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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

IJCAI 2026

scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu, Ziyue Qiao, Nan Yin, Jiancheng Lv, Wei Ju

Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.

IJCAI 2026

LEMD: Latent Environment Extrapolation and Message Disentanglement for Dynamic Graph Under Distribution Shift

Xiaoran Wei, Chen Zhao, Minglai Shao, Xintao Wu, Zhong Chen, Wenjun Wang, Qin Tian, Chang Liu

Dynamic graph neural networks (DyGNNs) are widely used to model evolving interactions, but may fail under data distribution shift. Due to limited and unreliable interventions and insufficient disentanglement, the existing dynamic graph domain generalization approaches lead to suboptimal results. We formalize a message sufficiency causal view: a node representation is fully mediated by its received message multiset. Building on this perspective, we propose Latent environment Extrapolation and Message Disentanglement (LEMD), a novel robust representation learning framework for dynamic graph domain generalization. A message extrapolation mechanism under soft uncertainty constraints is proposed to obtain the diverse counterfactual message distributions. Causal information is disentangled fully from the messages to suppress shortcuts via a recoverable evolving disentanglement module. We further provide rigorous theoretical analysis and proofs to ensure the effectiveness of LEMD. Across all six datasets and two tasks, LEMD consistently improves over state-of-the-art dynamic graph generalization baselines under distribution shift, and achieves the best performance increase of 7.7% relative compared to the suboptimal baseline. The code of LEMD for reviewer is available at https://github.com/W-WuJi/LEMD.

IJCAI 2026

DNFormer: Differential Attention for Graph Transformers

Zizhen Wang, Dongxiao He, Zhizhi Yu, Kuntharrgyal Khysru, Weixiong Zhang

Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning, leveraging attention mechanisms to enable flexible information exchange. Recent GTs often adopt local attention to restrict computation to neighborhoods, thereby reducing costs and enhancing scalability. However, local attention computation poses a serious issue that significantly reduces the efficacy of the overall approaches – because computation is restricted to local neighborhoods, node pairs that share topological patterns receive similar attention scores, thereby reducing attention’s discriminative power. Consequently, GT-based models typically overemphasize common structural motifs but fail to capture node-specific properties. To address this problem, we propose DNFormer, a novel GT grounded in differential modeling. We introduce a differential local attention mechanism in DNFormer that computes attention scores as differences across multiple topology-aware similarity measures, rather than relying on a single score. This mechanism suppresses shared topological patterns among neighbors and emphasizes relative node-to-node distinctions. As a result, DNFormer mitigates structural dominance in attention aggregation, better capturing node-specific relational patterns while preserving local structural properties. Extensive experiments confirm DNFormer's superior performance over representative GT baselines.

IJCAI 2026

When Evidence Falls Short: Router-Guided Fake News Detection with Pattern Augmentation

Yujing Wang, Xiaobao Wang, Yiqi Dong, Yueheng Sun, Di Jin, Dongxiao He

With the growing complexity of online information, trustworthy fake news detection has become increasingly critical. Although Large Language Models (LLMs) exhibit a strong ability to leverage factual evidence for verification, they remain highly vulnerable to unreliable, noisy, or scarce evidence, undermining robustness in real-world scenarios. Given the generalizability of deceptive patterns in fake news, we consider pattern as a complementary signal under insufficient evidence during factual verification. However, due to LLMs' lack of expertise in deception-specific patterns, realizing such effective collaboration remains challenging. To address these issues, we propose a Router-Guided Fake News Detection Framework with Pattern Augmentation (RGPA). Specifically, we introduce a hierarchical routing mechanism including a case router and an external evidence router. It guides news to appropriate reasoning paths adaptively based on a multi-dimensional quality assessment, prioritizing high-quality evidence while mitigating noise. Furthermore, we design an expert model to capture deceptive features and integrate them into LLMs' reasoning, enabling a synergy of factual verification and pattern awareness under evidence-scarce scenarios. Extensive experiments on two real-world datasets demonstrate that RGPA significantly outperforms existing approaches.

IJCAI 2026

Geometry-Aware Riemannian Residual Displacement for Cross-Broad-Domain Graph Anomaly Detection

Senyong Wang, Chen Zhu, Zihao Yao, Yaying Zhang

Cross-Domain Graph Anomaly Detection supports the transfer of knowledge to unknown targets. Nevertheless, current approaches frequently struggle in the Cross-Broad-Domain paradigm which is characterized by two significant discrepancies: feature heterogeneity and structural disparity. Such diverse geometric topologies result in substantial manifold mismatch, as the traditional dependence on linear residuals in flat Euclidean space induces considerable geometric distortion when accommodating various topological patterns. We propose a novel framework termed Geometry-aware Riemannian Anomaly DEtector (GRADE) to tackle these difficulties. To address the semantic gap, we develop a domain-based contrastive learning technique combined with target-adaptive prompt tuning, which dynamically adjusts source representations to align with the target distribution while maintaining local contexts. To tackle the manifold mismatch, we present the Riemannian Residual Displacement (RRD), a curvature-adaptive metric that maps node representations onto multi-curvature Riemannian manifolds. By quantifying geodesic deviations in curved spaces, RRD corrects geometric distortions and identifies anomaly patterns that remain invariant to topological alterations. Additionally, a geometry-aware prototype alignment approach secures these residuals to data-independent priors for resilient inference. Comprehensive experiments on 11 benchmark datasets across 4 distinct domains demonstrate that GRADE significantly outperforms state-of-the-art baselines in both zero-shot and few-shot settings.

IJCAI 2026

From Neural Collapse to Label-Limited Evolving Streams: Geometry-Constrained Learning Under Dynamic Class Imbalance

Hongliang Wang, Hongyuan Liu, Qirui Hao, Yun Sing Koh, Qinli Yang, Junming Shao

Learning from label-limited streams presents significant challenges, particularly when coupled with concept drift and dynamic class imbalance. Existing works often struggle to maintain a discriminative feature space under these constraints, biasing decision boundaries toward majority classes or outdated concepts. To address this, we propose a novel framework named Neural collapse Inspired Label-limited Evolving stream learning (NILE). Instead of utilizing learnable classifiers, NILE exploits Neural Collapse (NC) geometry to explicitly construct a Simplex Equiangular Tight Frame (ETF) as a fixed classifier, ensuring maximal inter-class separability to guide feature discriminability. To maintain this discrimination with limited labels, NILE employs hybrid active learning to prioritize uncertain and minority samples, and an NC-adapted semi-supervised mechanism to enhance representation learning. NILE further updates the classifier by dynamically adjusting the ETF structure, enabling continual adaptation to drifting concepts. Extensive experiments show that NILE can effectively guide features toward the NC state, significantly outperforming state-of-the-art baselines in nonstationary environments.

IJCAI 2026

StreamMTS: Towards Streaming Multivariate Time Series Forecasting

Binwu Wang, Jiaming Ma, Yudong Zhang, Pengkun Wang, Zhengyang Zhou, Xu Wang, Yang Wang

Current mainstream research in multivariate time series (MTS) forecasting often assumes that all data are static. However, real-world MTS data typically arrives continuously in a streaming manner, which we refer to as streaming MTS. The statistical characteristics and spatiotemporal graph topology of these data evolve over time, presenting two key challenges: the model's ability to adapt to new data distributions and the enhancement of cross-domain generalization capabilities. In this paper, we propose a streaming MTS prediction framework. We begin by designing a lightweight spatiotemporal causal learning model that captures generalizable causal spatiotemporal features from a decoupling perspective. Next, we introduce a framework to enhance the model's streaming learning capability, leveraging the adaptability of continual learning while strengthening cross-domain representation abilities. Specifically, we reformulate continual learning as a multi-task learning problem and present a multi-task optimization algorithm that identifies a set of Pareto-optimal solutions to address the inherent stability-plasticity dilemma in continual learning. Finally, we propose a topology-aware feature propagation strategy that disseminates well-trained node embedding features to unseen graph structures, thereby improving the model's cross-domain generalization. Results on real-world datasets demonstrate that our model achieves superior forecasting performance while substantially improving computational efficiency and memory usage.

IJCAI 2026

Joint Multi-Modal Multi-Interest Profiling and Preference-Grounded Reasoning for Explainable Recommendation

Anqi Wang, Jianye Xie, Weiming Liu, Rong Jiang, Lianyong Qi, Haolong Xiang, Xiaolong Xu, Wenmin Lin, Yang Zhang, Xiaokang Zhou

Explainable Recommendation (ER) aims to enhance recommendation transparency and prediction accuracy by providing faithful and persuasive explanations. However, Multi-Modal Multi-Interest Explainable Recommendation (MMER) is particularly challenging in two aspects: effectively utilizing diverse multi-modal information to construct reliable semantic evidence and precisely identifying the most relevant user interest to generate faithful explanations. Most previous methods fail to effectively exploit visual information to provide reliable evidence and primarily rely on a single unified representation of user preferences, making it difficult to distinguish diverse user interests and generate faithful explanations. To fill this gap, we propose Joint Multi-Modal Multi-Interest Profiling and Preference-Grounded Reasoning (PRIME) for solving the MMER problem. Specifically, PRIME first organizes items into interest-aware clusters to facilitate interest disentanglement. Based on these clusters, it constructs explicit multi-modal multi-interest user profiles and a comprehensive global item profile to capture fine-grained preferences and distinguish diverse user interests. Furthermore, PRIME performs preference-grounded reasoning by retrieving the most relevant user interest for each item, enabling faithful explanations and accurate predictions. Our experiments on three real-world datasets demonstrate that PRIME outperforms the state-of-the-art methods.

IJCAI 2026

From Values to Tokens: An LLM-Driven Framework for Context-Aware Time Series Forecasting via Symbolic Discretization

Xiaoyu Tao, Shilong Zhang, Mingyue Cheng, Daoyu Wang, Tingyue Pan, Bokai Pan, Changqing Zhang, Shijin Wang

Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary for context-aware time series forecasting. Specifically, TokenCast employs a discrete tokenizer to transform continuous numerical sequences into temporal tokens, enabling structural alignment with language-based inputs. To effectively bridge the semantic gap between modalities, both temporal and contextual tokens are embedded into a shared representation space via a pre-trained LLM, further optimized with generative objectives. Building upon this unified semantic space, the aligned LLM is subsequently fine-tuned in a supervised manner to predict future temporal tokens, which are then decoded back into the original numerical space. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework and highlight its potential as a generative framework for context-aware time series forecasting. The code is available at https://github.com/Xiaoyu-Tao/TokenCast.

IJCAI 2026

ADC-GNN: Adaptive Dual-level Collaborative Graph Neural Networks for Graph Classification

Wan Tang, Lu Bai, Lixin Cui, Ming Li, Hangyuan Du, Jing Li

Most existing Graph Neural Networks (GNNs) rely on the node-level message passing or attention mechanisms to propagate and extract useful information. Although recent advances attempt to move beyond purely the node-level propagation by constructing high-level representations, these approaches are often constrained by pre-computed substructures or unidirectional bottom-up aggregations. Consequently, high-level structural semantics cannot effectively feed back to guide node representation learning, limiting the collaborative optimization between fine-grained features and macroscopic structural semantics. To address these limitations, we propose a novel Adaptive Dual-level Collaborative GNN (ADC-GNN) associated with an adaptive dual-level collaborative mechanism. We commence by introducing a set of global, learnable latent prototypes as high-level semantic references, and then employ a relaxed Sinkhorn algorithm to establish differentiable, non-collapsing assignments between nodes and prototypes. Based on these assignments, the ADC-GNN constructs high-level representations and enables interactions among them. We show that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process. Experiments demonstrate the superior performance of the proposed ADC-GNN on graph classification.

IJCAI 2026

STCBN-EC: A Spatio-Temporal Constrained Bayesian Causal Network for Multimodal Brain Effective Connectivity Learning

Zhihao Su, Junzhong Ji, Minqi Yu, Jinduo Liu

Brain effective connectivity (EC) characterizes directional causal interactions among brain regions. However, learning stable and directionally explicit EC networks from multimodal data remains challenging. In practice, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) differ substantially in spatio-temporal resolution and noise characteristics. Existing methods often rely on manual spatio-temporal alignment and tend to recover only partial causal structures under high noise and Bayesian equivalence classes. To address these challenges, we propose a spatio-temporal constrained Bayesian causal network for multimodal brain effective connectivity learning (STCBN-EC). First, STCBN-EC constructs an anatomically guided EEG–fMRI spatial mapping and derives a unified spatio-temporal representation through slice-level alignment and adaptive modality fusion. Then, a Bayesian causal network is employed to model nonlinear inter-regional dependencies, where uncertainty-driven surrogate scoring is used to evaluate candidate structures. Finally, multimodal representation learning and EC structure estimation are jointly optimized via a gradient-free global optimization strategy. Experiments on simulated and real EEG–fMRI datasets demonstrate that STCBN-EC outperforms state-of-the-art methods and effectively captures state-dependent directional interactions among brain regions.

IJCAI 2026

Mitigating Dynamic Graph Distribution Shifts via Spectral Augmentation

Qianyu Song, Chao Li, Zhongying Zhao, Hua Duan, Qingtian Zeng

Dynamic Graph Neural Networks (DyGNNs) are facing challenges with distribution shifts between training and test data that are similar but not identical. Existing DyGNNs for out-of-distribution scenarios primarily focus on discovering invariant patterns in the spatial domain, overlooking its impact on the structural properties in the spectral domain. In this paper, we propose a spectral-based graph augmentation framework designed to investigate and improve generalization behavior in dynamic graphs under distribution shifts. Specifically, we augment the input graph spectra into a mixture of shift components by maximizing the variance of spectral distance and propose an efficient approximation to reduce the computational cost brought by eigen-decomposition. Building on this, we develop a multi-encoder architecture in which each encoder targets a specific spectral shift component to generate referential representations. Our method adopts a new learning objective to encourage the model to rely on robust spectral properties for better generalization under distribution shifts. Extensive experiments on both real-world and synthetic datasets demonstrate that our proposed method significantly outperforms state-of-the-art approaches in node classification and link prediction tasks. Source codes are available at https://github.com/SSQiana/DSPA.

IJCAI 2026

McGcn: Learning Continuous Graph Dynamics for Multi-Channel Fusion

Na Song, Zihan Fang, Weidong Zhang, Zehua Jia, Shiping Wang

Multi-channel data fusion is essential for capturing comprehensive representations in complex systems. While graph convolutional networks have demonstrated remarkable efficacy, existing fusion paradigms primarily rely on discrete architectures governed by first-order propagation. These models are typically confined to discrete message-passing mechanisms, which makes it difficult to characterize the continuous evolution of underlying system dynamics. To address these limitations, we propose a multi-channel Graph Continuous Network (mcGCN), a novel framework for multi-channel data fusion. By formulating the information propagation as a second-order partial differential equation on graphs, mcGCN transitions from discrete layer-wise updates to a continuous dynamical system. Specifically, mcGCN integrates a multi-channel encoding module with continuous feature dynamics to initialize and evolve the latent node representations over static graph topologies. This physical analogy enables more robust information flow and effectively mitigates the performance degradation typically associated with deep graph architectures. Extensive experiments on diverse benchmark datasets demonstrate that our method outperforms state-of-the-art baselines, validating its effectiveness and robustness.

IJCAI 2026

Relation-Aware Graph Learning with Mixture-of-Experts Prediction for Cognitive Diagnosis

Jingwei Qu, Mingze Zhang, Pingshun Zhang, Li Tao, Ying Wang, Zhaofang Yang, Haibin Ling

Cognitive diagnosis aims to infer students’ concept-level mastery from exercise response logs and exercise-concept associations. Fully leveraging heterogeneous relations and modeling large mastery-difficulty variations remain challenging, especially with a single predictor. To address these challenges, we propose RMCD, a unified cognitive diagnosis model that integrates relation-aware graph learning with Mixture-of-Experts (MoE) prediction. RMCD constructs a heterogeneous relational graph over students, exercises, and concepts with multiple relation types, and learns node and edge representations simultaneously. It derives relation-strength vectors from student-concept and exercise-concept edges to distinguish relation effects and refine node representations. RMCD further introduces an MoE-based prediction head that adaptively combines multiple expert predictors to capture diverse mastery-difficulty discrepancies. Experiments on benchmark datasets demonstrate that RMCD consistently outperforms state-of-the-art cognitive diagnosis methods. Our algorithm is available at https://github.com/swu-qjw-lab/code/tree/main/RMCD.

IJCAI 2026

Multi-View Ensemble for Time Series Anomaly Detection via Coupling Flows

Wanghui Qiu, Chenxi Liu, Shiyan Hu, Zhengyu Li, Chenjuan Guo, Bin Yang

Time series anomaly detection faces a critical challenge that different anomaly types require different detection mechanisms, yet single methods are inherently limited by their design biases. We propose FlowFuse, a multi-view ensemble framework with coupling flow-based score fusion for time series anomaly detection. FlowFuse combines four complementary detection modules spanning temporal and frequency domains, as well as clustering and reconstruction paradigms, to extract multi-perspective anomaly scores. Rather than simple averaging, an ensemble of coupling flows models the joint distribution of these multi-view scores, learning complex inter-view dependencies through invertible transformations with alternating updates between temporal and frequency scores. The ensemble naturally quantifies detection uncertainty through prediction disagreement, which can optionally guide selective supervision when labels are available. Extensive experiments across 18 diverse benchmarks show that FlowFuse achieves state-of-the-art performance, demonstrating effectiveness across multiple anomaly types.

IJCAI 2026

WBMCF: Robust Spatiotemporal Forecasting of Terrorism Fatalities with Multi-Scale Time–Frequency Fusion

Zhenkai Qin, Baozhong Wei, Huan Zeng, Caifeng Gao, Ziqian Lin

Reliable forecasting of cross-regional terrorism fatalities is essential for early warning and security planning. However, real-world terrorism data are characterized by extreme sparsity, abrupt volatility, and weak, rapidly shifting non-Euclidean spatial relationships, posing substantial challenges for existing spatiotemporal forecasting models in learning stable multi-scale dependencies. To address these challenges, we propose a robust spatiotemporal forecasting framework, termed WBMCF. In the temporal dimension, a single-level Discrete Wavelet Transform (DWT) is introduced to stabilize temporal structures, and a Bidirectional Gated Mamba module is employed to capture stage-dependent long-range temporal dependencies; additionally, in the spatial dimension, a topology-agnostic Convolutional Feed-Forwar+d Network (ConvFFN) is incorporated to model cross-regional dependencies via implicit feature coupling, thereby eliminating reliance on fixed spatial topologies. Extensive experiments on five regional subsets of the Global Terrorism Database (GTD) demonstrate that WBMCF achieves stable and competitive forecasting performance under sparse and noisy conditions. The source code is available at https://github.com/weibaozhong/WBMCF.

IJCAI 2026

A Unified Spectral-Spatial Framework for GNNs: Balancing Over-Smoothing and Over-Squashing

Xinya Qin, Lu Bai, Lixin Cui, Ming Li, Hangyuan Du, Jing Li

Over-smoothing (OSM) and over-squashing (OSQ) are two fundamental phenomena that limit the performance of Graph Neural Networks (GNNs), yet a unified spectral-spatial understanding of these phenomena remains underexplored. In this paper, we adopt polynomial spectral filters as an analytical tool to establish a unified spectral-spatial framework for graph convolution and systematically characterize the effect of the polynomial order k on information propagation in GNNs. Within this framework, we reveal an intrinsic trade-off induced by the polynomial order. Specifically, higher-order filters enhance spectral expressiveness and alleviate OSM caused by the dominant low-frequency components. However, they also expand the spatial receptive field, thereby intensifying information compression and increasing the risk of OSQ. Based on this analysis, we provide a principled guideline for selecting the polynomial order and propose a Quadratic Spectral Graph Convolution Network (QS-GCN) for graph classification. Experiments demonstrate the effectiveness and robustness of the proposed method.

IJCAI 2026

Inductive Knowledge Graph Wave Networks

Giuseppe Pirrò

Inductive knowledge graph reasoning (IKGR) requires generalization to entities and relation types not seen during training. We introduce Knowledge Graph Wave Networks (KGWN), an IKGR approach that learns adaptive per-relation wave operators to control relation-dependent propagation, along with velocity normalization ensuring stability at any propagation depth. Experiments demonstrate state-of-the-art results on inductive benchmarks and competitive transductive performance.

IJCAI 2026

CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection

Junjun Pan, Yixin Liu, Yu Zheng, Lianhua Chi, Alan Wee-Chung Liew, Shirui Pan

Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, fraudsters continuously evolve their camouflaging strategies by deliberately mimicking textual responses of benign users, thereby concealing their malicious purposes. This phenomenon, referred to as semantic camouflage, fundamentally undermines commonly relied assumptions on how structural and attribute cues can be exploited to identify fraudsters, and makes it difficult to spot fraudsters with unsupervised TAGFD. To bridge the gaps, we propose a Case-Adaptive Multi-cue Expert fRAmework (CAMERA) for unsupervised TAGFD. CAMERA employs an ego-decoupled mixture-of-experts architecture, where each expert specializes in modeling a distinct type of fraud-indicative cue. A context-informed gating model is introduced to jointly consider the ego node representation and its local neighborhood context for adaptive integration of cues learned by different experts. Furthermore, CAMERA leverages the inherent rarity of fraudsters to support unsupervised one-class learning with expert-level objectives that encourage modeling dominant benign patterns, thereby enabling reliable unsupervised detection of camouflaged fraudsters. Experiments on 4 challenging datasets show that CAMERA consistently outperforms competitors, showing its effectiveness against semantically camouflaged fraudsters. Code available at https://github.com/CampanulaBells/CAMERA

IJCAI 2026

StreamTimer: Efficient Inference for Long-Context Time Series Transformers

Xiyu Meng, Yuhan Wu, Canran Xiao, Yabo Dong, Duanqing Xu

Time series forecasting (TSF) plays a vital role across various domains such as finance, energy, healthcare, and meteorology. Currently, most deep learning based TSF methods typically operate with a fixed lookback window. This approach comes from the high compute and memory costs of long contexts, as well as the standard practice of using sliding windows. This creates a trade-off. Making the window larger reduces the number of training samples, which can harm stability and generalization. However, keeping the window small prevents the model from using long history during inference. We propose an inference-only streaming autoregressive framework that replaces repeated full-context recomputation with a one-time context warmup and incremental decoding, enabling efficient long-history forecasting without retraining. While straightforward caching attentions is brittle for time series due to distribution shifts and noisy or redundant histories, we address these issues with cache-consistent normalization and selective memory under a fixed cache budget. Across diverse benchmarks, our approach substantially reduces inference latency with no or marginal accuracy loss, and often improves performance when longer lookbacks are beneficial.

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