiments across multiple benchmark datasets show that ProCTI outperforms strong baselines overall under random missingness, while remaining competitive under attribute-wise missingness. We further introduce a theoretical framework that distinguishes between local and global conditioning in diffusion-based imputation, showing that combining both can reduce conditional uncertainty and improve imputation quality.

Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, including oscillations and high-frequency variations. Flow Matching (FM) offers an efficient alternative to diffusion models, but practical implementations typically rely on a single finite-capacity global vector-field estimator. In such heterogeneous temporal distributions, distinct regimes may pass through nearby flow states while requiring incompatible conditional velocities. A monolithic estimator trained with the standard $\ell_2$ velocity-matching objective may therefore learn an overly smoothed approximation of the local transport field. This estimator-level smoothing can attenuate branch-specific dynamics, leading to spectral distortion and poor mode coverage. To address this, we propose PrismFlow, a new FM method with Koopman-inspired dynamical experts. Each expert learns residual corrections in a latent space where local nonlinear temporal evolution can be approximated by linear transitions. We further propose a confidence-aware Winner-Take-All (WTA) objective that updates only the expert best aligned with each sample while masking gradients to the others, encouraging mode-specific specialization. During sampling, the selected expert adds a residual dynamical correction to the global transport field, preserving FM stability while recovering fine-grained and high-frequency temporal structures. Across various benchmarks, PrismFlow effectively mitigates the spectral contraction in standard FM and achieves state-of-the-art performance, with a 15.6\% gain in Context-FID and a 38.6\% improvement in Discriminative Score, while remaining robust in low-data settings and effective for forecasting and imputation.

Dynamic MRI reconstruction from highly undersampled k-space is a severely ill-posed inverse problem that requires strong measurement consistency, fine spatial recovery, and coherent temporal dynamics. Diffusion priors provide useful population-level regularization, but task-matched spatiotemporal diffusion models are costly to train, while generic pretrained priors may be mismatched to protocols, vendors, or anatomies, leading to hallucination or over-smoothing. Untrained test-time priors such as Time-Dependent Deep Image Prior (TD-DIP) adapt to each scan and naturally couple with data consistency, but can drift toward aliasing artifacts and overfit noise. We propose Geometry-Gated TD-DIP for this weak-diffusion regime, where diffusion is used as a local geometric reference rather than a generative driver. Each TD-DIP update is decomposed into a diffusion-consistent tangent component and a measurement-supported orthogonal innovation component. A self-calibrated gate adaptively regulates the innovation to counter prior mismatch while suppressing noise amplification. We further provide a mismatch-aware analysis showing descent of a local posterior surrogate under explicit conditions. Experiments on cine MRI show consistent gains over diffusion-only, TD-DIP-only, and additive hybrid baselines, especially at high acceleration and under out-of-distribution diffusion priors.

Time-series classifiers deployed in risk-sensitive domains such as healthcare, wearable sensing, and industrial monitoring must be accurate, interpretable, and capable of abstaining when evidence is weak. No existing method delivers all three, and the obstacle is structural, rather than logistical. Meaningful temporal patterns cannot be defined without knowing segment boundaries, yet meaningful boundaries cannot be placed without knowing which patterns to expect. Existing approaches break this circularity through fixed windows or expert-annotated vocabularies, sacrificing at least one of the three properties. We introduce ConceptTime, which closes the loop through a single self-supervised signal. A frozen probabilistic forecaster predicts a distribution over the next observation at every timestep of the sequence. We call the mismatch between its prediction and the realized signal, the predictive surprise. This signal both locates segment boundaries and characterizes each resulting segment through the forecaster's own predictive statistics. Segments are thus grounded by \emph{how} they behaved relative to expectation, not by what they look like. These summaries cluster into a vocabulary of dynamical regimes that we call concepts. The vocabulary distinguishes calm-after-spike from calm-after-calm, and unifies visually distinct segments that violate expectation in the same way. A lightweight head classifies from the concept sequence, and the distance to the nearest concept yields per-input reliability for free. Across seventeen UEA, human-activity, biomedical, and fault-diagnosis benchmarks, ConceptTime matches state-of-the-art black-box accuracy, beats every interpretable baseline, and retains near 98% accuracy on Epilepsy with 1% of labels. It equals or exceeds softmax, prototype-distance, and SHAP/TimeX/TimeX++ on OOD detection and deletion-faithfulness, without a single annotation, language-model call, or domain expert.

Real-time quantum error correction (QEC) is a critical bottleneck for fault-tolerant quantum computing due to strict hardware latency constraints. While neural decoders achieve state-of-the-art logical accuracy, their reliance on high-precision, compute-intensive inference precludes real-time deployment. Conversely, uniformly quantizing these models to extreme low-bit regimes for FPGAs triggers a catastrophic collapse in decoding accuracy. To overcome this precision-accuracy bottleneck, we propose Precision Pyramid ($\mathsf{PP}$), a hardware-algorithm co-designed neural decoder applicable to both surface and BB codes. $\mathsf{PP}$ features a monotonically escalating activation precision hierarchy built upon a globally weight-binarized (W1) foundation. By scaling intermediate activations down to INT2 for the massive perception stages and reserving higher precision strictly for the lightweight final logical decision, this hierarchy effectively shifts over $99\%$ of the computational workload to abundant FPGA look-up tables. Comprehensive evaluations on both simulated and hardware-calibrated noise data demonstrate that $\mathsf{PP}$ consistently suppresses logical errors below highly optimized classical baselines (e.g., MWPM and Relay-BP) while seamlessly satisfying stringent sub-microsecond latency budgets.