NeurIPS 2026 论文列表

Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and unconstrained authentication. Existing datasets are largely restricted to controlled setups and fail to capture the compound variability of real-world environments. In this paper, we introduce X-Palm, a cross-domain dataset comprising 6,006 palm images from 103 individuals (206 hands). To the best of our knowledge, X-Palm is the first palmprint dataset providing novel paired-identity acquisition specifically designed to bridge the gap between reliably controlled multispectral enrollment and unconstrained mobile authentication while encompassing a broad spectrum of in-the-wild variability. Unlike existing datasets that focus on single to a few variations, X-Palm addresses the massive modality and environmental shifts encountered in practical deployments by capturing paired data for identities across two distinct domains: (1) a controlled Multispectral Palmprint setting using our custom-developed scanner, and (2) an unconstrained smartphone palmprint setting that is participant-driven, incorporating simultaneous variations in hardware, hand pose, illumination, background, camera-to-hand distance, perspective, and palm surface conditions (e.g., moisture and occlusions). Our extensive benchmarks of 12 SOTA models reveal that while existing methods achieve high performance on controlled data, they experience severe performance collapse on X-Palm. Conversely, models trained on X-Palm demonstrate consistent robustness across domains, positioning X-Palm as a valuable resource for training a model towards real-world, cross-domain generalization. Data access instructions and the related benchmarking codes are publicly available at: https://github.com/X-Palm/X-Palm-2026

Identifying root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored systems. Existing studies evaluate root cause analysis (RCA) methods using top-k accuracy. We show that this metric has a fundamental blind spot: it conflates two failure modes: *retrieval failure*, where the true cause is never considered, and *reranking failure*, where it is considered but ranked too low. In this work, we introduce a retrieval-reranking decomposition and audit four well-known benchmarks to expose this blind spot. Our experiments show that, on benchmarks with complex faults, statistical baselines mis-rank the true cause 78-100\% of the time, and graph-based methods perform poorly because of unreliable causal graphs constructed using short fault windows. Meanwhile, on simple benchmarks where faults manifest significantly at their origin, retrieval is trivially solved at 100\%. Guided by the decomposition, we build a two-stage pipeline combining a multi-signal retriever with an LLM reranker that lifts top-1 accuracy by $+$2 to $+$26 points over the best baseline on complex-fault benchmarks and $+$9 to $+$13 points on simple benchmarks, with no causal graph or labeled data required.

Alignment-based representation learning for cross-dynamics RL admits a degenerate global minimizer: per-domain feature covariances collapse to a common point, driving the alignment loss to zero while transfer regret remains large and rendering the standard Bures-Wasserstein (BW) transfer regret bound vacuous. We document this collapse across multiple environments, alignment kernels (BW, Frobenius, MMD), and methods (BW-CORAL, VGDF), where per-domain effective rank drops from p to $\approx 1$ within a few epochs—structurally the same failure as representation collapse in self-supervised learning. We propose a minimal remedy: a per-domain Prototype Trust Region (TR) that anchors each empirical covariance to a slow-moving EMA prototype, introduces no learnable parameters, and acts as an explicit second-moment stabilizer, mirroring momentum targets and covariance regularizers in SSL. Because TR depends only on per-domain covariances, it is combined with any BW-based method; adding it to VGDF inherits the same resistance to collapse. Theoretically, we prove a BW non-collapse lower bound that restores the transfer-regret guaranty to a non-vacuous form, and present a Representation Stability Transfer Bound that decomposes regret into coverage and stability terms TR directly controls. Empirically, TR delivers statistically significant gains under pre-registered tests in collapse-dominated regimes (finite-sample, lower-dimensional) and is correctly neutral when other bottlenecks dominate. We frame TR not as a universal improvement but as a targeted fix for an identifiable and broadly shared failure mode.

List-wise reranking arranges a request-specific pool of candidate items into an ordered slate that maximizes user satisfaction. Existing generative rerankers fall into two paradigms: Autoregressive (AR) rerankers construct the slate left to right and capture inter-item dependencies in the exposure list, but they suffer from error propagation because early mistakes affect subsequent slots. Non-autoregressive (NAR) rerankers predict all slots in parallel and avoid error propagation, but they weaken inter-item interaction modeling under a slot independence assumption. This raises a central question: is there a unified architecture that combines the strengths of both paradigms and delivers stronger reranking performance? We answer this question with UniRank, a unified list-wise reranking framework whose inference time variants recover AR and NAR rerankers as special cases. UniRank integrates bidirectional slate modeling into an iterative denoising process and fills the most confident slot at each step. To instantiate this framework for reranking, we introduce the Task Grounded Diffusion Interface (TGD), which performs denoising at the item level and restricts prediction to the request-specific candidate pool. TGD aggregates each item's semantic tokens into a single item embedding and scores each slot directly against the candidate pool. Experiments on Amazon Books, MovieLens-1M, and an industrial short video dataset show that UniRank consistently outperforms state-of-the-art baselines. Our code is available online.

Despite the promise of Neural Combinatorial Optimization (NCO), recent literature has increasingly coupled *decision paradigms*, *training objectives*, and *problem classes* with specific backbone architectures, limiting generic model design and fair cross-paradigm evaluation. We propose UECO, a simple yet effective **U**nified **E**ncoder for **C**ombinatorial **O**ptimization with a structure-aware attention mixture scheme, where a lightweight module defined upon graph-based locality is interleaved with conventional (global) node-focused attention blocks to dynamically evolve edge representations and inject local topological information into attention scores. Instead of fusing static edge features in a single step, UECO thereby integrates local and global messages in a progressive and problem-agnostic manner. UECO is orthogonal to task-specific decoders, and works seamlessly with *Local Construction* (LC), *Global Prediction* (GP), and *Adaptive Expansion* (AE) paradigms, across node- or edge-oriented problems, symmetric or asymmetric tasks, and sparse or dense graphs, for supervised, reinforcement, or generative learning. Extensive experiments show that, via plug-in substitution of encoders, UECO consistently improves solution and generalization quality over diverse backbones and task scales on TSP, ATSP, CVRP, ACVRP, MIS, MaxClique, and MaxCut.

Convolutional neural networks trained on ImageNet are known to rely heavily on local high-frequency texture, an inductive bias that translates into fragile robustness against distribution shifts in real-world environments. Event cameras, in contrast, record only changes in scene brightness and are therefore well suited to capturing contour information; however, due to the absence of diagnostic benchmarks in the event domain, the inductive bias that event-camera data instills in vision models has remained unexplored. In this work, we use knowledge distillation from the event domain to the RGB domain so as to exploit the rich evaluation toolkit available in the RGB domain and systematically dissect this inductive bias. Our experiments show that distillation from the event domain induces, in the RGB domain, notable color invariance, shape bias, and robustness to high-frequency noise. We identify the underlying mechanism as the model suppressing its dependence on high-frequency texture while simultaneously acquiring a strong dependence on edge-based object shape. This hypothesis is supported by changes in how color and spatial information are processed at the early layers, together with a "spectral trade-off" in which robustness to the absence of high-frequency components coexists with vulnerability to contamination of the relied-upon frequency bands and to disruption of geometric structure. We further show that this inductive bias differs markedly from existing robustification methods and that it functions as a strong prior for diverse downstream tasks that demand shape-based reasoning, such as medical imaging. The code and experimental configurations for reproducing our experiments are submitted alongside this paper as supplementary material.

Transformer-based autoregressive multilingual language models (LMs) are often assumed to store factual knowledge in an interlingua: a language-agnostic latent space. If this assumption holds, a parameter update applied in a source language during knowledge editing should inherently propagate to its semantic equivalents in target languages. However, empirical benchmarks reveal that cross-lingual propagation of edits consistently fails, a degradation that prior work treats largely as an algorithmic symptom rather than a structural bottleneck. In this work, we hypothesize that this failure stems from language anisotropy, and we perform a rigorous geometric dissection of the cross-lingual propagation gap. Using associative memory-based "locate-then-edit" methods, including ROME, MEMIT, and AlphaEdit, as analytical frameworks, we derive a closed-form decomposition of this gap into key-space propagation, parallel residual mismatch, and orthogonal residual mismatch. We empirically find that cross-lingual propagation is consistently limited by a large orthogonal residual component, indicating that source-language edits often fail to span the target-language update directions required for successful transfer. Furthermore, we show that post-hoc correction using only source-language residual statistics faces a large residual-prediction barrier. Finally, we demonstrate that structural realignment via a targeted contrastive alignment objective substantially improves key-space propagation and partially improves cross-lingual edit transfer, while leaving value-space mismatch as a remaining bottleneck.

Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. At the column/table level, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. At the row level, within-table prediction and cross-table linkage favor different training regimes, with the row-matching stage of DLTE pipelines correlating strongly with atomic linkage performance. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions.

Cross-city transfer leverages labeled data from well-instrumented cities to improve prediction in label-scarce ones, but remains challenging when cities adopt incompatible partitions with no ground-truth region correspondences. Even with expressive GNN encoders, transfer quality varies dramatically across methods sharing nearly identical backbones---indicating that alignment design, not encoder capacity, is the binding constraint. Existing paradigms exhibit complementary failure modes: heuristic anchor matching collapses to hubness under unequal partitions, while distribution-level matching over-mixes embeddings under heterogeneity. Both stem from a single missing primitive---explicit, mass-controlled soft correspondence between unequal region sets. The challenge intensifies in multi-source transfer, where independent source-to-target alignments yield conflicting gradients and source domination. We propose SCOT, which adapts entropic OT to this regime through three application-specific designs: an OT-weighted contrastive objective that resolves the geometric--semantic tension, a one-sided cycle regularizer respecting the rectangular $n_s\!\neq\!n_t$ geometry, and---as our central contribution---a shared prototype hub coordinated through balanced entropic OT under a target-induced prior, bypassing the source-selection problem in label-scarce regimes. Across real-world cities and tasks, \scot consistently improves transfer accuracy, achieving 5--50\% relative MAE/MAPE reductions over the strongest baseline, with learned couplings and hub assignments quantitatively confirming that the diagnosed failure modes are resolved.

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.

Cross-modal place recognition aims to retrieve a target 3D location from a spatial map using a natural language description. Most existing methods follow a global-descriptor matching paradigm, in which the text query and each 3D scene cell are independently compressed into single vectors and compared by global similarity. Although simple and widely adopted, this paradigm tends to discard the fine-grained object correspondences that are essential for language-guided localization. In this paper, we challenge the necessity of global descriptors and propose OLA-Place, an Object-Level Alignment framework for cross-modal place recognition without global descriptors. Instead of representing a query or a scene cell as a single holistic embedding, OLA-Place formulates place recognition as set-to-set semantic alignment between textual object mentions and 3D object instances. The framework contains three key modules: ObjectSet Encoder (OSE), which extracts object-level representations from both language descriptions and 3D scene cells; Object Message Encoder (OME), which injects intra-set object-context information while preserving object-level granularity; and Masked Max Alignment (MMA), which computes the query-cell matching score by aligning each textual object mention with its most similar valid 3D object. This formulation is permutation-invariant, naturally handles variable-size object sets, and requires no object-level correspondence annotations. Extensive experiments show that object-level alignment alone significantly outperforms global-descriptor-based methods, demonstrating that cross-modal place recognition is better understood as an object-level semantic correspondence problem rather than a global-embedding retrieval problem. Our code is available at: https://github.com/Anonymous09871745/OLA-Place.

Understanding relationships between variable sets is fundamental in machine learning, particularly in medical data analysis. Existing methods typically provide only coarse shared/private partitioning of variables, limiting their ability to reveal finer-grained predictive structures across views. We propose Mutual Predictability Decomposition (MPD), a framework that decomposes the variables in each view into three subsets: mutually predictable variables that can be inferred across views, auxiliary variables that are themselves not cross-view predictable but are necessary for predicting the mutually predictable subset, and unpredictable variables that do not contribute to cross-view predictability. MPD is formulated as a coupled bi-directional prediction problem with sparse variable assignment, and optimized through a differentiable relaxation implemented using gated neural networks. Experiments on synthetic and real-world medical datasets demonstrate that MPD recovers interpretable cross-view structures, improves cross-view prediction, and provides more informative decompositions than existing relationship analysis methods. Code will be available on GitHub upon acceptance.

Cross-modal person re-identification (ReID) adversarial attacks face a fundamental generalization dilemma: existing methods suffer from source-pair overfitting, where the learned perturbations become entangled with the feature covariance and alignment patterns of the source modality pair, thereby limiting their transferability under heterogeneous domain shifts. We argue that the core issue is the difficulty of reducing source-pair-specific bias while maintaining attack effectiveness. To address this, we propose Multiform Attack (MA). The framework first learns a universal attack direction via Mahalanobis-guided gradient optimization to capture the intrinsic covariance structure of the feature manifold, though this perturbation may still retain source-pair-specific bias. To overcome this bias and achieve cross-distribution generalization, the second stage leverages multiple heterogeneous source distributions to optimize for generalization, performing sparse, structure-sensitive residual correction in the discrete pixel space via attention-shift-filtered multi-objective evolutionary search. This two-stage design models the attack as a base direction plus a structured residual, which helps reduce overfitting to the source modality pair and improve transferability to unseen modalities. Extensive experiments show that MA achieves superior transferability across unseen modalities, datasets, and model architectures.

This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene structure, thereby enabling transferable multi-view geometric representations. Yet, existing encoder-based NVS methods yield poor representations. This is not because of lack of supervisory signal, but rather due to inconspicuous architectural choices: spatially expressive decoders that dilute representational capabilities of the scene encoder, and low-level pixel-space targets that hinder feature learning. We present SNAP, a self-supervised encoder-decoder transformer that addresses both through a pose-conditioned local decoder and a latent-space reconstruction objective. SNAP is general purpose, and we show that it is on par or better than special-purpose geometry-supervised methods. SNAP also outperforms self-supervised representations across five geometric tasks: visual localization, pose estimation, point correspondence, depth estimation, and robot manipulation. Remarkably, SNAP's patch tokens exhibit emergent viewpoint invariance that rivals heavily supervised models despite lower compute and data budgets. Under severe camera shifts where standard 2D representations collapse, SNAP maintains strict spatial consistency, revealing that restricting decoder expressivity actively prevents the suppression of transferable geometric structure.

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.

Unified models (UMs) hold promise for their ability to understand and generate content across heterogeneous modalities. Compared to merely generating visual content, the use of UMs for interleaved cross-modal reasoning is more promising and valuable, e.g., for solving understanding problems that require dense visual thinking, improving visual generation through self-reflection, or modeling visual dynamics of the physical world guided by stepwise action interventions. However, existing UMs necessitate pixel decoding as a bridge due to their disjoint visual representations for understanding and generation, which is both ineffective and computationally cumbersome. In this paper, we introduce LatentUM, a novel unified model that represents all modalities within a shared semantic latent space, eliminating pixel-space mediation for model-internal reasoning over generated visual states. By making generated visual tokens directly interpretable to the model itself, LatentUM enables flexible interleaved cross-modal reasoning and generation. Empirically, LatentUM achieves state-of-the-art performance on the Visual Spatial Planning benchmark, pushes the limits of visual generation through self-reflection and demonstrates world modeling capability by predicting future visual states in the shared semantic latent space.

Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. By repurposing the model architecture to enable cross-thread attention, LACE allows concurrent reasoning paths to share intermediate insights and correct one another during inference. A central challenge is the absence of natural training data that exhibits such collaborative behavior. We address this gap with a synthetic data pipeline that explicitly teaches models to communicate and error-correct across threads. Experiments show that this unified exploration substantially outperforms standard parallel search, improving reasoning accuracy by over 7 points. Our results suggest that large language models can be more effective when parallel reasoning paths are allowed to interact.

Covariance estimation yields a fundamental second-order statistic underlying representation learning, dimension reduction, and dependence modeling. While covariance has been well understood in Euclidean spaces, it is ill-defined for random objects residing on nonlinear Riemannian manifolds, which increasingly arise in modern machine learning applications involving shapes, symmetric positive definite (SPD) matrices, etc. This paper introduces an intrinsic Riemannian cross-covariance for manifold-valued random objects. Our approach defines covariance and correlation by transporting local variations to a common tangent space via parallel transport, yielding a second-order descriptor that is independent of arbitrary coordinate choices. We establish that the proposed covariance inherits desirable properties of its Euclidean counterparts and characterize its asymptotic behavior. Numerical studies on spheres and SPD manifolds, together with real-data experiments on heart valve shapes in Kendall's shape space, demonstrate the effectiveness and verify the properties. Our results position Riemannian covariance as a fundamental tool for second-order learning and analysis in non-Euclidean representation spaces.

Image classification models often encounter objects in diverse and unpredictable contexts at test time, which may differ substantially from those seen during training. This phenomenon, termed *context shift*, exposes a key fragility in neural networks: when models rely on non-causal contextual cues for prediction, shifts in context can lead to significant performance degradation. In this paper, we propose a novel training framework to improve the robustness of convolutional neural networks under context shift. Our approach leverages spatial feature attribution to guide models toward making predictions that rely less on contextual regions and more on object-relevant features. As a first step, we identify a theoretical limitation of existing feature attribution methods and introduce a new variant, *ContrastiveCAMs*, which produces more faithful attribution maps of model predictions. Building on ContrastiveCAMs, we further propose *Context-Regularized Cross-Entropy* (CR-CE), a modification of the standard cross-entropy loss that regularizes the model’s attention by suppressing the influence of contextual regions, thereby improving robustness to context shift. We evaluate the effectiveness of our approach on several medium-to-large scale datasets (Waterbirds, Spawrious, ImageNet/ImageNet-BG, Hard-ImageNet), and report consistent improvements in context-shift robustness.