NeurIPS 2026 论文列表

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.

The recently introduced 4-bit floating-point format, NVFP4, demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of redundancy in the existing NVFP4 encoding: (1) The FP4 format naturally exposes an unused quantization value due to its sign-magnitude representation that contains both positive and negative zeros. (2) The FP8 block scaling factor, with 4-bit exponent and 3-bit mantissa, contains an unused sign bit given that the scaling factor is always positive. Additionally, we find that LLM weights are more tolerant to a lower-precision block scaling factor, such as 6 bits with 3-bit exponent and 3-bit mantissa. Based on these observations, we propose **R**edund**a**nt **Ze**ro **R**emapping (RaZeR), an enhanced numerical format that pushes the limits of NVFP4 for more accurate LLM quantization under the same memory footprint. RaZeR leverages the unused bits in the block scaling factor to adaptively remap the negative FP4 zero to a set of pre-defined special values, which maximally utilizes the NVFP4 encoding and better fits the LLM tensor distribution. Extensive experiments validate RaZeR’s superior performance for 4-bit LLM quantization. For example, RaZeR reduces the average perplexity loss of NVFP4 by 29.4% and 32.3% under weight-only and weight-activation quantization, respectively.

We explore whether Omni-Language Models (OLMs) can be directly applied to zero-shot Semantic Audio-Visual Navigation (SAVN). Recent work demonstrates that even state-of-the-art specialized models still struggle to achieve generalist multimodal navigation, despite extensive task-specific training. In this paper, we introduce **RAO-Nav**, short for Reasoning All-in-One OLM, a deployment pipeline for zero-shot SAVN. By leveraging the rich implicit audio-visual knowledge encoded in OLMs, the embodied agent is enabled to hear, see, reason, and act in the environment. To further elicit the built-in thinking ability of OLMs, we propose a test-time Latent Navigation Reasoning (LNR) module that can be seamlessly integrated into the decoding space. LNR encourages the model to retrieve more target-relevant observations and make effective navigation decisions. Through comprehensive experiments, we show that our framework surpasses existing state-of-the-art baselines on public SAVN benchmarks without using any training data. Moreover, we introduce a new Global Navigation Instruction setting to further evaluate the ability of OLMs to serve as embodied navigation agents. Results under this setting posit that applying OLMs to real-world natural-language-guided audio-visual navigation remains challenging.

Several disruptive research directions have recently emerged in computer vision, including foundation models achieving previously unseen zero-shot performance in scene understanding, even interactively, and generative models that synthesize extremely realistic images. The latter have also been shown to be highly effective in scene understanding tasks thanks to their rich priors. However, for promptable segmentation, foundation models struggle with accurately segmenting an object's region, leading to false positives and over-segmentation. Notably, early attempts that leverage generative priors use prompts only during post-processing, yielding suboptimal segments because the process is agnostic to the user input. In this paper, we target these limitations with Prompt2Seg, a spatial conditioning framework for diffusion-based segmentation. Prompt2Seg augments a frozen diffusion segmentation model with a conditioning branch. Our approach takes spatial prompts, represented as 2D Gaussians or confidence maps, as explicit input signals, training the model to respond directly to user intent. Fine-tuned on a deliberately constrained set of object categories drawn from Hypersim and Virtual KITTI 2, Prompt2Seg generalizes zero-shot to a wide range of unseen object types and visual domains. We evaluate on seven datasets ranging from standard benchmarks to more challenging domains, including paintings, egocentric views, and X-ray data. Furthermore, we demonstrate that Prompt2Seg consistently outperforms the underlying diffusion segmentation backbone across all benchmarks. Our results suggest that the rich priors encoded in generative pretraining, combined with principled spatial conditioning, offer a compelling path toward broadly generalizing interactive segmentation without large-scale mask supervision.

Zero-shot skeleton-based action recognition (ZSSAR) is typically treated as a skeleton-text alignment problem: encode joint-coordinate sequences, align them with language, and classify unseen actions. We argue that this alignment is often too late. Skeletons are not complete action observations, but compressed outputs of human pose estimation (HPE); by the time alignment begins, human-object interactions and pose-relative visual cues may no longer be explicit. We call this upstream semantic loss. To address it, we propose PoseBridge, an HPE-aware ZSSAR framework that bridges intermediate HPE representations to skeleton-text alignment. Rather than adding an RGB action branch or object detector, PoseBridge extracts pose-anchored semantic cues from the same HPE process that produces skeletons, then transfers them through skeleton-conditioned bridging and semantic prototype adaptation. Across NTU-RGB+D 60/120, PKU-MMD, and Kinetics-200/400, PoseBridge improves ZSSAR performance under the evaluated protocols. On the Kinetics-200/400 PURLS benchmark, which contains in-the-wild videos with diverse scenes and action contexts, PoseBridge shows the clearest separation, improving the strongest compared baseline by 13.3-17.4 points across all eight splits. Our code will be publicly released.

We study the task of agnostic learning of multiclass linear classifiers under the Gaussian distribution. Given labeled examples $(x, y)$ from a distribution over $\mathbb{R}^d \times [k]$, with Gaussian $x$-marginal, the goal is to output a hypothesis whose error is comparable to that of the best $k$-class linear classifier. While the binary case $k=2$ has a well-developed algorithmic theory, much less is known for $k \ge 3$. Even for $k=3$, prior robust algorithms incur exponential dependence on the inverse of the desired accuracy in both complexity and representation size. In this work, we develop new structural results for multiclass linear classifiers and use them to design fully polynomial-time robust learners with dimension-independent error guarantees. Our first result shows that the standard multiclass perceptron algorithm requires super-polynomially many samples and updates, even with clean labels and Gaussian marginals, revealing a basic obstruction absent in the binary case. Our main positive result is a pairwise improper-learning framework which yields an efficient learner with error $\widetilde O(k^{3/2}\sqrt{\mathrm{opt}})+\epsilon$ for general $k$. Additionally, we develop a sharper localization-based framework which leads to error $O(\mathrm{opt})+\epsilon$ for $k=3$, and error $\mathrm{poly}(k)\mathrm{opt}+\epsilon$ for geometrically regular $k$-class linear classifiers.

We propose a structure learning method for directed acyclic graph (DAG) models of zero-inflated multivariate count data. Although causal structure learning with zero-inflated Poisson models is identifiable, existing algorithms rely on local search over the DAG space, leading to poor scalability and a tendency to converge to suboptimal solutions. To address these limitations, we develop a novel order-based scoring framework that searches over variable orderings rather than directly over DAGs. Building on this framework, we further extend the method to multiple heterogeneous datasets by identifying a shared variable ordering while allowing each dataset to have its own edge set. We implement a stochastic hill climbing algorithm with a random-to-random (R2R) proposal operator to efficiently explore the ordering space. Simulation studies show that the proposed method outperforms competing approaches and improves graph estimation accuracy under increasing heterogeneity when a shared causal ordering is preserved. We apply the method to single-nucleus RNA sequencing data from a major depressive disorder study.

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.

Activation monitoring, which probes a model's internal states using lightweight classifiers, is an emerging tool for AI safety. However, its worst-case robustness under a misalignment threat model, where a model might learn to actively conceal its internal states, remains untested. We ask: could a model learn to evade previously unseen activation monitors? We demonstrate that fine-tuning can create **Neural Chameleons**: models capable of zero-shot evading activation monitors. Specifically, we fine-tune an LLM to evade monitors for benign concepts (e.g., HTML) when conditioned on a trigger phrase "You are being probed for {concept}". This learned mechanism generalizes zero-shot: substituting the concept with a safety-relevant term like 'deception' causes the model to successfully evade previously unseen safety monitors, even those trained post hoc on no-trigger activations from the frozen fine-tuned checkpoint. We validate this across diverse model families (Llama, Gemma, Qwen), finding that evasion is highly selective to the triggered concept and incurs minimal capability degradation. Mechanistic analyses suggest that the trigger induces an activation shift that moves representations away from probe-aligned directions. Our work provides a proof-of-concept for this failure mode of activation monitoring under misalignment threat models.

Mobility trajectory data provide essential support for smart city applications. However, such data are often difficult to obtain. Meanwhile, most existing trajectory generation methods implicitly assume that at least a subset of real mobility data from target city is available, which limits their applicability in data-inaccessible scenarios. In this work, we propose a new problem setting, called bus-conditioned zero-shot trajectory generation, where no mobility trajectories from a target city are accessible. The generation process relies solely on source city mobility data and publicly available bus timetables from both cities. Under this setting, we propose MobTA, the first approach to introduce task arithmetic into trajectory generation. MobTA models the parameter shift from bus-timetable-based trajectory generation to mobility trajectory generation in source city, and applies this shift to target city through arithmetic operations on task vectors. This enables trajectory generation that reflects target-city mobility patterns without requiring any real mobility data from it. Furthermore, we theoretically analyze MobTA's stability across base and instruction-tuned LLMs. Extensive experiments show that MobTA significantly outperforms existing methods, and achieves performance close to models finetuned using target city mobility trajectories.

In overparameterised classification, training data can be linearly separable even when the underlying distribution is not. In this setting, gradient descent (GD) on the logistic loss diverges in norm while converging in direction to a max-margin interpolating classifier, whose implicit bias can be statistically suboptimal. In this work, we show that early stopping can overcome this suboptimality: in a Gaussian mixture model with label-flipping noise, GD stopped at an appropriate oracle time achieves minimax-optimal excess zero-one risk for covariance spectra with fast and continuous decay, including polynomial and exponential spectral decays. Our analysis combines a sharp upper bound for the early-stopped iterate with a matching statistical lower bound over arbitrary classifiers, yielding optimal rates that are validated by experiments. A central technical contribution is a new calibration result that converts excess logistic risk into excess zero-one risk; it handles the model misspecification induced by the label-flipping noise, and removes the square-root rate in standard bounds. We also establish a lower bound for linear interpolators, showing that interpolation can require exponentially more samples than early stopping to achieve the same excess risk.

Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve accurate, generalizable decoding from non-invasive, temporally resolved signals, including electroencephalography (EEG). A major obstacle towards this goal is the low signal-to-noise ratio of EEG and the substantial inter-subject variability, which render direct end-to-end EEG–image supervision weak and unstable. To address this, we introduce a tri-modal contrastive framework for EEG-based visual decoding that aligns EEG, visual, and textual representations within a unified latent space. Our approach follows a two-stage design. First, we pre-train an EEG encoder via masked reconstruction on unlabeled trials, learning spatio-temporal regularities that transfer robustly downstream. Second, we jointly align EEG, image, and LLM-generated textual descriptions through contrastive learning, where text supervision acts as a semantic regularizer that injects linguistic structure into the shared space without overwhelming the primary EEG–image signal. The encoder integrates subject-specific adaptation, graph-attention over channels, and temporal-spatial convolutional embeddings. On the Things-EEG2 200-way zero-shot benchmark, our framework achieves 54.1\% Top-1 and 83.4\% Top-5 accuracy, substantially exceeding the strongest prior baseline (32.4\% / 64.0\%), with paired Wilcoxon tests confirming significance (p < 0.01) over all in-subject baselines. We validate generalization on Things-MEG. Analysis reveals that compact embedding geometries (CN-CLIP) outperform much larger backbones, and that decoding aligns with established neurophysiology of visual processing. This work is a critical step towards robust, semantically-grounded visual decoding from non-invasive temporal neural signals.

Meta-reinforcement learning (meta-RL) agents adapt to novel tasks from test-time experience, but require diverse training sets of environments and reward functions that are expensive to construct. Behavior Foundation Models (BFMs) learn policies from reward-free data that are capable of zero-shot RL (ZSRL), adapting to new objectives at test time when given a large reward-labeled dataset. Recent work has shown that BFM task inference can be performed online, making BFMs and meta-RL direct competitors for generalization in fixed environments. We ask whether BFMs can be extended to adapt to novel reward functions in novel environments, and identify four key limitations: environment identification, online data collection, test-time exploration-exploitation, and mixed reward supervision. We study these subproblems through toy domains, standard BFM benchmarks, and simulated humanoid locomotion, then propose a unified framework that addresses all four. The resulting method, MetaBFM, is a hybrid RL agent spanning meta-RL, ZSRL, and intrinsic exploration. During training, MetaBFM combines supervised reward-following with unsupervised reward-free learning; at test time, it interpolates between exploration, exploitation, and ZSRL-style reward inference. We evaluate MetaBFM on two toy meta-RL domains and at scale in MetaWorld, showing that hybrid meta-RL/ZSRL agents can learn more general behavior from the same set of reward functions and may reduce the need for future meta-RL domains to hand-design diverse training sets.

Recently, Noise2Noise provides a powerful principle for learning image denoising without clean data. However, zero-shot methods for 2D images often rely on local similarity or non-local self-similarity to construct image pair for training, which is unreliable in complex scenes with highly textures. Different from 2D image, 4D Light Field (LF) captures multiple views for one specific scene. The multiple views with independent noises provides make it possible to find image pair across views. In this paper, we exploit the inter-view correspondence in LF and propose a novel way to implement the Noise2Noise in 4D LF. Specifically, we design a Matching2Matching (M2M) framework to construct matching views for each view in LF. This framework generates geometrically consistent training samples by searching for corresponding patches along the epipolar line, crucially enforcing both visible and disparity consistency constraints. The constructed views enable the network to train the denoised network in a zero-shot way without any other data. Experimental results show that our method effectively suppresses noise while outstandingly preserving the consistency and fine details. Our M2M framework significantly outperforms existing zero-shot methods in both quantitative and qualitative evaluations on synthetic and real-world noise, demonstrating superior generalization ability without relying on external training datasets.

Post-training quantization (PTQ) is a key technique for efficiently deploying large language models, as it compresses weights into low-bit representations without retraining. Existing PTQ solvers, including GPTQ, LDLQ, and block coordinate descent, commit each weight block to a single codeword before its neighbors are considered, leaving near-optimal alternatives unexplored. In this work, we cast row-wise PTQ as posterior inference over discrete codebooks, in which existing solvers correspond to a zero-temperature, hard-decision limit retaining only the top codeword per block. We propose \emph{Message-Passing Quantization} (MPQ), which preserves soft posterior information across blocks during inference and commits to hard codewords only at the output stage. MPQ first uses vector approximate message passing to compute approximate blockwise posteriors, then refines a hard-decision initialization by exact block coordinate descent. Across 2-bit lattice and 3- and 4-bit scalar quantization on dense language models from Llama, Qwen, and Mistral families, MPQ improves perplexity and zero-shot accuracy over strong hard-decision baselines, and composes favorably with preprocessing techniques such as activation-aware scaling and incoherence processing.

Deep neural networks are highly vulnerable to bit corruption in stored weights, whether from naturally occurring memory faults or deliberate fault-injection attacks. Existing defenses remain fundamentally limited: fault-tolerance methods degrade under cumulative corruption and remain vulnerable to strong white-box attacks, while ECC-based recovery methods are often tied to specific precisions or architectures and expose concentrated vulnerable surfaces. We propose a simple training-based recovery framework that endows model weights with recoverable structure through block-wise zero-sum constraints enforced by differentiable projection. The resulting method incurs no parameter-space overhead and generalizes naturally across architectures and numerical precisions. We further develop adaptive adversarial bit-flip attacks tailored to recovery-based defenses, covering both our method and prior ECC-based approaches. Across diverse architectures, precisions, datasets, and threat models, our method consistently delivers strong robustness and recoverability while preserving clean performance.

Does a neuron’s shape predict whom it connects to? Peters’ rule, a canonical geo- metric principle in connectomics, predicts connectivity from spatial arbor overlap, but true wiring also depends on molecular and morphological specificity. We ask how far neuron skeletons, with identity and graph features withheld, predict directed connections in the FlyWire Drosophila connectome. Because most non- connected pairs are far apart, headline metrics are dominated by easy negatives that geometry can reject. We therefore evaluate controlled contrasts: plausible non-connections whose arbors are nearby, overlapping, or cell-type matched, but that still do not synapse. Across a controlled model ladder, from arbor overlap and hand-crafted morphology baselines to Connectoformer (a bidirectional Trans- former with pooled and pairwise cross-attention scoring), performance improves when the model gains the corresponding signal: global shape, local branch com- patibility, and tree context. Morphology and arbor co-occupancy are complemen- tary: morphology discriminates similar-looking unconnected pairs but can score pairs whose arbors never touch; arbor co-occupancy does the inverse. By suppress- ing essentially-zero-overlap pairs without boosting high-overlap pairs, a fixed one- sided arbor prior performs well in both regimes. The encoder also transfers across Drosophila individuals: zero-shot evaluation on the male CNS connectome beats arbor overlap on hard-negative discrimination. Stratified evaluation reveals a clean division of labor: geometry rules out pairs that cannot physically connect, and morphology picks the actual partners from what remains.

Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain–computer interfaces, but remains challenging due to low signal-to-noise ratios and high inter-subject variability. As a result, many existing approaches rely on subject-specific models, which fail to exploit shared structure in neural signals and do not generalize to unseen subjects. To address these limitations, we propose LAtte, a framework that combines Lorentz attention with a hyperbolic InceptionTime-based encoder to improve cross-subject generalization in EEG classification. The model explicitly decomposes EEG signals into a learned baseline component and task-relevant deviations, enabling more structured representation learning. To further improve robustness and adaptability, we incorporate subject-specific low-rank adaptation (LoRA) modules at both encoder and decoder levels, augmented with a Lorentz boost–based LoRA mechanism and hyperbolic projection layers to reduce overfitting in geometric representations. We evaluate LAtte with and without finetuning in three settings: subject-specific, subject-conditional, and leave-one-subject-out (LOSO) on five established EEG datasets, achieving a consistent improvement in performance over current state-of-the-art methods for smaller datasets and maintaining performance for larger datasets.

Metric-induced discrete flow matching (MI-DFM) exploits token-latent geometry for discrete generation, but its practical use is limited by two issues: heuristic schedulers requiring hyperparameter search, and finite-step path-tracking error from its first-order continuous-time Markov chain (CTMC) solver. We address both issues. First, we derive a kinetic-optimal scheduler for prescribed scalar-parameterized probability paths, and instantiate it for MI-DFM as a training-free numerical schedule that traverses the path at constant Fisher–Rao speed. Second, we introduce a finite-step moment correction that adjusts the jump probability while preserving the CTMC jump destination distribution. We validate the resulting method, GibbsTTS, on codec-based zero-shot text-to-speech (TTS). Under controlled comparisons with a unified architecture and large-scale dataset, GibbsTTS achieves the best objective naturalness and is preferred in subjective evaluations over masked discrete generative baselines. Additionally, in comparison with the evaluated state-of-the-art TTS systems, GibbsTTS shows strong speaker similarity, achieving the highest similarity on three of four test sets and ranking second on the fourth. Demo and code are available in the supplementary materials.