NeurIPS 2026 论文列表

We address the problem of evaluating machine learning models under a limited labelling budget in streaming environments, where data arrive sequentially. This setting is particularly critical for embedded and continuously deployed systems, such as autonomous driving or computer-aided diagnosis, where models must reliably assess their own performance online. We focus on a constrained online setting, where labelling decisions must be made immediately, and data cannot be stored. We introduce an \emph{online active testing} (OAT) framework based on an adaptive importance sampling estimator that provides unbiased risk estimation for both regression and classification tasks. While importance sampling has been extensively studied in pool-based settings, we establish novel theoretical ground for the fully online setting, including deviation bounds and consistency guarantees. We derive an optimal sampling strategy in order to minimise the estimator's variance, which is driven by the expected second moment of the loss. This strategy is expressed for common loss functions, including mean squared error, mean absolute error, 0–1 loss, and log-loss, and analysed from a statistical perspective. We empirically demonstrate the effectiveness of OAT on synthetic benchmarks, deep learning tasks, and a real-world application involving the evaluation of machine learning models for molecular simulations, where data are generated sequentially, and labels require expensive quantum chemistry computations, showing improved efficiency and reliable performance estimation under limited labelling budgets. In particular, on the real-world task, OAT requires only $\sim$30\% of labels to match the accuracy of a naïve uniform baseline, compared to $\sim$23\% for an oracle method that has access to the labels.

Visual signals, especially videos, are highly redundant: most regions are temporally predictable, while informative changes are sparse. We hypothesize that vision encoders should allocate computation according to this non-uniform information density rather than process dense pixel grids uniformly. From this perspective, codec-derived motion and residual signals provide a natural basis for identifying informative regions. OneVision-Encoder instantiates this idea through Codec Patchification, which replaces uniform dense computation with selective processing of only 3.1\%-25\% of regions that carry high signal entropy. To support irregular spatiotemporal token layouts, OneVision-Encoder employs a shared 3D RoPE and is pretrained with a large-scale cluster discrimination objective over more than one million semantic concepts, enabling unified representation learning for appearance and motion. Empirically, OneVision-Encoder improves both efficiency and accuracy under fixed token budgets. When integrated into large multimodal models, it achieves a 4.1\% average improvement over Qwen3-ViT on video understanding benchmarks while remaining competitive on image and document understanding. Under attentive probing, it further yields substantial gains on motion-sensitive benchmarks, improving Top-1 accuracy on Diving48 by 17.1 and 8.1 percentage points over SigLIP2 and DINOv3, respectively, at matched patch budgets. These results suggest that codec-guided patch sparsity is an effective design principle for scalable visual representation learning.

Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems, offering end-to-end joint optimization and lower serving overhead compared to multi-stage cascaded architectures. OneSearch, as a representative industrially deployed generative framework, has delivered substantial commercial benefits. However, three key limitations constrain further improvement: shallow understanding of complex queries, insufficient personalized intent reasoning over user context, and a separately trained reward model that adapts slowly to emerging queries and is prone to sampling bias and reward hacking. To address these challenges, we propose OneSearch-V2 with three key innovations: (1) a thought-augmented query understanding module that generates compact keyword-based chains-of-thought (CoTs), overcoming the shallow-matching limitation of single-pass SID generation; (2) a reasoning-internalized self-distillation pipeline that encodes keyword-guided reasoning into the existing model weights via information-asymmetric supervision, without any extra parameters, special tokens, or inference-time CoT generation; (3) a behavior-feedback preference alignment system that replaces the separate reward model with composite rewards built from real user interactions, and introduces a token-position marginal advantage (TPMA) mechanism for position-aware credit assignment over hierarchical SID sequences. Extensive offline evaluations demonstrate OneSearch-V2's strong query understanding and personalized intent modeling capabilities. Online A/B tests further validate its business effectiveness, yielding +3.98\% item CTR, +1.17\% PV CTR, +2.90\% PV CVR, +2.07\% buyer volume, and +2.11\% order volume. Manual evaluation further confirms gains in search experience quality, with +1.37\% in page good rate and +1.65\% in query-item relevance. Importantly, OneSearch-V2 achieves these gains without any additional inference cost or serving latency, while also mitigating information bubbles and long-tail sparsity.

Backpropagation is the cornerstone of deep learning, but its reliance on symmetric weight transport and global synchronization makes it computationally expensive and biologically implausible. Feedback alignment offers a promising alternative by approximating error gradients through fixed random feedback, thereby avoiding symmetric weight transport. However, this approach often struggles with poor learning performance and instability, especially in deep networks. Here, we show that a one-time soft alignment between forward and feedback weights at initialization enables deep networks to achieve performance comparable to backpropagation, without requiring weight transport during learning. This simple initialization condition guides stable error minimization in the loss landscape, improving network trainability. Spectral analyses further reveal that initial alignment promotes smoother gradient flow and convergence to flatter minima, resulting in better generalization and robustness. Notably, we also find that allowing moderate deviations from exact weight symmetry can improve adversarial robustness compared to standard backpropagation. These findings demonstrate that a simple initialization strategy can enable robust and effective learning in deep networks without dynamic weight transport, providing a resource-efficient alternative to standard backpropagation.

We propose a simple framework for constructing differentially private confidence regions _in one shot_, i.e., by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resample. The cost of privacy of our procedure is $O(\log B)$ under with-replacement ($m$-out-of-$n$) sampling and independent of $B$ under without replacement sampling (subsampling), avoiding the $\sqrt{B}$ factor that arises in previous works. We provide _nonasymptotic_ Gaussian Differential Privacy (GDP) and utility guarantees for both subsampling and $m$-out-of-$n$ resampling, covering mean-like estimators with small global sensitivity as well as estimators admitting efficiently computable _smooth sensitivity_ bounds, including quantiles and degenerate U-statistics. This allows us to also obtain private confidence regions for degenerate U-statistics where the private error is much smaller than the non-private error. In all, we provide a toolbox for widely applicable DP uncertainty quantification procedures under popular resampling strategies while avoiding the computational and privacy costs of privatizing many intermediate resample statistics.

We study dynamic measure transport for generative modeling, focusing on transport maps that connect a source measure $P_0$ to a target measure $P_1$ by integrating a velocity field of the form $v_t(x) = \mathbb{E}[\dot X_t \mid X_t = x]$, where $X_{\bullet} = (X_{t}) _{t}$ is a stochastic process satisfying $(X _{0}, X _{1}) \sim {P _{0}} \otimes {P _{1}} $ and $\dot X _{t}$ is its time derivative. We investigate when $X _{\bullet}$ induces a _straight-line flow_: a flow whose pointwise acceleration vanishes and is therefore exactly integrable by any first-order method. First, we develop multiple characterizations of straight-line flows in terms of PDEs involving the conditional statistics of the process. Then, we prove that straight-line flows under endpoint independence exhibit a sharp dichotomy. On the one hand, we construct explicit, computable straight-line processes for arbitrary Gaussian endpoints. On the other hand, we show that straight-line processes do not exist for targets with sufficiently well-separated modes. We demonstrate this obstruction through a sequence of increasingly general impossibility theorems that uncover a fundamental relationship between the sample-path behavior of a process with independent endpoints and the space-time geometry of this process' flow map. Taken together, these results provide a structural theory of when straight-line generative flows can, and cannot, exist.

One-shot Federated Graph Learning (OFGL) has emerged as a communication-efficient paradigm for Federated Graph Learning (FGL) under strict bandwidth constraints, compressing collaboration into a single transmission round. Nevertheless, this extreme setting suffers from a fundamental **Single-Transmission Bottleneck**, which limits both the quality and utility of exchanged knowledge. First, while existing methods attempt to distill private graphs into shareable proxies, their aggressive one-shot compression inherently triggers a fidelity crisis. This compression distorts local graph topology and semantics across clients, leading to **externalized knowledge underexpression**. Second, although the server aggregates these proxies to update global models, current integration schemes suffer from transferability agnosticism. The server cannot reliably identify cross-client collaboration potential, resulting in **integrated knowledge underutilization**. To tackle these dual challenges, we propose FedHFT, an effective One-shot Personalized Federated Graph Learning (OPFGL) framework that addresses this bottleneck from two perspectives, namely High-Fidelity Knowledge Externalization and Transferability-Ware Knowledge Integration. We use Fidelity-Driven Proxy Refinement to preserve client-side knowledge fidelity, and Transferability-Steered Personalized Collaboration to enable adaptive server-side collaboration. The superiority of FedHFT is validated through extensive experiments on both homophilic and heterophilic graphs. The code is available at https://anonymous.4open.science/r/FedHFT-DC7F/.

Chain-of-Thought (CoT) enables transformers to solve complex reasoning tasks by generating intermediate reasoning steps. Despite strong empirical success, the mechanisms by which such reasoning abilities emerge during training remain poorly understood, particularly from a theoretical perspective. In this work, we study this question in a stylized yet fully analyzable setting: in-context $\mathrm{K}$-nearest-neighbor ($\mathrm{K}$-NN) prediction with a one-layer transformer. Earlier work has demonstrated that one-layer transformers can be trained to perform $1$-NN without CoT \citep{Li2024One} by having the softmax attention attend to the nearest neighbor in context. However, this result on $1$-NN cannot be extended to $\mathrm{K}$-NN for $K>1$, as softmax attention is much less naturally suited to attending to the 2nd through $K$-th nearest neighbors. We first give a rigorous negative result showing that this limitation already appears on a well-separated class of binary classification tasks for odd $K>1$. We then give an explicit construction of a one-layer transformer, and show that it can solve $\mathrm{K}$-NN via CoT reasoning. We further prove that, under a stylized training setup, gradient descent can recover this construction, thereby showing that transformers can acquire the capability to solve $\mathrm{K}$-NN through training. In addition, we show that the resulting trained model applies to a broader test class than that assumed in the training analysis. Our results shed light on how CoT expands the ability of transformers to learn and solve tasks that are otherwise hard to solve.

Clustering is a fundamental problem in statistics and machine learning. We propose the first one bit clustering method for two-component sub Gaussian mixture models. The method uses only one bit per entry of each sample obtained via a dithered quantizer. Under a mild non-spikiness condition on the cluster centers, we show that a variant of Lloyd’s algorithm achieves a misclassification rate that decays exponentially with a signal to noise ratio comparable to that in the unquantized setting. This result further implies exact recovery under an explicit separation condition, which exceeds the optimal threshold for unquantized data by only a logarithmic factor. When the dimension $p$ is sufficiently large, the non-spikiness condition can be enforced by applying a random rotation using a Haar distributed matrix prior to quantization. In particular, it holds with high probability when $p \gtrsim 1$ for partial recovery and $p \gtrsim \log n \log\log n$ for exact recovery, where $n$ is the sample size. We also establish a minimax lower bound, showing that the misclassification rate and separation condition are in general optimal up to constants. Numerical results are provided to corroborate the theory and demonstrate the efficacy of the proposed method.

A central challenge in reinforcement learning (RL) is to learn models that generalize beyond the tasks on which they are trained, a goal traditionally pursued through multi-task and meta RL. Recently, transformer architectures have emerged as a promising approach, enabling adaptation to new tasks via in-context learning without explicit parameter updates. From a functional perspective, a transformer can be viewed as a **functional operator** that maps a context to a task-specific function. It is thus fundamental to understand and design this operator to support stronger generalization in RL. In this work, we address this resulting question of generalization from a kernel-based perspective by establishing a connection between non-linear transformers and kernel-based temporal difference learning. By interpreting the transformer as performing regression in a **Reproducing Kernel Hilbert Space** (RKHS), we show that value functions from different domains can be represented using a shared set of weights, provided they lie within the same RKHS. Experiments on multiple MetaWorld domains support this interpretation, demonstrating convergence of the temporal-difference objective.

Reconstructing dynamic scenes from Vehicle-to-Infrastructure Cooperative Autonomous Driving (VICAD) data is fundamentally complicated by temporal asynchrony: vehicle and infrastructure cameras operate on independent clocks, capturing the same dynamic agent such as cars and pedestrians at different physical times. Existing Gaussian Scene Graph methods implicitly assume synchronized observations and assign a single pose per agent per frame, which is an assumption that breaks in cooperative settings, where the resulting gradient conflicts cause severe ghosting on dynamic agents. We identify this as a \textit{representation-level failure}, not an optimization artifact: we prove that any single-timeline formulation incurs an irreducible photometric loss scaling quadratically with agent velocity and cross-source time offset. To resolve this, we propose Dust (DecoUpled Spatio-Temporal) Gaussian Scene Graph for 4D Cooperative Driving Reconstruction. DUST Gaussian Scene Graph shares a canonical Gaussian set per agent for appearance consistency, while maintaining decouple pose trajectories aligned to each source's true capture timestamps. We prove that this decoupling enables the pose-gradient kernel block-diagonal, eliminating cross-source interference entirely. To make Dust practical, we further introduce a static anchor-based pose correction pipeline that corrects spatio misalignment between vehicle and infrastructure annotations, and a pose-regularized joint optimization scheme that prevents trajectory jitter and drift during early training. On 26 sequences from V2X-Seq, DUST achieves state-of-the-art performance, improving dynamic-area PSNR by 3.2 dB over the strongest baseline and reducing Fréchet Video Distance by 37.7\%, with keeping robustness under larger temporal asynchrony. Code is available at https://anonymous.4open.science/r/DUST-6A55.

Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training to a narrow set of purpose-built datasets. We propose in-the-wild monocular pretraining: a frozen depth estimator lifts each source image into 3D and reprojects under sampled poses to yield pseudo-target views; masked losses restrict supervision to valid regions and an adversarial objective covers disoccluded areas. Scaled to 30 million uncurated images, this produces OVIE, requiring only a source image and target pose at inference. Prior work trains without multi-view data but needs a depth estimator at inference, or drops this dependency but requires multi-view training pairs; OVIE is the first to require neither. Without multi-view supervision, OVIE rivals in-domain baselines on RealEstate10K and surpasses all on DL3DV; brief multi-view fine-tuning outperforms all geometry-free methods on their training domain. At 116 FPS, it is over 600× faster than the fastest baseline. Code and models will be open-sourced.

The Representation Autoencoder (RAE) shows that a frozen high-dimensional semantic ViT representation enables both image reconstruction and DiT-based generation. However, RAE's semantic-only representation lacks appearance details, yielding suboptimal reconstruction. Full fine-tuning the encoder with reconstruction loss induces catastrophic semantic collapse: the encoder overfits to appearance, loses semantic discriminability, and cripples generation. This failure arises because the reconstruction loss imposes an appearance prior that erodes semantic structure. To resolve this conflict, we propose Semantic Structure Regularization (SSR). SSR builds on self-distillation, using a learnable student and a frozen teacher that retains the original semantics. In addition to standard latent alignment, which forces the student's representation to match the teacher's, we introduce two complementary regularizers that prevent appearance overfitting from corrupting semantic structure. First, spatial self-similarity distillation forces the student to reproduce the teacher's patch-wise relational patterns. Invariant to appearance changes, this relational signature anchors high-order semantics and prevents the sacrifice of structure for pixel fidelity. Second, structure-anchored alignment decouples structure from appearance. Using a frequency-domain transform, we extract a high-frequency structural map (edges, boundaries) from each image while discarding low-frequency appearance. The teacher's representation of this structural map then regularizes the student's representation of the original image. Consequently, the student learns appearance details from the reconstruction loss without penalty, where the regularization enforces only structural consistency and enables pixel-level encoding without semantic drift. The result is a unified representation that excels at high-fidelity reconstruction, discriminative perception, and efficient DiT-based generation. Our work provides a principled path toward a single semantic space bridging perception and generation, challenging the classic dichotomy. Code and models will be released.

Inference-time sampling is a central mechanism for improving large language models, with temperature controlling the tradeoff between reliability and diversity. While recent work suggests that varying temperature across samples can improve multi-sample inference, simple fixed-temperature schemes remain the standard in practice. We investigate this discrepancy in the canonical pass@$K$ setting, asking when temperature diversity is useful, and when a single tuned temperature suffices. To this end, we analyze per-question success probabilities across multiple models and datasets, tracking how they vary with sampling temperature. We identify several consistent properties of these profiles, including that harder questions are less likely to share efficient sampling temperatures, although this trend is only logarithmic in hardness. We then derive an efficient method for evaluating the theoretically optimal multi-temperature allocation scheme, and show that although such a scheme improves performance, its gains over single-temperature baselines are typically modest when temperature is tuned as a function of the sampling budget $K$. We explain this phenomenon through mathematical mechanisms that we call dulling effects, and show how they ensure the robustness of temperature choices across different question distributions. Our results clarify when multi-temperature sampling is useful, and how temperatures should ideally be chosen.

Deploying a language-guided navigation policy in a new building usually means watching it fail. Public-benchmark VLAs degrade scene by scene as layout, appearance, and language drift, and the standard fix of teleoperating fresh trajectories on site does not scale. We replace human demonstration with a single brief iPhone scan. Scan2Instruction compiles that scan into a complete training environment, automatically synthesizing executable navigation episodes with grounded instructions. PRPT-KS then adapts the policy through a closed-form Pareto utility read directly off the reconstructed scene, with no learned critic and no human labels. Across five real environments, the two designs together drive our RealNav to an 89% real-robot success rate, almost doubling the strongest VLA baseline at 48%, and still achieve 87% in unscanned regions of the same buildings. Our work turns demonstration-free scene adaptation for language-guided navigation from an aspiration into a working reality. All code and model will be released open-source upon acceptance.

Neural quantum states (NQS) provides a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov-chain methods. Yet their optimization remains comparatively underexplored: Adam is a scalable method but ignores function space geometry, while stochastic reconfiguration is principled but costly and numerically fragile in large models. To address this gap, we show that variational energy minimization can be viewed as an advantage policy-gradient problem over the Born distribution, motivating trust-region optimization for NQS training. We introduce Proximal Wavefunction Optimization (PWO), a trust-region algorithm that clips probability-ratio changes in the amplitude channel and wrapped phase increments in the phase channel. PWO avoids explicit matrix inversion, reuses samples across inner updates, and preserves the scalability of first-order optimization. Across Ising, Heisenberg, and frustrated $J_1$--$J_2$ spin chains, PWO improves stability and wall-clock convergence over Adam, minSR, and SPRING. Finally, we fine-tune a $1.5$B-parameter RWKV-7 model as a neural quantum state, demonstrating NQS optimization at a scale over three orders of magnitude beyond prior work.

Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages. The sequential nature of EHR, analogous to natural language, has motivated the use of next-token prediction to train prior EHR Foundation Models (FMs) over events. However, this training fails to capture the full structure of EHR. When a given event occurs must be captured, but the event value (abnormal lab) also modulates the likelihood of other clinical events. Most existing EHR FMs do not jointly model this likelihood and are unable to capture the full observation process, impacting downstream capabilities. We propose ORA, a marked time-to-event pretraining objective that jointly models event timing and associated measurements. Across multiple datasets, downstream tasks, and model architectures, this objective consistently yields more generalizable representations than next-token prediction and pretraining losses that ignore continuous measurements. Importantly, the proposed objective yields improvements beyond traditional classification evaluation, including better regression and time-to-event prediction. Beyond introducing a new family of FMs, our ablations suggest a broader takeaway: pretraining objectives that account for EHR structure are critical for expanding downstream capabilities and generalizability.

Universal visual anomaly detection (AD) aims to identify anomalous images and segment anomalous regions towards open and dynamic scenarios, typically adhering to zero- and few-shot paradigms without dataset-specific fine-tuning. Recently, the field has seen significant progress driven by the integration of vision-language foundation models. However, we observe that current methods often struggle with laborious prompt engineering, elaborate adaptation modules, and complex training strategies that ultimately constrain their flexibility and generalizability. In this paper, we rethink the fundamental mechanism of vision-language models for AD and present UniADet, an embarrassingly simple, effective and general framework for Universal vision Anomaly Detection. Our approach is built on two key insights: First, we reveal that the primary function of the language encoder is merely to derive decision weights and we demonstrate that it is unnecessary, as these weights can be learned more directly and efficiently. Second, to resolve learning conflicts arising from disparate feature manifolds, we introduce a systematic decoupling strategy that learns independent weights across both distinct tasks (classification vs. segmentation) and hierarchical features. UniADet is highly simple and efficient, learning only decoupled weights and requiring 0.02M learnable parameters. It is inherently versatile, adapting seamlessly to various foundation models (\eg, CLIP, DINOv2, and DINOv3). Extensive evaluations of 14 real-world benchmarks in the industrial and medical domains demonstrate that UniADet not only exceeds state-of-the-art zero/few-shot methods by a substantial margin, but also outperforms full-shot AD methods for the first time. This empirical evidence reveals the profound potential of language-free frameworks to redefine the boundaries of visual anomaly detection. The code and models will be made publicly available.

Cross-device deployment of a single reconstructed avatar often requires adaptation to varying Gaussian budgets with minimal degradation in visual quality. Motivated by the need for a robust budget-adaptive 3D representation, we propose RobustAvatar, a train-once Level-of-Detail (LoD) adaptation framework for dynamic Gaussian avatars based on sequential selection and compensation. RobustAvatar supports flexible compression-ratio control within a single trained avatar, without requiring fine-tuning at each compression ratio. The framework consists of two coordinated modules: Semantic Distribution-Regularized Selection (SDS) and Canonical Budget-Conditioned Compensation (CBC). SDS performs budget-aware Gaussian selection guided by semantic distribution regularization, preserving the coverage of perceptually salient facial regions relative to the full avatar under constrained budgets. CBC refines the selected primitives in a canonical, expression- and pose-neutral space by predicting ratio-conditioned attribute residuals, reducing detail loss from Gaussian removal and improving robustness to decreasing Gaussian budgets. Experiments on the NeRSemble dataset show that RobustAvatar achieves more robust budget adaptation than state-of-the-art methods, with slower quality degradation as Gaussian budgets decrease. The code will be made publicly available.