GenAI4ELab/papercli-papers huggingface dataset:
Knowledge-Enhanced Explainable Hypergraph Convolution Network for Medication Recommendation
Authors: Zihan Zhang; Hongzhi Liu; Xiaoshuang Guo; Tianqi Sun; Zhonghai Wu
Venue: AAAI 2026 (main)

Abstract: Medication recommendation systems aim to provide personalized and safe medication options based on individual patient records. However, existing approaches often face challenges related to inadequate modeling of complex relationships within Electronic Health Records (EHRs), data sparsity, and a lack of explainability for recommendations. In this paper, we present a Knowledge-enhanced Explainable HyperGraph Convolution Network (KEHGCN) that constructs a hierarchical hypergraph structure to capture the multi-level relationships within EHR data. By incorporating external knowledge graphs, our approach introduces additional positive relations that help alleviate the impact of data sparsity on model learning. Furthermore, by performing generalized metapath construction and selection on the knowledge graph, our approach achieves effective knowledge filtering and extracts semantically meaningful metapaths, thereby further enhancing the explainability of the recommendation results. We also explicitly introduce negative relations present in the domain knowledge to improve the safety of medication recommendation. Extensive experiments on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate that KEHGCN outperforms other state-of-the-art baselines.
TriFusion-IDS: A Multimodal Graph-Tabular-Text Contrastive Framework for Cross-Dataset Intrusion Detection
Authors: Qinxin Zhao; Sheng Zhong
Venue: AAAI 2026 (main)

Abstract: Traditional Intrusion Detection Systems (IDS) are typically trained in specific network environments, and their performance often degrades significantly when deployed in new environments with different attack categories. To address this challenge, we propose and define the task of cross-dataset intrusion detection and design a novel multimodal contrastive learning framework named TriFusion-IDS. This framework represents network traffic from three complementary dimensions: a graph view to capture structural communication patterns, a tabular view to model statistical features, and a textual view to define the semantics of attacks. TriFusion-IDS fuses the graph and tabular representations and aligns them with textual descriptions in a shared embedding space using a CLIP-style contrastive loss function. This semantics-based alignment mechanism enables the model to overcome the effects of zero-shot categories and thus generalize to new network environments. Our extensive experiments on several mainstream datasets demonstrate that this method significantly outperforms existing baselines in cross-dataset intrusion detection scenarios.
ST-VLM: A Spatial-to-Image Multimodal Spatial-Temporal Prediction Framework with Vision-Language Model
Authors: Tong Zhao; Junping Du; Zhe Xue; Meiyu Liang; Aijing Li; Xiaolong Meng; Dandan Liu
Venue: AAAI 2026 (main)

Abstract: Spatial-temporal prediction plays a crucial role in various domains, including intelligent transportation and environmental monitoring. Although large language model has shown advantages in long-range dependency modeling and excellent generalization ability for forecasting, it has limited understanding of spatial-temporal features. Especially for spatial features, most existing methods still simplify the spatial-temporal prediction task into multiple independent temporal prediction tasks, failing to effectively encode the dynamic evolution of spatial relations. To address these problems, we propose ST-VLM (Spatial-Temporal Forecasting with Vision-Language Model), a novel framework that leverages visual representations to encode the dynamic spatial dependencies within spatial-temporal data and integrates multi-modal information to enhance prediction. This framework transforms spatial-temporal features into three modalities: vision, text, and time series, enhances cross-modal fusion through an attention-aware fusion mechanism in the first-layer of Vision-Language Model (VLM), optimizes multi-modal feature interaction via adaptive fine-tuning strategies. After fusion, the multi-modal embeddings are subsequently used for the final spatial-temporal prediction task. Extensive experiments demonstrate that ST-VLM achieves state-of-the-art performance across various datasets. In particular, the framework exhibits promising results in few-shot scenarios, verifying its strong generalization ability.
CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling
Authors: Yiming Zhao; Jiwei Tang; Shimin Di; Libin Zheng; Jianxing Yu; Jian Yin
Venue: AAAI 2026 (main)

Abstract: Recommending event schedules is a key issue in Event-based Social Networks (EBSNs) in order to maintain user activity. An effective recommendation is required to maximize the user's preference, subjecting to  both time and geographical constraints. Existing methods face an inherent trade-off among efficiency, effectiveness, and generalization, due to the NP-hard nature of the problem. This paper proposes the Chain-of-Scheduling (CoS) framework, which activates the event scheduling capability of Large Language Models (LLMs) through a guided, efficient scheduling process. CoS enhances LLM by formulating the schedule task into three atomic stages, i.e., exploration, verification and integration. Then we enable the LLMs to generate CoS autonomously via Knowledge Distillation (KD). Experimental results show that CoS achieves near-theoretical optimal effectiveness with high efficiency on three real-world datasets in a interpretable manner. Moreover, it demonstrates strong zero-shot learning ability on out-of-domain data.
MusicRec: Multi-modal Semantic-Enhanced Identifier with Collaborative Signals for Generative Recommendation
Authors: Yuqiu Zhao; Lei Shi; Yan Zhong; Feifei Kou; Pengfei Zhang; Jiwei Zhang; Mingying Xu; Yanchao Liu
Venue: AAAI 2026 (main)

Abstract: Generative recommendation as a new paradigm is influencing the current development of recommender systems. It aims to assign identifiers that capture richer semantic and collaborative information to items, and subsequently predict item identifiers via autoregressive generation using Large Language Models (LLMs). Existing approaches primarily tokenize item text into codebooks with preserved semantic IDs through RQ-VAE, or separately tokenize different modality features of items. However, existing tokenization methods face two major challenges: (1) Learning decoupled multi-modal features limits the quality of the semantic representation. (2) Ignoring collaborative signals from interaction history limits the comprehensiveness of identifiers. To address these limitations, we propose a multi-modal semantic-enhanced identifier with collaborative signals for generative recommendation, named MusicRec. In MusicRec, we propose a tokenization approach based on shared-specific modal fusion, enabling the generated identifiers to preserve semantic information more comprehensively from all modalities. In addition, we incorporate collaborative signals from user interactions to guide identifier generation, preserving collaborative patterns in the semantic representation space. Extensive experiments on three public datasets demonstrate that MusicRec achieves state-of-the-art performance compared to existing baseline methods.
Uplift Modeling with Delayed Feedback: Identifiability and Algorithms
Authors: Chunyuan Zheng; Anpeng Wu; Chuan Zhou; Taojun Hu; Qingying Chen; Hongyi Liu; Chenxi Li; Huiyou Jiang; Haoxuan Li; Zhouchen Lin
Venue: AAAI 2026 (main)

Abstract: Uplift modeling has obtained significant attention, with broad applications in medicine, economics, and marketing. For example, in a push notification scenario, accurately estimating the uplift of different push frequencies on user activation and notification switch close rate is critical for balancing user experience and business goals. Existing methods only use binary labels, i.e., convert or not within the observational window. However, they ignore time information (e.g., users who convert on day 1 vs. day 14 reflect different sensitivities) and fail to model potential closures outside the window, i.e., due to treatments always taking time to manifest causal impacts on outcomes, the potential outcomes of interest cannot be observed promptly and accurately. Failing to account for these issues can result in skewed uplift modeling. To address this gap, this work examines how observation timing influences the assessment of uplift by explicitly modeling the potential response time. Theoretical analysis establishes the conditions for identifiability under delayed feedback scenarios. We introduce CFR-DF (Counterfactual Regression with Delayed Feedback), a systematic framework that jointly learns both the latent response times and the underlying potential outcomes. Empirical evaluations on synthetic and real-world datasets, including an A/B test with over 1 billion users for 14 days, validate the approach, demonstrating its ability to handle temporal delays and improve estimation accuracy compared to previous uplift modeling methods.
Unified Minimax Optimization Framework for Propensity Score Estimation in Debiased Recommendation
Authors: Chunyuan Zheng; Haocheng Yang; Jinkun Chen; Shufeng Zhang; Tianyu Xia
Venue: AAAI 2026 (main)

Abstract: Recommendation systems commonly face selection bias from missing-not-at-random (MNAR) collected data. To address this bias, propensity-based methods such as inverse propensity scoring (IPS) and doubly robust (DR) estimators are widely used. In addition, many methods extend the vanilla IPS and DR to further control the bias, variance, propensity mis-calibration, and imbalance, but they only optimize some of the above metrics, limiting the debiasing performance. In this paper, we first empirically find that controlling one metric cannot guarantee the control of other important metrics, then we reveal a fundamental structural commonality among the above four important metrics, and propose a Unified Propensity Optimization (UPO) framework that optimizes all metrics simultaneously by a minimax optimization algorithm. Theoretically, we demonstrate that minimizing the UPO loss effectively controls all metrics, ensuring their simultaneous improvements without incurring additional bias, and achieving reduced variance compared to naively adding up multiple control losses in penalty terms. Empirically, experiments on a semi-synthetic dataset and three real-world datasets validate UPO’s effectiveness, demonstrating superior performance compared to state-of-the-art methods with minor computational overhead. We fully open-source our code.
From Semantics to Spectrum: A New Lens on Graph Augmentation Strategy
Authors: Xiangping Zheng; Xiuxin Hao; Bo Wu; Wei Li; Bin Ren; Bin Tang; Yuhui Guo; Xun Liang; Zhiwen Yu
Venue: AAAI 2026 (main)

Abstract: Graph augmentation is a cornerstone of effective graph contrastive learning, yet existing methods often rely on random designed perturbations, which may distort latent semantics and impair representation quality. In this work, we argue that semantic consistency can be effectively approximated by low-frequency components in the spectral domain, offering a principled proxy for guiding augmentation. Based on this insight, we propose Frequency-Aware Graph Contrastive Learning (FA-GCL), a novel framework that explicitly preserves low-frequency signals while selectively perturbing high-frequency components. By aligning augmentation with frequency-aware decomposition, FA-GCL generates diverse yet semantically coherent views, mitigating semantic drift and enhancing representational discrimination. 
Extensive experiments across multiple benchmarks demonstrate that FA-GCL consistently outperforms state-of-the-art baselines with statistically significant gains, validating its exclusive merits.
HaNa: Hardness and Noise-Aware Robust Cross-modal Retrieval
Authors: Fangming Zhong; Haiquan Yu; Cun Zhu; Suhua Zhang
Venue: AAAI 2026 (main)

Abstract: Noisy correspondence in cross-modal retrieval introduces significant challenges due to its inherent difficulty in identification and correction. Although existing methods attempt to minimize the influence of noisy samples by the weighting mechanism, these methods still struggle with performance degradation under increasing noise levels. Specifically, the clean samples are assigned the same weight of 1, which ignores the sample hardness. In addition, the weights for noisy samples are approaching 0, leading to the overlook of sample diversity. To address these issues, we propose a Hardness and Noise-aware (HaNa) robust cross-modal retrieval method. HaNa introduces a momentum-based reweighting mechanism to adaptively balance learning difficulty across clean samples, avoiding overfitting risk and accumulative partitioning bias. Moreover, HaNa addresses the limitation that weights for noisy data are approaching 0 from a new perspective to fully employ the diversity of samples to further improve its generalization. It employs an Asymmetric Noise-aware Regularization Loss (ANRL) to treat identified noisy data as negative samples for optimization. Extensive experiments demonstrate that HaNa achieves superior matching accuracy and stability, especially in high-noise scenarios, outperforming state-of-the-art methods.
IdeFN: Identifying Unclicked Space False Negatives via Relaxed Partial Optimal Transport for Conversion Rate Prediction
Authors: Weiyi Zhong; Weiming Liu; Lianyong Qi; Xiaoran Zhao; Xiaolong Xu; Haolong Xiang; Yang Cao; Shichao Pei; Qiang Ni
Venue: AAAI 2026 (main)

Abstract: Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-task learning frameworks to alleviate these issues. However, existing methods implicitly mislabel some unclicked samples with genuine conversion potential as negatives, thereby exacerbating the false negative sample (FNS) problem. To address this, we propose IdeFN, a multi‑task CVR framework that identifies false negatives in the unclicked space to enable CVR prediction across the entire exposure space and leverages CTR as an auxiliary task for shared‑parameter learning. Specifically, IdeFN consists of two main components, i.e., relaxed partial optimal transport (RPOT) module and sample relabeling mechanism (SRM). The former estimates the soft matching strengths between unclicked samples and positive samples under a relaxed partial optimal transport formulation, establishing corresponding relationships between these samples. The latter adaptively re-labels the unclicked samples according to the derived matching strengths, without relying on static or heuristic thresholds, thus enhancing the reliability of the generated pseudo-labels. Experimental results demonstrate that IdeFN effectively mitigates the FNS problem, achieving substantial improvements in CVR prediction accuracy.
Exploiting Pre-trained Language Model for Cross-city Urban Flow Prediction Guided by Information-theoretic Analysis
Authors: Qiang Zhou; Xudong Tong; Yuting Liu; ChuanXing Liu; Jingjing Gu
Venue: AAAI 2026 (main)

Abstract: Cross-city urban flow prediction is critical for democratizing smart application benefits in data-scarce developing cities. However, existing methods face an inherent performance ceiling, constrained by both the inevitably finite samples from the source city and the distributional gap between cities. In this paper, we present PLM-CUP, the first theoretically-grounded framework that breaks this bottleneck by leveraging a pre-trained language model (PLM) as an additional source domain. Through an information-theoretic analysis of the generalization error bound, we reveal that the key challenge lies in constructing a semantic bridge encoder and a task-specific adapter to enable cross-domain alignment when incorporating a PLM. Accordingly, PLM-CUP adopts a three-stage architecture, including a semantic bridge encoder that transforms spatiotemporal flow patterns into languagealigned representations via trend-periodicity decomposition, a PLM fine-tuned for knowledge transfer, and a task adapter with spatiotemporal self-attention to conduct multi-step prediction. We further introduce GDAConv, a graph convolution module with dual activation functions that enhances spatial modeling throughout the framework. Experiments on real-world datasets demonstrate that PLM-CUP significantly outperforms state-of-the-art baselines, validating the effectiveness of the proposed PLM enhanced cross-city transfer paradigm for urban flow prediction.
Inference Scaling Law for Retrieval Augmented Generation
Authors: Shu Zhou; Yuxuan Ao; Yunyang Xuan; Xin Wang; Tao Fan; Hao Wang
Venue: AAAI 2026 (main)

Abstract: Retrieval-augmented generation (RAG) has recently emerged as a powerful framework for knowledge-intensive natural language processing tasks, which leverages the strengths of both pre-trained language models and external knowledge. While significant progress has been made, the scaling behavior of these approaches during inference remains poorly understood. Towards this end, this paper presents a comprehensive study of inference scaling law for RAG models, which investigates how inference performance scales with respect to key factors including retriever model scale, generator model scale, number of retrieved documents, and context window size. Through extensive experiments on benchmark datasets, we establish empirical scaling laws that reveal power-law  and sigmoid-type relationships between these factors and performance. We further build a joint inference scaling law with theoretical justification. With the proposed scaling laws, we can understand the performance tendency of RAG models under different computational resources. We believe our insights can pave the way for efficient and effective deployment of RAG models in more applications.
EdgeMTSC: A Lightweight Large-Kernel ConvNet for Multivariate Time Series Classification
Authors: Xueyi Zhou; Zhenyu Li; Dong-Kyu Chae
Venue: AAAI 2026 (main)

Abstract: In large-scale sensor networks, Multivariate Time Series Classification (MTSC) is a pivotal task for identifying events dependent on longitudinal data at the edge. However, existing methods focus on neither the inherent ability of convolutional networks to perceive subsequence features, nor the prolonged processing pipeline and the model deployment overhead brought by the highly parameterized models. To resolve these difficulties, we present EdgeMTSC, a lightweight large-kernel ConvNet for MTSC, which naturally extracts and learns features of diverse subsequences. Specifically, a novel module named Inter-channel Message Passing-driven Kernel Block (IMP-KB) is proposed, which maintains a learnable correlation matrix to propagate and merge inter-channel messages, and fuses miscellaneous patterns learned by parallel conv kernels of different sizes. EdgeMTSC sequences two modules of different receptive fields to aggregate local features using small kernels and study long-term representation provided by large kernels, respectively. For inference parameter reduction and accelerating inference without performance loss, the conv blocks in IMP-KBs are structurally reparameterizable. The performance of our model (76.2%) is benchmarked on 26 UEA MTSC datasets and is superior to the SOTA model (MPTSNet, 75%). At the same time, EdgeMTSC uses the fewest parameters and achieves the minimum inference time, applicable on any machine (8 devices ranging from large-scale distributed AI computing servers to resource-constrained edge devices) and in any application scenario.
DFRec: Dual Fluctuation Modeling of Multi-level Intent Evolution for Next-Item Recommendation
Authors: Nengjun Zhu; Lingdan Sun; Qi Zhang; Jian Cao; Hang Yu
Venue: AAAI 2026 (main)

Abstract: User sequential behaviors are driven by a variety of complex and evolving intents. Capturing the dynamic change of user intents has become critical yet challenging in the next-item recommendation. Existing studies usually model the transition relationships among multiple intents within a session or integrate temporal information to capture the dynamic evolution of user intents. However, they struggle to identify the precise timing and magnitudes of intent changes, leading to ambiguity in providing consistent or violated recommendations and ultimately yielding subpar performance. To this end, we propose a novel framework called Dual Fluctuation Modeling of Multi-level Intent Evolution for Next-Item Recommendation (DFRec) in this paper. DFRec explicitly identifies the user intent changes and further quantifies the magnitude of the changes. Specifically, we assume that a user's intent fluctuates around an inherent intent, with the magnitude of fluctuations indicating the extent of changes in user intents. Thus, we design an Emerging Intent Generation Module that employs a normal distribution with dynamic variance to capture intent fluctuations at each time step. Furthermore, we introduce a dual-layer dynamic variance update mechanism to capture fluctuation characteristics at different temporal levels, enhancing the representation of possible emergent intents. Extensive experiments on three real-world datasets verify DFRec's superiority over state-of-the-art baselines.
Stage-Aware Graph Contrastive Learning with Node-oriented Mixture of Experts
Authors: Xiangkai Zhu; Yeyu Yan; Saiqin Long; Chao Li; Guanwen Chen; Longsheng Su
Venue: AAAI 2026 (main)

Abstract: Text-attributed graphs (TAGs), which associate rich textual descriptions with each node, are widely employed to represent complex relationships among real-world textual entities.
Currently, representation learning for TAGs leverages large language models (LLMs) to transform node-matched textual descriptions into node features or labels, followed by the message passing in graph neural networks (GNNs) that further improves the expressiveness of graph representation learning. Nevertheless, a simple experiment we conducted demonstrates that not all LLMs are readily compatible with GNNs.
A salient finding indicates that  architectural heterogeneity among LLMs manifests as substantial performance gap across diverse TAGs representation learning.
Moreover, the node semantics encoded by LLMs are often misaligned with the message passing in GNNs, causing performance collapse. 
Motivated by this observation, we propose a novel  self-supervised graph learning framework called Stage-Aware Graph Contrastive Learning (SAGCL).
In particular, we propose the node-oriented mixture of experts (NodeMoE) to assign suitable candidate experts for each node. 
It flexibly balances the strengths of different language experts by low-rank decomposition and reparameterization strategies. 
Subsequently, to align the inductive biases of graph structures with the semantic perception capabilities of LLMs, the message passing in GNNs is decoupled into the feature transformation  stage and the feature propagation  stage.  Given the two stage views, stage-aware graph contrastive learning is proposed to match the node semantics encoded by the LLM with the locally aware topological patterns within the GNN via self-supervised contrastive learning. Experiments on eight datasets and three downstream tasks demonstrate the effectiveness of SAGCL.