AAAI 2026 论文列表

While deep learning (DL) has demonstrated significant success in recommender systems, it suffers from high computational complexity and poor scalability. In this work, we demonstrate, from an information-theoretic perspective, the redundancy of existing DL-based recommender models in two aspects: (1) Feature Redundancy. We show that many features are highly mutually correlated, noisy, or weakly predictive of user-item interaction labels. (2) Structural Redundancy. We further show that a large proportion of parameters in the dense layers contribute minimally to overall performance, indicating significant redundancy within the model architecture. To address these challenges, we propose REACTION (paRameter-Efficient LeArning for recommendaTION), an information-theoretic framework designed to reduce model complexity without sacrificing performance. REACTION consists of two core components: Adaptive Feature Extraction (AFE) leverages mutual information to project high-dimensional sparse features into a compact, informative subspace. This adaptively filters noisy or weak features, reduces embedding parameters, and preserves implicit feature interactions without explicit high-order computation. Dynamic Tower Fusion (DTF) bridges the representational gap between dual-tower expressiveness and single-tower efficiency. It facilitates rich cross-tower interactions during training, then merges the towers into a unified, low-latency single tower for inference. Extensive experiments on four large-scale benchmarks demonstrate that REACTION not only outperforms existing methods in accuracy but also achieves a drastic reduction in both model parameters and inference costs, thus establishing a new paradigm for efficient and scalable recommendation systems.

Graph Structure Learning (GSL) aims to simultaneously enhance the original graph and the performance of Graph Neural Networks. However, existing GSL methods for node classification fail to consider neighborhood label dependencies during training, which limits their ability to refine the graph structure in an adaptive manner. Furthermore, the training of those methods lacks a proper schedule based on graph structure quality, thereby yielding suboptimal performance. To address these challenges, we propose a novel GSL framework for node classification, termed DuAl hypeRgraph-enhanced curricuLum-guided graph structure learnING for node classification (DARLING). It first introduces a graph structure curriculum module to effectively discriminate the suboptimal graph structures by examining both the distribution of neighborhood labels and the degree of nodes. Subsequently, a self-supervised dual hypergraph similarity learning module is proposed to capture higher-order neighborhood label dependencies. This is achieved via formulating a pre-training task that involves hyperedge batch-filling within the dual hypergraph of the input graph. The experimental results on six datasets demonstrate that the proposed DARLING outperforms eleven state-of-the-art methods significantly, in terms of effectiveness and robustness.

Cross-Domain Recommendation (CDR) transfers user preferences from a source domain to alleviate data sparsity in a target domain. While disentangling representations into domain-specific and shared components is a common method, existing methods overlook user preference heterogeneity and item appeal heterogeneity. To this end, we propose DPGCDR, a Dual-Perspective Group-aware CDR method that learns symmetric group-aware representations from both user and item. Conceptually, DPGCDR dynamically clusters users into groups and items into themes, then symmetrically disentangles user preferences into group-specific and cross-group shared components, and item attributes into theme-specific and cross-theme shared components. We propose a two-stage training scheme: 1) an initial warm-up stage learns preliminary representations to dynamically cluster users and items into group and theme structures which generalize cross-domain scenarios into multi-group disentanglement analogous to multi-domain settings; 2) a fusion-based aggregation stage integrates these group/theme-specific components into unified global representations. Additionally, an information-theoretic alignment regularizer further ensures consistency and discriminability between global shared and group/theme-specific representations, facilitating effective knowledge transfer by explicitly modeling and preserving the inherent multi-group structure within cross-domain interactions. Extensive experiments show DPGCDR achieves state-of-the-art performance, with significant gains of up to 25% in HR@10 over baselines on datasets with heterogeneous interaction structures. Further analyses confirm our dynamic clustering mechanism effectively adapts to underlying data patterns, enabling fine-grained cross-domain knowledge transfer.

Precipitation nowcasting, a critical task for weather-sensitive applications, is highly challenging owing to the chaotic nature of atmospheric dynamics. Despite recent progress in deep learning, existing methods are limited in their capacity to model turbulent motions, one of the key drivers of precipitation evolution. Thus, we propose MoCast, the first work that incorporates turbulence knowledge to decompose turbulent motions into solvable components for precipitation nowcasting. Specifically, inspired by the continuity equation, MoCast introduces two core innovations: (1) a physics-guided motion module that learns turbulent motions from physically interpretable mean and fluctuating components based on Reynolds, Helmholtz, and Wavelet decomposition techniques, and (2) a motion-guided source-sink module that learns source-sink features considering the multi-scale impact from motions based on a mixture-of-experts architecture. Extensive experiments on three real-world datasets demonstrate that MoCast achieves the state-of-the-art performance. MoCast and its diffusion-based variant MoCast+ reduce CSI error by an average of 4.9% and 4.5% compared to the best deterministic and probabilistic baselines, respectively.

Accurate air quality forecasting is essential for public health and environmental sustainability, but remains challenging due to the complex pollutant dynamics. Existing deep learning methods often model pollutant dynamics as an instantaneous process, overlooking the intrinsic delays in pollutant propagation. Thus, we propose AirDDE, the first neural delay differential equation framework in this task that integrates delay modeling into a continuous-time pollutant evolution under physical guidance. Specifically, two novel components are introduced: (1) a memory-augmented attention module that retrieves globally and locally historical features, which can adaptively capture delay effects modulated by multifactor data; and (2) a physics-guided delay evolving function, grounded in the diffusion-advection equation, that models diffusion, delayed advection, and source/sink terms, which can capture delay-aware pollutant accumulation patterns with physical plausibility. Extensive experiments on three real-world datasets demonstrate that AirDDE achieves the state-of-the-art forecasting performance with an average MAE reduction of 8.79% over the best baselines.

Modeling user interest based on lifelong user behavior sequences is crucial for enhancing Click-Through Rate (CTR) prediction. However, long post-click behavior sequences themselves pose severe performance issues: the sheer volume of data leads to high computational costs and inefficiencies in model training and inference. Traditional methods address this by introducing two-stage approaches, but this compromises model effectiveness due to incomplete utilization of the full sequence context. More importantly, integrating multimodal embeddings into existing large recommendation models (LRM) presents significant challenges: These embeddings often exacerbate computational burdens and mismatch with LRM architectures. To address these issues and enhance the model's efficiency and accuracy, we introduce Deep Multimodal Group Interest Network (DMGIN). Given the observation that user post-click behavior sequences contain a large number of repeated items with varying behaviors and timestamps, DMGIN employs Multimodal LLMs(MLLM) for grouping to reorganize complete lifelong post-click behavior sequences more effectively, with almost no additional computational overhead, as opposed to directly introducing multimodal embeddings. To mitigate the potential information loss from grouping, we have implemented two key strategies. First, we analyze behaviors within each group using both interest statistics and intra-group transformers to capture group traits. Second, apply inter-group transformers to temporally ordered groups to capture the evolution of user group interests. Our extensive experiments on both industrial and public datasets confirm the effectiveness and efficiency of DMGIN. The A/B test in our LBS advertising system shows that DMGIN improves CTR by 4.7% and Revenue per Mile by 2.3%.

Retrieval-Augmented Generation (RAG) has significantly enhanced Large Language Models' ability to access external knowledge, yet current graph-based RAG approaches face two critical limitations in managing hierarchical information: they impose rigid layer-specific compression quotas that damage local graph structures, and they prioritize topological structure while neglecting semantic content. We introduce T-Retriever, a novel framework that reformulates attributed graph retrieval as tree-based retrieval using a semantic and structure-guided encoding tree. Our approach features two key innovations: (1) Adaptive Compression Encoding, which replaces artificial compression quotas with a global optimization strategy that preserves the graph's natural hierarchical organization, and (2) Semantic-Structural Entropy (S²-Entropy), which jointly optimizes for both structural cohesion and semantic consistency when creating hierarchical partitions. Experiments across diverse graph reasoning benchmarks demonstrate that T-Retriever significantly outperforms state-of-the-art RAG methods, providing more coherent and contextually relevant responses to complex queries.

Spatio-temporal data generation aims to synthesize realistic urban data across graph nodes by learning spatial and temporal dependencies. This task plays a crucial role in urban planning by enabling the simulation of unobserved nodes. However, existing approaches face critical limitations that time series generation methods fail to generalize to unseen nodes, while spatio-temporal generative models are either restricted to the trajectory generation task or dependent on auxiliary data inputs. To bridge these gaps, we propose a Knowledge Graph Guided Heterogeneity-Informed Diffusion Model (KGDiff) in this paper through the following key innovations. First, we design a geometry-aware mixture of experts integrating Euclidean, hyperbolic, and hyperspherical representations to comprehensively encode urban structural knowledge. Next, we present a learnable meta spatio-temporal pattern module that normalizes node-specific heterogeneity before the generation process, and a conditional denoising process that progressively transforms random noise into realistic samples under structural guidance. Finally, extensive experiments across real-world urban datasets demonstrate that KGDiff achieves the state-of-art performance in generating realistic urban spatio-temporal data.

Recent advances in the field of sequential recommendation have highlighted the potential of Large Language Models (LLMs) in enhancing item embeddings and improving user understanding. However, existing approaches face three major limitations: 1) insufficient understanding of the reasons behind users' purchase decisions, 2) the high-dimensional embeddings directly produced by LLMs are not well compatible with traditional low-dimensional ID embeddings and 3) reliance on additional fine-tuning and high inference overhead to adapt LLMs to the recommendation task. In this paper, we propose MoMoREC, a simple yet effective user-understanding-based recommendation strategy. This method leverages the intrinsic comprehension capabilities of LLMs combined with residual semantic IDs to better understand users. Specifically, starting from common user purchasing behaviors and incorporating item characteristics, we employ a multi-agent framework to utilize LLMs in analyzing user shopping motivations and extracting high-dimensional dense embeddings. These embeddings are then transformed into low-dimensional IDs using a residual semantic ID approach via clustering and residual dimensionality reduction, which can be fed into the recommendation model. MoMoREC effectively integrates the understanding power of LLMs with the strengths of recommendation systems, preserving rich semantic language embeddings while reducing or eliminating the need for auxiliary trainable modules. As a result, it seamlessly adapts to any sequential recommendation framework. Experiments on three benchmark datasets show that MoMoRec significantly improves traditional recommendation models, demonstrating its effectiveness and flexibility.

Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a "see-saw" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noise within the tail item set, which existing long-tail approaches fail to identify and constrain effectively. To resolve our fundamental conflict, we propose HID (Hybrid Intent-based Dual Constraint Framework), a plug-and-play framework that transforms the conventional "see-saw" into a "win-win" relationship through introducing the hybrid intent-based dual constraints. Two key innovations are incorporated in this framework: (i) Hybrid Intent Learning, where we reformulate the intent extraction strategies by employing attribute-aware spectral clustering to reconstruct the item-to-intent mapping. Furthermore, discrimination of session-irrelevant noise is achieved through the assignment of both target and noise intents to each sessions. (ii) Intent Constraint Loss, where we propose two novel constraint paradigms regarding the diversity and accuracy to regulate the representation learning process, and unify the two optimization objectives into a unique loss. Extensive experiments across multiple SBR models and datasets demonstrate that HID can enhance both long-tail performance and recommendation accuracy, establishing new state-of-the-art performance in long-tail recommender systems.

Origin-Destination (OD) flow matrices are critical for urban mobility analysis, supporting traffic forecasting, infrastructure planning, and policy design. Existing methods face two key limitations: (1) reliance on costly auxiliary features (e.g., Points of Interest, socioeconomic statistics) with limited spatial coverage, and (2) fragility to spatial topology changes, where reordering urban regions disrupts the structural coherence of generated flows. We propose Sat2Flow, a structure-aware diffusion framework that generates structurally coherent OD flows using only satellite imagery. Our approach employs a multi-kernel encoder to capture diverse regional interactions and a permutation-aware diffusion process that maintains consistency across regional orderings. Through joint contrastive training linking satellite features with OD patterns and equivariant diffusion training enforcing structural invariance, Sat2Flow ensures topological robustness under arbitrary regional reindexing. Experiments on real-world datasets show that Sat2Flow outperforms physics-based and data-driven baselines in accuracy while preserving flow distributions and spatial structures under index permutations. Sat2Flow offers a globally scalable solution for OD flow generation in data-scarce environments, eliminating region-specific auxiliary data dependencies while maintaining structural robustness for reliable mobility modeling.

Sequential recommendation has become indispensable in modern digital services. Prevalent recommendation techniques formulate the recommendation task with a language instruction fed into large language models (LLMs) to generate recommendations. However, the implicit interaction scenario of recommendation task cannot provide explicit reasoning supervision to activate LLM's multi-step reasoning capability. Besides, the manner of reasoning for enhancing recommendation is still underexplored. Therefore, we investigate activating multi-step reasoning with users' interactions and propose a multi-step reasoning-enhanced LLM (MSR-Rec), which tightly integrates reasoning with recommendation from designing reasoning chain to reasoning-based recommendation. A task-decomposed reasoning chain is elaborately designed to imitate users' thinking process, seamlessly involving reasoning into recommendation. Following the reasoning chain, MSR-Rec synthesizes reasoning supervision and fine-tunes LLM to adapt for task-specific reasoning. In inference, bidirectional reasoning is implemented from user and item sides, performing a closed-loop reasoning for recommendation. Comprehensive experiments demonstrate that MSR-Rec achieves the state-of-the-art performance in both recommendation quality and reasoning interpretability, advancing the integration of reasoning and recommendation in LLM-based systems.

Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-the-art RAG approaches often use the graph data as the external data since they capture the rich semantic information and link relationships between entities. However, existing graph-based RAG approaches cannot accurately identify the relevant information from the graph and also consume large numbers of tokens in the online retrieval process. To address these issues, we introduce a novel graph-based RAG approach, called Attributed Community-based Hierarchical RAG (ArchRAG), by augmenting the question using attributed communities, and also introducing a novel LLM-based hierarchical clustering method. To retrieve the most relevant information from the graph for the question, we build a novel hierarchical index structure for the attributed communities and develop an effective online retrieval method. Experimental results demonstrate that ArchRAG outperforms existing methods in both accuracy and token cost.

Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel ("serendipitious") answers. In this paper, we formally define the serendipity-aware KGQA task and propose the SerenQA framework to evaluate LLMs' ability to uncover unexpected insights in scientific KGQA tasks. SerenQA includes a rigorous serendipity metric based on relevance, novelty, and surprise, along with an expert-annotated benchmark derived from the Clinical Knowledge Graph for drug repurposing. Additionally, it features a structured evaluation pipeline encompassing three subtasks: knowledge retrieval, subgraph reasoning, and serendipity exploration. Our experiments reveal that while state-of-the-art LLMs perform well on retrieval, they still struggle to identify genuinely surprising and valuable discoveries, underscoring a significant room for future research.

Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring the reliability of Graph Neural Networks (GNNs) when deployed in open-world scenarios. Recent advances in graph OOD detection have focused on test-time training techniques that facilitate OOD detection without accessing potential supervisory information (e.g., training data). However, most of these methods employ a one-pass inference paradigm, which prevents them from progressively correcting erroneous predictions to amplify OOD signals. To this end, we propose a Self-Improving Graph Out-of-Distribution detector (SIGOOD), which is an unsupervised framework that integrates continuous self-learning with test-time training for effective graph OOD detection. Specifically, SIGOOD generates a prompt to construct a prompt-enhanced graph that amplifies potential OOD signals. To optimize prompts, SIGOOD introduces an Energy Preference Optimization (EPO) loss, which leverages energy variations between the original test graph and the prompt-enhanced graph. By iteratively optimizing the prompt by involving it into the detection model in a self-improving loop, the resulting optimal prompt-enhanced graph is ultimately used for OOD detection. Comprehensive evaluations on 21 real-world datasets confirm the effectiveness and outperformance of our SIGOOD method.

Deep hashing offers efficient storage and fast retrieval capabilities. As a result, it has been extensively applied to large‑scale retrieval tasks. To alleviate the dependence on high-quality annotated data, recent research has focused on unsupervised domain adaptive hashing methods, which aim to transfer knowledge from a label-rich source domain to a label-scarce target domain. However, in open-world scenarios, source domain labels are often inevitably noisy, which tends to undermine the quality of learned hash codes and induce considerable performance deterioration. To this end, we introduce a novel Robust Domain Adaptive Hashing (RDAH) method to jointly mitigate the adverse effects of label noise and domain discrepancy. Specifically, we first model the loss distribution of training samples using a two-component Gaussian mixture model to estimate each sample’s confidence, based on which the data is partitioned. Subsequently, we introduce a neighbor consistency-guided correction strategy, which leverages the semantic structure of high-confidence neighbors to perform weighted correction on noisy samples. Moreover, we design a dual-level cross-domain alignment mechanism that jointly mitigates domain shift from two complementary perspectives. Extensive experimental results validate the effectiveness and robustness of RDAH across multiple benchmark datasets.

Point-of-Interest (POI) recommendation plays a pivotal role in location-based services by guiding users to discover new and relevant places. While graph-based methods have shown promising results, effectively modeling the diversity and dynamics of user preferences remains a key challenge. Addressing this requires richer representations of both POIs and user interests, as well as more adaptive learning strategies. In this work, we propose TMHKG, a Task-aware Meta-learning framework with a Heterogeneous Knowledge Graph for POI recommendation. To enhance representation learning, TMHKG constructs a dual-view POI knowledge graph that integrates geographical proximity and user-aware category transitions, and models users' evolving interests from sequential visit histories. On top of enriched features, TMHKG adopts a task-aware meta-learning paradigm, treating each user's recommendation task as a separate meta-task. A generalizable recommendation policy is first learned from diverse training tasks and then quickly adapted to each user's unique behavior, enabling highly personalized predictions. Extensive experiments on two real-world datasets demonstrate that TMHKG consistently outperforms state-of-the-art baselines, highlighting its effectiveness in capturing complex user-POI interactions.

In recent years, Large Vision-Language Models (LVLMs) have significantly advanced multimodal tasks. However, their inference requires intensive processing of numerous visual tokens and incurs substantial computational overhead. Existing methods typically compress visual tokens either at the input stage or in early model layers, ignoring variations across tasks and depths. To address these limitations, we introduce TOP-RL, a Task-Optimized Progressive token pruning framework based on Reinforcement Learning. TOP-RL formulates visual token pruning as a multi-stage Markov Decision Process (MDP). It employs an agent trained with dense and fine-grained reward signals to progressively generate differentiable binary masks. This enables TOP-RL to adaptively select crucial visual tokens tailored to each task, effectively balancing accuracy and computational efficiency. Extensive experiments on leading multimodal datasets and advanced LVLMs validate that TOP-RL effectively learns task-optimized pruning policies, significantly boosting inference efficiency while preserving robust performance. For instance, LLaVA-NeXT equipped with TOP-RL achieves a 1.9x speedup in inference time and a 9.3x reduction in FLOPs, with 96% performance preserved.

Multi-modal knowledge graph completion (MMKGC) aims to infer missing entities of triples by leveraging heterogeneous information in knowledge graph (KG). However, existing approaches often struggle with inconsistent modality alignment, limited reasoning depth, and insufficient negative sample quality. In this work, we propose HFR-MKGC, a novel framework that integrates hierarchical modal fusion and Multimodal Large Language Model (MLLM) reasoning for robust and expressive MMKGC. Specifically, we introduce a relation-guided hierarchical modal fusion module, which conducts fine-grained intra-visual fusion and relation-guided cross-modal integration to yield rich entity representations. HFR-MKGC employs a fine-tuned MLLM to perform instruction-based triple reasoning, producing candidate entities for completion. Then, it constructs hard negative samples through textual perturbation by MLLM and visual feature augmentation with rotation and noise. HFR-MKGC optimizes the model via adversarial training. Extensive experiments on three MMKGC benchmarks demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness in MMKGC.