NeurIPS 2026 论文列表

Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Due to strong empirical performance, internal representation–based methods have emerged as the prevailing direction for detecting hallucinations, yet they remain largely model-specific and often fail to generalize to unseen LLMs. In this paper, we study an important yet underexplored problem, termed cross-model hallucination detection (CMHD), which aims to train hallucination detectors on source LLMs while ensuring performance on unseen target LLMs. The core challenge of CMHD lies in cross-model representation heterogeneity: hidden states from different LLMs exhibit severe semantic inconsistency, which limits the transferability of classical representation-based detectors. Through analysis and empirical validation, we show that relational structures over layers, tokens, and features capture transferable detection signals despite semantic inconsistency. Based on this observation, we propose cross-model hallucination detection via heterogeneity-oriented relational discrimination (CHORD), a relational graph discrimination framework that represents hidden states as joint relational graphs over layers, tokens, and features. It feeds these graphs into relation-aware attention to obtain structure-aware features, and uses meta-learning to shape these features toward transferable detection signals. Extensive experiments show that CHORD outperforms representative detection methods in cross-model generalization.

Graph-structured data is crucial in various domains like biology and social networks. Comparing graphs, which is a fundamental problem in graph data analysis, is nonetheless highly challenging. Recently, the Gromov-Wasserstein (GW) distance has provided a principled way to compare two graphs. However, computing the GW distance involves solving a complex non-convex optimization problem, making it computationally expensive, especially when the graphs are large. In this work, we propose a neural approximation of the GW distance, called NeuralGW. In NeuralGW, we use a combination of a graph isomorphism network and a transformer to represent the nodes of two graphs as two sets of vectors, treated as two discrete distributions, on which we compute multiple maximum mean discrepancy values given by different kernels. We then use a multilayer perceptron to convert the vector formed by these values into a single value, which is the prediction of the GW distance. Once trained, the model allows for efficient inference, enabling fast structural comparisons between graphs across diverse domains. We also provide a theoretical guarantee for the generalization ability of NeuralGW. Experiments demonstrate the effectiveness and practical applicability of our approach on real-world datasets, in comparison to baselines.

Graph contrastive learning constructs augmented views and aligns corresponding instances across views to learn view-invariant representations. However, existing methods usually enforce cross-view consistency within a single representation space, lacking an explicit characterization of view-specific information. This may suppress task-relevant complementary information, reduce embedding diversity, and induce dimensional collapse. To this end, we propose Manifold-Guided Representation Disentanglement (MGRD), a plug-and-play framework for boosting graph contrastive learning. MGRD decomposes graph representations into shared and complementary subspaces. The shared subspace captures view-invariant semantics while preserving local graph structure through a graph-anchored manifold prior, whereas the complementary subspace captures residual view-specific information beyond the shared representation and is regularized by asymmetric consistency and subspace decorrelation. By jointly exploiting shared and complementary representations, MGRD balances cross-view invariance and representation diversity. Experiments on node-level and graph-level benchmarks show that MGRD consistently improves multiple GCL backbones, while theoretical and empirical analyses demonstrate its effectiveness in mitigating dimensional collapse.

Learning meaningful representations from cellular microscopy images remains a central challenge in computational biology and computer vision. Recent advances in self-supervised learning (SSL) have shown that scaling model and dataset size can improve the recovery of biological relationships such as gene–gene or compound–target associations. However, this progress has been driven primarily by scale rather than structure—requiring vast data and compute while offering no guarantee that the resulting embeddings capture underlying biology. We introduce NOVA, a structure-first framework for learning biologically aligned representations of cellular perturbations. NOVA integrates curated biological graphs directly into the training objective of a vector-quantised transformer, aligning embeddings of related genes and pathways through graph-based regularisation. By incorporating these relational priors during training, NOVA learns biologically faithful representations with orders of magnitude less data and compute than current state-of-the-art SSL models, while achieving superior performance on gene-gene retrieval benchmarks. This approach reframes representation learning for microscopy from a problem of scale to one of structure, offering a more systematic and efficient route toward biologically grounded embeddings.

Graph Neural Networks (GNNs) perform computations on graphs by routing the signal between graph regions using a graph shift operator or a message passing scheme. Often, the propagation of the signal leads to a loss of information, where the signal tends to diffuse across the graph instead of being deliberately routed between regions of interest. Two notions that depict this phenomenon are oversmoothing and oversquashing. In this paper, we propose an alternative approach for modeling signal propagation, inspired by quantum mechanics, using the notion of observables. Specifically, we model the place in the graph where the signal lies, how much the signal is concentrated there, and how much of the signal is propagated towards a location of interest when applying a GNN. Using these new concepts, we prove that standard spectral GNNs have poor signal propagation capabilities. We then propose a new type of spectral GNN, termed Schrödinger GNN, which we show has a superior capacity to route the signal across the graph.

Graph foundation models have emerged as a promising paradigm for generalizing across diverse structural tasks. Current approaches serialize graphs into node token sequences and apply autoregressive or masked prediction, yet this node sequential modeling disrupts the relation-centric nature of graphs and violates permutation equivariance. We propose **relational diffusion**, a framework that reconceptualizes graph modeling through edge co-occurrence modeling. By treating edges as fundamental modeling units and learning their joint distribution via discrete diffusion processes, our approach naturally respects the relational semantics of graphs while maintaining permutation equivariance. We introduce topological edge tokenization to enrich edge representations with multi-hop structural context, and relational prompt completion to unify node classification, link prediction, and graph classification as sequence completion problems over edge representations. Through extensive experiments, we demonstrate that relational diffusion achieves strong performance while providing a principled foundation aligned with the relational nature of graphs.

Existing depth-compression methods for large language models (LLMs) operate on layers individually or restrict grouping to adjacent or contiguous positions, implicitly assuming functional redundancy is local. We challenge this assumption by computing full pairwise Centered Kernel Alignment (CKA) similarity matrices across thirteen LLMs spanning six architecture families and 1.6B--14B parameters, revealing that non-adjacent layers exhibit 3.6--15.9$\times$ more high-similarity pairs than adjacent ones. We propose Fusion via Community Mining (FCM), which builds the full $L \times L$ similarity graph, applies spectral community detection to recover clusters of functionally equivalent layers, retains the highest-Block-Influence layer per cluster, and applies LoRA recovery. At 2$\times$ compression, FCM lowers mean perplexity below ShortGPT on twelve of thirteen models, with statistically significant improvements on ten under a Welch's $t$-test with Holm--Bonferroni correction (up to 50\% on Qwen3-4B, $p_\text{corr}<0.001$); two rows are ties and Mistral-7B is the lone loss ($+$14.3\%). On three anchor models, FCM also outperforms three recent adjacent-pair methods (FlattenGPT, SWM, DeltaLLM) by 7.8--35.3\% under matched calibration and recovery. The perplexity advantage transfers downstream: on a five-benchmark zero-shot reasoning suite (ARC-Easy, ARC-Challenge, HellaSwag, WinoGrande, PIQA) over four anchor models, FCM matches or exceeds ShortGPT on 15 of 20 (model, benchmark) cells, and the 2$\times$-compressed Qwen2.5-7B realises 46\% fewer parameters, 42\% lower peak VRAM, and 84\% higher decode throughput on a single A100. Ablations confirm spectral grouping outperforms Fisher contiguous segmentation by 41\% and uniform partitioning by 51\%, and a compression sweep reveals a 2$\times$ crossover beyond which FCM's advantage grows monotonically and persists at the 14B scale. Code: \url{https://anonymous.4open.science/r/fcm-layer-fusion-D820}.

Self-supervised learning (SSL) has received notable attention in the graph machine learning community, enabling the efficient use of unlabeled data. However, evaluation setups vary significantly across studies, and baseline tuning often receives limited attention, making it difficult to draw reliable conclusions about model performance. In this paper, we reevaluate four representative graph SSL methods within a unified setup across a diverse set of datasets. We focus on rigorously tuning both SSL and supervised methods by employing enhanced GNN architectures and comprehensive hyperparameter optimization. We observe that, contrary to prior literature, two popular generative methods, MaskGAE and GraphMAE, regularly fail to outperform well-tuned supervised baselines. In contrast, the contrastive methods BGRL and GRACE consistently perform better than both the generative methods and supervised baselines. We hypothesize that this discrepancy arises because BGRL and GRACE capture information from both graph structure and node features, whereas MaskGAE and GraphMAE focus on a single source of information. We support this hypothesis through an analysis on carefully designed synthetic data. Motivated by our observations, we advocate for designing SSL objectives that capture both feature and structural information. To verify the effectiveness of this approach, we propose a simple generative method, GrASP, which reconstructs both graph structure and node features. Despite its simplicity, GrASP outperforms all other evaluated approaches and can be considered as a strong baseline in future studies.

Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, current evaluation frameworks for Explainable AI (XAI) in this domain often rely on artificial datasets or simplified tasks, employing data-derived metrics that fail to capture the complexity of real-world scenarios and lack a direct link to explanation faithfulness. To address this, we introduce B-XAIC, a novel benchmark constructed from real-world molecular data and diverse tasks with known ground-truth rationales for assigned labels. Through a comprehensive evaluation using B-XAIC, we reveal limitations of existing XAI methods for Graph Neural Networks (GNNs) in the molecular domain. This benchmark provides a valuable resource for gaining deeper insights into the faithfulness of XAI, facilitating the development of more reliable and interpretable models.

Graph neural ordinary differential equations provide continuous-time propagation on graphs, but their long-time behavior is largely determined by the asymptotic structure of the mixing operator. We show that diffusion-style Graph ODEs with positive irreducible mixing face a *monostability trap*, where node features converge to a rank-one consensus subspace. This reveals a structural limitation of positive diffusion-style continuous graph propagation: within this consensus class, exact long-time avoidance requires changing the effective support; otherwise, smooth suppression should be interpreted as finite-horizon or metastable mitigation. We address this by introducing a *hysteretic mechanism* for topology evolution, where candidate edges carry latent bistable potentials in a double-well landscape. A node-feature-conditioned force field evolves these potentials, and a smooth sigmoid gate converts them into differentiable propagation weights. We prove the consensus trap theoretically, show that idealized support separation yields block-wise rather than global consensus, and evaluate the framework on real-world benchmarks.

Operator learning has been highly successful for continuous mappings between infinite-dimensional spaces, such as PDE solution operators. However, many operators of interest—including differential operators—are discontinuous or set-valued, and lie outside classical approximation frameworks. We propose a paradigm shift by formulating approximation via graph convergence (Painlevé–Kuratowski convergence), which is well-suited for closed operators. We show that uniform and $L^p$ approximation are fundamentally inadequate in this setting. Focusing on maximally monotone operators, we prove that any such operator can be approximated in the sense of local graph convergence by continuous encoder–decoder architectures, and further construct structure-preserving approximations that retain maximal monotonicity via resolvent-based parameterizations.

Message-passing neural networks (MPNNs) learn node representations by iteratively aggregating information from neighbors, but stacking too many layers causes all representations to converge, a phenomenon known as oversmoothing. We propose a geometric perspective on this phenomenon. At each depth $k$, the propagation operator assigns to every node $v$ a walk distribution $p_v^{(k)}$, a probability distribution over all nodes describing how $v$ distributes its attention across the graph. These walk distributions form a point cloud on the probability simplex, and oversmoothing is the contraction of this cloud to a single point. To measure this contraction, we exploit the intrinsic geometry of the simplex: the Fisher--Rao metric, the unique Riemannian metric invariant under sufficient statistics, whose chordal distance under the square-root embedding $p \mapsto \sqrt{p}$ is the Hellinger distance. The resulting diagnostic, the \emph{aggregation dispersion} $\mathcal{D}_k$, is the variance of the embedded point cloud on the unit sphere. We prove that: (i) $\mathcal{D}_k = 0$ if and only if the $k$-th step propagation matrix has rank one, providing a necessary and sufficient condition for representational collapse; (ii) $\mathcal{D}_k$ is monotonically non-increasing with depth, so each layer irreversibly spends a finite diversity budget; (iii) the spectral gap of the propagation matrix controls the rate of this decay; and (iv) for contractive MPNNs, the aggregation dispersion upper-bounds the representation dispersion up to architecture-dependent constants, connecting the geometry of the simplex to the geometry of the feature space. In experiments across 14 model configurations and 11 datasets, $\mathcal{D}_k$ achieves the highest average Pearson correlation with accuracy degradation among all training-free diagnostics ($|r| = 0.721$), outperforming the post-training effective rank ($|r| = 0.688$).

Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods predominantly rely on a small set of fixed, handcrafted views---typically a local and a global perspective---which fundamentally constrains their capacity to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded in fractional-order continuous dynamics. By systematically varying the fractional derivative order $\alpha\in(0,1]$, our encoders produce a continuous spectrum of semantically distinct views: small values of $\alpha$ induce localized, memory-attenuated feature propagation, whereas values approaching $1$ recover broader, global aggregation. Crucially, we treat $\alpha$ as a learnable parameter, enabling the model to automatically identify informative views in a data-driven manner, without resorting to manual view engineering. Extensive experiments on standard node- and graph-level benchmarks demonstrate that the resulting representations are more robust and expressive, consistently outperforming state-of-the-art GCL baselines.

Node classification in multimodal graphs plays an important role in many real-world applications, where nodes are described by multiple modalities such as text and images, and the graph structure captures relations among entities. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns. Recent studies have explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by this line of work, we propose Adaptive Gated Simplicial Propagation (AGSP), an end-to-end framework for node classification in multimodal graphs. Specifically, AGSP first introduces a simplicial propagation layer to capture higher-order relations beyond pairwise connections; then it applies an adaptive gating mechanism to balance multi-order topological relations, enhancing the discriminative ability of node classification. Extensive experiments validate that AGSP generally outperforms the state-of-the-art baselines, highlighting the effectiveness of combining multimodal information with higher-order graph topology through adaptive gated fusion.

Attribute-Missing Graph Clustering (AMGC) is a critical yet challenging task in real-world applications, in which only a subset of nodes hold complete attributes information while others are partially missing. Though existing feature propagation-based methods can effectively recover missing node attributes, they commonly assume uniform contributions across all nodes, ignoring unreliable nodes that may degrade imputation quality. Furthermore, adopting low-pass graph filters often suffers from the over-smoothing problem and inevitably sacrifices discriminative information. %Besides, traditional contrastive learning pulls all the node embeddings evenly, which might conflict with the rule that intra-cluster nodes should be closer to each other. To address these limitations, we propose \underline{\textbf{A}}daptive \underline{\textbf{F}}eature \underline{\textbf{P}}ropagation (AFP) for attribute-missing graph clustering. Specifically, we first design a reliability-aware feature propagation mechanism that adaptively weights edges based on node importance. Then, we introduce a multi-scale embedding module to capture both local and global structural information. Finally, we develop a topology-aware contrastive loss to enhance clustering consistency. Extensive experiments on benchmark datasets demonstrate the superiority of our method.

Graph representation learning often relies on message passing or spectral/positional encodings to summarize graph structure, but these indirect summaries can collapse structurally distinct graphs, including non-isomorphic cospectral pairs. We propose A2I, a structural encoding framework that renders BFS--degree-ordered local adjacency patterns as fixed-resolution images and embeds them with a frozen pre-trained vision encoder. The resulting structural tokens are mapped to learnable prototypes and aggregated with node embeddings via a lightweight Transformer. We provide a conditional analysis with two related findings: a stability result, showing that two orderings of the same graph yield embeddings that converge under bounded-degree assumptions, and a separability result, showing that two distinct graphs yield embeddings whose gap grows at least linearly with the difference in their BFS profiles. Empirically, A2I is competitive with recent GNN, Graph Transformer, and structural-encoding baselines on six node-classification benchmarks, with pronounced gains for GCN-based models on heterophilous graphs and under partial feature masking.

Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods typically address these requirements with separate models and objectives. A key obstacle is the lack of a single learned representation whose structure can serve all three tasks through different readouts. Inspired by spectral approaches in graph signal processing and polynomial chaos theory, we model uncertainty-bearing node embeddings as random graph signals: graph Fourier filters capture structural variation, while a scalar orthogonal-polynomial chaos coordinate captures latent stochastic variation. This yields a \emph{doubly-spectral stochastic} (DSS) expansion in which the mean coefficient carries evidence for classification and energy-based OOD scoring, higher-order coefficients encode structured logit variation, and quadrature averaging turns that variation into a calibration-sensitive predictive distribution. Calibration thus uses the quadrature-averaged predictive distribution, OOD detection uses the mean-logit energy score, and distribution-shift robustness uses the corresponding DSS branch as a regularized spectral residual when paired with a deterministic encoder. Under mild conditions this representation universally approximates Gaussian-latent random graph signals with exponentially decaying truncation error. The resulting model, \emph{DSS-GNN} (Doubly-Spectral Stochastic GNN), can be used standalone or as a residual branch alongside a deterministic encoder (\emph{DSS-Hybrid}). In standalone form, DSS-GNN achieves the lowest Brier score among the compared uncertainty-aware baselines on all 14 node classification benchmarks without post-hoc correction; in hybrid form, DSS-Hybrid achieves the best AUROC on most node-OOD settings, competitive cross-graph OOD performance, and the strongest shifted accuracy among compared baselines on all 7 GOOD concept-shift benchmarks under standard ERM training.

Most temporal graph learning methods reduce future prediction to discriminative inference over past interactions, focusing on edges or node labels for a largely persistent set of nodes. This view breaks down when new nodes and edges appear, since the future graph becomes a new object with changing size, composition, and structure. We address this gap by framing temporal graph prediction as an inverse topology problem. Instead of predicting edges directly, we first predict a multiscale topological descriptor of the future graph and then reconstruct a plausible future snapshot that realizes this descriptor under inductive constraints. This approach makes global structure and node churn explicit prediction targets and produces forecasted graphs on which downstream tasks can be evaluated without retraining. Across 14 temporal graph datasets, we evaluate TopoGED on node, link and graph property prediction and compare it against state-of-the-art temporal graph models. TopoGED achieves a significant improvement in node-level forecasting accuracy over the strongest baseline, with a macro average of 0.49 versus 0.05. It also outperforms baselines in 60% of graph-structure metric evaluations and yields a substantial increase in macro-average edge prediction metrics, from near-zero to 0.12. Our results show that topology-guided graph forecasting can predict inductive future snapshots whose structure supports multiple downstream evaluations.

Dynamic link prediction requires determining whether the historical interactions of a source node provide reliable structural support for a specific target. Existing target-conditioned methods capture such support through structural heuristics such as repeated interactions or co-neighbor overlap, but typically instantiate them as identity-based encodings and inject them as passive token-level features before aggregation. This design is brittle when exact structural encodings are sparse, and the resulting structural signals can be diluted after being concatenated with high-dimensional time and edge features, while their dynamics remain unmodeled. In this paper, we revisit target-conditioned dynamic link prediction from a structural dynamics modeling perspective and propose TCSD, which formulates structural signals as target-conditioned states, evolves them over recent histories, and uses them to guide aggregation. Concretely, we construct hybrid structural states from exact indicators that preserve time-aware repeat and co-neighbor matches, and relaxed indicators that recover latent support beyond exact identity matching. TCSD further models the dynamics of these states to capture temporal consistency within indicators and complementarity across indicators. Instead of treating structural states as passive features, TCSD uses the learned dynamics as aggregation controllers to amplify target-relevant interactions and suppress irrelevant ones. Experiments on sixteen dynamic graphs show that TCSD outperforms ten baselines, achieving up to a 14.27% relative improvement in MRR.