{"total":40,"rows":[{"id":"2201.12740","title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","created":"2022-01-30","cited_by_count":1210,"area":null},{"id":"2203.08913","title":"Memorizing Transformers","created":"2022-03-16","cited_by_count":226,"area":null},{"id":"2205.08897","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","created":"2022-05-18","cited_by_count":147,"area":null},{"id":"2209.12951","title":"Liquid Structural State-Space Models","created":"2022-09-26","cited_by_count":136,"area":null},{"id":"2203.07852","title":"Block-Recurrent Transformers","created":"2022-03-11","cited_by_count":110,"area":null},{"id":"2311.12351","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey","created":"2023-11-21","cited_by_count":72,"area":null},{"id":"2305.15805","title":"Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers","created":"2023-05-25","cited_by_count":67,"area":null},{"id":"2506.04761","title":"Log-Linear Attention","created":"2025-06-05","cited_by_count":40,"area":null},{"id":"2309.16354","title":"Transformer-VQ: Linear-Time Transformers via Vector Quantization","created":"2023-09-28","cited_by_count":26,"area":null},{"id":"2302.04542","title":"Efficient Attention via Control Variates","created":"2023-02-09","cited_by_count":23,"area":null},{"id":"2502.17019","title":"Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems","created":"2025-02-24","cited_by_count":22,"area":null},{"id":"2112.13776","title":"Transformer Uncertainty Estimation with Hierarchical Stochastic Attention","created":"2021-12-27","cited_by_count":21,"area":null},{"id":"2305.01638","title":"Sequence Modeling with Multiresolution Convolutional Memory","created":"2023-05-02","cited_by_count":19,"area":null},{"id":"2401.00955","title":"Learning Long Sequences in Spiking Neural Networks","created":"2023-12-14","cited_by_count":19,"area":null},{"id":"2305.19534","title":"Recasting Self-Attention with Holographic Reduced Representations","created":"2023-05-31","cited_by_count":14,"area":null},{"id":"2207.10284","title":"Multi Resolution Analysis (MRA) for Approximate Self-Attention","created":"2022-07-21","cited_by_count":11,"area":null},{"id":"2405.18616","title":"Wavelet-Based Image Tokenizer for Vision Transformers","created":"2024-05-28","cited_by_count":10,"area":null},{"id":"2501.14577","title":"ZETA: Leveraging Z-order Curves for Efficient Top-k Attention","created":"2025-01-24","cited_by_count":9,"area":null},{"id":"2405.02842","title":"IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs","created":"2024-05-05","cited_by_count":8,"area":null},{"id":"2403.17978","title":"Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection","created":"2024-03-23","cited_by_count":6,"area":null},{"id":"2110.02442","title":"PoNet: Pooling Network for Efficient Token Mixing in Long Sequences","created":"2021-10-06","cited_by_count":5,"area":null},{"id":"2311.04449","title":"Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability","created":"2023-11-08","cited_by_count":5,"area":null},{"id":"2507.02748","title":"Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics","created":"2025-07-03","cited_by_count":5,"area":null},{"id":"2404.11163","title":"LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory","created":"2024-04-17","cited_by_count":4,"area":null},{"id":"2502.16249","title":"Linear Attention for Efficient Bidirectional Sequence Modeling","created":"2025-02-22","cited_by_count":4,"area":null},{"id":"2205.14014","title":"What Dense Graph Do You Need for Self-Attention?","created":"2022-05-27","cited_by_count":3,"area":null},{"id":"2310.11960","title":"Fast Multipole Attention: A Scalable Multilevel Attention Mechanism for Text and Images","created":"2023-10-18","cited_by_count":3,"area":null},{"id":"2408.08567","title":"S$^3$Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching","created":"2024-08-16","cited_by_count":2,"area":null},{"id":"2512.23824","title":"MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling","created":"2025-12-29","cited_by_count":2,"area":null},{"id":"2509.10406","title":"Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2509.15448","title":"Hierarchical Self-Attention: Generalizing Neural Attention Mechanics to Multi-Scale Problems","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2208.03791","title":"Global Hierarchical Attention for 3D Point Cloud Analysis","created":"2022-08-07","cited_by_count":0,"area":null},{"id":"2306.01768","title":"A Quantitative Review on Language Model Efficiency Research","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2508.10758","title":"Natively Trainable Sparse Attention for Hierarchical Point Cloud Datasets","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2509.07963","title":"Customizing the Inductive Biases of Softmax Attention using Structured Matrices","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2601.18401","title":"Superlinear Multi-Step Attention","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2603.09685","title":"Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2608.20427","title":"BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.05738","title":"RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.07237","title":"CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention","created":"2026-09-07","cited_by_count":0,"area":null}],"next_cursor":null}