IJCAI 2026 Papers

Inferring latent social ties from large-scale spatiotemporal mobility traces is a foundational AI task with broad applicability. Existing hypergraph-based methods often model higher-order relations by treating hyperedges as static snapshots, thus failing to capture the temporal dynamics and co-evolution of user interactions. Meanwhile, many approaches still struggle to distinguish stable social gatherings from transient noisy co-occurrences. To address these challenges, we propose a novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies. The framework consists of two complementary components: (1) a temporal meeting graph channel that decomposes multi-user interactions into ordered snapshots and aligns trajectory evolution through message propagation, and (2) a gathering hypergraph channel that uses a structure-aware encoder with homogeneity-guided aggregation to filter noise in high-order co-occurrences. A mutual-information-aware fusion module integrates both views while preserving their distinct semantics. Extensive experiments across three real-world datasets show that HyGNN consistently outperforms state-of-the-art baselines in PRAUC and ROCAUC metrics, and robustly captures the nonlinear interplay between mobility and sociality. The code is available at https://github.com/KunLiangChen/HyGNN.

Hyper-relational Knowledge Graphs (HKGs) extend traditional knowledge graphs by introducing high-order dependencies, where auxiliary qualifiers provide detailed information. However, modeling such intricate topologies (e.g., mixtures of hierarchies, cycles, and chains) is challenging. Existing methods suffer from structural distortion due to reliance on a single geometric space and overlook dynamic semantics across entity roles. To address these issues, we propose AdaGeM (Adaptive Geometric Learning on Manifolds), an innovative space-adaptive framework that integrates geometric learning with a role-aware Transformer. First, we propose an adaptive geometric hypergraph encoder that projects entities into a product manifold and design a topology-aware gating mechanism to dynamically select optimal geometric spaces for each entity. Second, we develop a role-aware Transformer equipped with role-specific projections and micro-structural bias injection to refine embeddings by distinguishing entity semantics across different roles and focusing on valid n-ary interactions, respectively. Extensive experiments on benchmark datasets demonstrate that AdaGeM outperforms state-of-the-art baselines in entity/relation prediction tasks, with ablation studies validating the necessity of multi-manifold modeling for heterogeneous HKG structures.

Reconstruction-based frameworks are widely adopted in Time Series Anomaly Detection (TSAD), assuming that models reconstruct normal behavior well but yield larger errors on anomalies. However, in unsupervised TSAD, minimizing reconstruction loss alone often breaks this assumption. Models tend to overfit local patterns for trivial reconstruction and fail to capture the global dependencies that characterize normal behavior. Consequently, anomalies that violate global dependencies can also be reconstructed well, leading to a misalignment between reconstruction and detection. To address this challenge, we propose AnoMamba, a novel TSAD framework that aligns reconstruction with anomaly detection by enhancing global dependency modeling. Specifically, AnoMamba employs patch embedding to reduce local redundancy and introduces a Mamba variant with global step-size reweighting to select meaningful global dependencies. The reweighting process is guided by multi-scale long-tail priors, which adaptively balance global and local dependencies to mitigate overfitting in the unsupervised setting. Extensive experiments on both univariate and multivariate benchmarks demonstrate that AnoMamba consistently outperforms state-of-the-art methods in both accuracy and efficiency, while offering interpretability through its hidden attention map.

Recent generative AI renders multimodal misinformation structurally harder to detect, making reliable detection dependent on semantic verification grounded in verifiable evidence. However, current benchmarks often fail to isolate true semantic checking from superficial shortcut exploitation. We introduce Falsdo, a diagnostic benchmark designed to make robustness and auditability identifiable. Constructed via a heterogeneous web-mining pipeline, Falsdo provides instruction-conditioned grounding and formalizes a counterfactual artifact-control protocol. This stress test reveals a critical failure: when low-level generation traces are equalized, detector performance degrades, indicating reliance on artifacts rather than robust verification. To enable reproducible diagnosis, we propose DREA, a failure-aligned evidence-auditing baseline. DREA specifies evidence acquisition with explicit noise control and implements constrained channel-wise auditing with reliability-aware late fusion. Experiments demonstrate that Falsdo reliably exposes shortcut dependence, while DREA improves instruction-level grounding (Joint-F1) and minimizes degradation under artifact-controlled settings.

Learning universal representations for time series is fundamental for diverse downstream tasks. However, current approaches largely rely on handcrafted data augmentations, which may distort intrinsic temporal dynamics and structural regularities. In addition, most static representation learning frameworks struggle to cope with the non-stationary nature of real-world time series. To address these issues, we propose Heterogeneous Hypergraph Structure-aware Contrastive Adaptive Network (H²SCAN), a novel augmentation-free framework that derives contrastive supervision directly from graph topology. Specifically, H²SCAN constructs a heterogeneous hypergraph with three node types to capture multi-scale temporal characteristics. Building upon this representation, a meta-adaptation network is introduced to dynamically reweight heterogeneous hyperedges, enabling the model to adapt to distribution shifts in real-time. Finally, a structure-aware contrastive learning objective is employed to align latent representation similarity with the intrinsic hypergraph topology. Experiments on multiple benchmarks and cloud Kafka cluster datasets demonstrate that H²SCAN outperforms existing methods by modeling high-order multi-domain dependencies and preserving the semantic integrity of time series data.

Accurate user behavior modeling is fundamental to the prediction of click-through rates (CTR) in industrial recommendation systems and online advertising. Traditional discriminative models, which rely on isolated ID features, struggle to capture the evolving nature of user intents across multiple channels due to the inherent semantic gap. While Large Language Models (LLMs) offer rich semantic understanding to bridge this gap, their direct application faces two main challenges: modality misalignment between continuous semantic representations and discrete item IDs, and prohibitive computational costs that make them unsuitable for real-time inference. To address these issues, we propose the Hierarchical Semantic Interest Evolution Network (HSIEN), a novel generative-discriminative framework. HSIEN leverages an LLM to construct a Markovian state-space model that recursively captures and updates user interests from multi-channel behavior sequences. It then employs a Semantic Mixture-of-Experts (SMoE) strategy to aggregate multi-channel signals into hierarchical intent representations, which are encoded as compact dynamic semantic vectors for integration with downstream discriminative models. Extensive experiments on a public dataset and a large-scale industrial dataset demonstrate that HSIEN significantly alleviates modality misalignment and enhances CTR prediction performance through feature complementarity. This work provides a practical and efficient pathway for leveraging LLMs in large-scale recommendation scenarios.

Emerging foundation models have spurred growing interest in task-unspecific time series modeling, which can accommodate data from diverse domains and support various tasks. However, most existing methods still suffer from poor adaptability and generalization across cross-domain time series with varying feature dimensionalities. This study achieves unified time series modeling to transcend task and data boundaries, inspired by the recent advances in visual foundation models. We propose a universal cross-modal Time series modeling method named TimeIG, featuring Tailored Temporal Imaging and Dependent Channel Grouping. Specifically, TimeIG adopts the novel dependent channel grouping method to project heterogeneous time series into hierarchical spaces to identify representative features and learn inter-dependencies across variables. Then, a tailored temporal imaging method is proposed to generate carefully hand-crafted images from time series, facilitating complementary temporal-spatial feature extraction. Finally, a dual-branch module is designed to simultaneously model the sequential and visual data, enabling cross-modal alignment via the proposed Cross-Modal Attention and Dynamic Weighted-Averaging. Extensive experiments on real-world cross-domain time series datasets show that TimeIG consistently outperforms existing SOTA methods. The code of TimeIG is publicly available at https://github.com/missCmj/TimeIG.

Attributing model behavior to training data is an evolving research field. A common benchmark is data removal, which involves eliminating data instances with either low or high values, then assessing a model's performance trained on the modified dataset. Many existing studies leverage Shapley-based data values for this task. In this paper, we demonstrate that these data values are not optimally suited for pruning low-value data when only a limited amount of data remains. To address this limitation, we introduce the Constraint-Data-Value-Maximization (CDVM) approach, which effectively utilizes data attributions for pruning in low-data scenarios. By casting pruning as a constrained optimization that both maximizes total influence and penalizes excessive per-test contributions, CDVM delivers robust performance when only a small fraction of the data is retained. On the OpenDataVal benchmark, CDVM shows strong performance and competitive runtime.

The Weighted Partial MaxSAT (WPMS) problem requires finding an assignment that satisfies all hard clauses while maximizing the total weight of satisfied soft clauses. From a computational perspective, WPMS is not merely an extension of the uniform-weight Partial MaxSAT (PMS); rather, it requires search strategies that actively exploit weight information to navigate the solution space. While state-of-the-art heuristic and SAT-based solvers have successfully integrated weight-aware mechanisms, Branch-and-Bound (BnB) solvers, notably WMaxCDCL, largely treat weight information passively during search and pruning. In this paper, we bridge this gap by introducing two novel weight-aware strategies into the BnB framework. Our methods integrate weight information directly into the preprocessing and lower-bound estimation. Experimental results demonstrate that these strategies significantly enhance the performance of WMaxCDCL, establishing a new state-of-the-art for exact WPMS solving.

Multi-objective bandits with hierarchical preferences and safety constraints is central to many real-world decision-making tasks such as healthcare treatment planning and safe autonomous control, where multiple objectives must be optimized according to their priorities while ensuring safety requirements are satisfied. In this paper, we study a multi-objective stochastic linear bandit framework that incorporates hierarchical preferences together with safety constraints, requiring the learner to remain competitive with respect to a known baseline policy. We consider two practically motivated safety models: (i) cumulative constraints, which require the cumulative performance to exceed the baseline, and (ii) stage-wise constraints, which impose this requirement at each time step. We propose two algorithms, LexUCB-C and LexTS-S, designed for the cumulative and stage-wise settings, respectively. We establish regret bounds showing that both algorithms achieve performance comparable to existing single-objective safe linear bandit methods, while simultaneously optimizing multiple objectives. In addition to theoretical guarantees, we develop a carefully designed experimental framework that captures the interaction between hierarchical preferences and safety constraints. Experiments on synthetic and real-world datasets validate our theory and demonstrate the effectiveness of the proposed methods.

Propositional Model Counting (#SAT) is essential for probabilistic reasoning but faces scalability limits on single cores. Existing distributed approaches struggle with high initialization overheads (static decomposition) or rigid architecture. We propose a novel, generic framework for distributed exact model counting. Leveraging C++ templates, our architecture decouples parallel orchestration from solving logic, enabling state-of-the-art solvers to be parallelized with minimal modification. We implement an adaptive work-stealing strategy that ensures effective load balancing. Experiments on competition benchmarks show that our approach achieves near-linear scalability and significantly outperforms existing distributed solvers.

Structured output generation is increasingly critical for real-world AI systems, particularly in on-device settings where small language models (0.5B–8B parameters) must produce machine-executable outputs under strict latency and privacy constraints. Although constrained decoding provides formal guarantees of structural validity without retraining, its effectiveness across different tasks, models, and constraint formalisms remains insufficiently understood. We introduce StructureBench, a comprehensive benchmark for structured generation on edge devices, covering JSON and tool-call generation, code synthesis, mathematics, science, and domain-specific languages, and evaluating over 11 on-device language and vision–language models spanning 0.5B–8B parameters. StructureBench systematically compares prompt-based generation with three widely used constrained decoding frameworks—Outlines, XGrammar, and Guidance—and adopts decoupled metrics to separately assess structural validity and semantic correctness. Our experiments show that constrained decoding consistently enforces syntactic validity, but does not reliably improve semantic accuracy and may even degrade performance for smaller models or complex grammars. These findings reveal clear task- and model-dependent boundaries for effective constrained decoding and highlight the need for scenario-aware adaptation in on-device structured generation. We release StructureBench at https://github.com/Str-Ben/StructureBench.

Singleton consistency significantly reduces the search space of backtracking search for solving constraint satisfaction problems (CSP). However, it is too expensive to be used throughout the search, and it has not been successfully applied in solving general CSPs. In this paper, we propose a variable-oriented adaptive singleton consistency, namely VOASC, that could be applied in general-purpose constraint solvers. It calculates the filtering efficiency of local consistency enforced by the default propagation engine and the singleton-based consistency for each variable, and then selects an appropriate consistency to propagate the decisions of the variable in subsequent searches. We performed extensive experimentation with the MiniZinc benchmark suite. VOASC demonstrates superior solving performance on both CSP and COP, compared to the two candidate local consistencies and several existing adaptive propagation methods.

Model editing updates a pre-trained LLM with new facts or rules without retraining while preserving unrelated behavior. In real deployment, edits arrive as long streams, creating a plasticity–stability dilemma: repeated locate-then-edit “hard writes” can accumulate interference over time, while rigid preservation constraints may protect only explicitly constrained directions, allowing past edits or unconstrained behaviors to deviate. We propose RLSEdit, a recursive least-squares editor for long sequential editing. RLSEdit formulates editing as an online quadratic optimization with soft constraints, minimizing a cumulative key-value fitting objective together with two regularizers that control deviation from the pre-trained weights and from a designated anchor mapping. This objective admits an efficient Woodbury-based online recursion, with per-edit cost independent of history length and scaling only with the current edit size. We further provide deviation bounds and an asymptotic characterization of the adherence–preservation trade-off in the many-edits regime. Experiments on CounterFact and ZsRE across multiple model families show stable scaling to 10K edits, outperforming strong baselines in both edit success and holistic stability, while retaining early edits and preserving general capabilities on GLUE and held-out reasoning/code benchmarks. Code will be at here.

Post-training quantization (PTQ) is widely used to compress large language models without retraining. However, many existing weight-only methods rely on heuristic objectives and greedy rounding strategies, leading to noticeable degradation under low-bit quantization. We propose OJBKQ, a layer-wise PTQ method that formulates weight quantization as a joint optimization problem over activations and weights, yielding a multiple-right-hand-side box-constrained integer least squares (BILS) problem per layer. To solve this NP-hard problem, we extend Klein’s randomized variant of Babai’s nearest-plane algorithm to handle box constraints and run the extended algorithm K times to generate K box-constrained Klein points. The minimum-residual candidate among these points and the box-constrained Babai point is selected as a suboptimal solution. Experiments on large language models show that OJBKQ achieves lower perplexity at 3–4 bits than existing PTQ methods, while maintaining comparable computational cost.

Fairness k-submodular maximization has attracted increasing interest due to its broad relevance in artificial intelligence and machine learning. However, most existing works are limited to monotone objectives or simple size constraints, while non-monotone settings with richer constraints remain largely unexplored. In this paper, we first introduce a constant-factor approximation algorithm for the problem with a general non-monotone objective function under a matroid constraint. Our approach is built upon a two-stage algorithmic framework. Specifically, we first develop an algorithm that guarantees feasibility with respect to upper fairness bounds only. We then show how this algorithm can be systematically extended to simultaneously enforce fairness bounds, while preserving provable approximation guarantees. Comprehensive experiments on standard benchmark datasets demonstrate that our algorithm achieves competitive objective values while maintaining a favorable balance between fairness guarantees and query complexity efficiency compared to existing state-of-the-art methods.

The Kidney Exchange Problem is a prominent challenge in healthcare and economics, arising in the context of organ transplantation. It has been extensively studied in artificial intelligence and optimization. In a kidney exchange, a set of donor-recipient pairs and altruistic donors are considered, with the goal of identifying a sequence of exchanges—comprising cycles or chains starting from altruistic donors—such that each donor provides a kidney to the compatible recipient in the next donor-recipient pair. These exchanges create a network of transplants aimed at maximizing the total number, t, of successful transplants. Due to constraints in medical resources, limits are often imposed on the lengths of these cycles and chains. Recently, this problem was deterministically solved in O* (14.34ᵗ) time (IJCAI 2024). In this paper, we introduce the representative set technique for the Kidney Exchange Problem, showing that the problem can be deterministically solved in O* (6.855ᵗ) time.

Join ordering is the NP-hard problem of selecting the most efficient order in which to evaluate joins (conjunctive, binary operators) in a database query. Because query execution performance critically depends on this choice, join ordering lies at the core of query optimization. Traditional approaches cast this problem as a discrete combinatorial search over binary trees guided by a cost model, but they have trade-offs between effectiveness and efficiency. We show that when the cost model is differentiable, query plans can be continuously relaxed into a soft adjacency matrix that represents a superposition of plans. This continuous relaxation, combined with differentiable constraints that enforce plan validity, enables a gradient-based search for low-cost plans within this relaxed space. Using a Graph Neural Network as the cost model, we demonstrate that this gradient-based approach can find comparable and even lower-cost plans compared to traditional discrete search methods on two different graph datasets. Furthermore, we empirically show that the runtime of this approach scales better than discrete search algorithms. We believe this first step towards gradient-based join ordering can lead to more effective and efficient query optimizers in the future.

When solving optimization problems under uncertainty with contextual data, utilizing machine learning to predict the uncertain parameters' values is a popular and effective approach. Decision-focused learning (DFL) aims at learning a predictive model such that decision quality, instead of prediction accuracy, is maximized. Common practice is to predict a single scenario representing the uncertain parameters, implicitly assuming that there exists a deterministic problem approximation (proxy) that allows for optimal decision-making. The opposite has also been considered, where the underlying distribution is estimated with a parameterized distribution. However, little is known about when either choice is valid. This paper investigates for the first time problem properties that justify using a certain decision proxy. Using this, we present alternative decision proxies for DFL, with little or no compromise on the complexity of the learning task. We show the effectiveness of presented approaches in experiments on continuous and discrete problems, as well as problems with uncertainty in the objective function and in the constraints.