{"total":60,"rows":[{"id":"2412.06464","title":"Gated Delta Networks: Improving Mamba2 with Delta Rule","created":"2024-12-09","cited_by_count":532,"area":null},{"id":"2406.06484","title":"Parallelizing Linear Transformers with the Delta Rule over Sequence Length","created":"2024-06-10","cited_by_count":396,"area":null},{"id":"2407.02490","title":"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention","created":"2024-07-02","cited_by_count":383,"area":null},{"id":"2410.02660","title":"How to Train Long-Context Language Models (Effectively)","created":"2024-10-03","cited_by_count":146,"area":null},{"id":"2507.10524","title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","created":"2025-07-14","cited_by_count":133,"area":null},{"id":"2410.20672","title":"Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA","created":"2024-10-28","cited_by_count":78,"area":null},{"id":"2409.07146","title":"Gated Slot Attention for Efficient Linear-Time Sequence Modeling","created":"2024-09-11","cited_by_count":76,"area":null},{"id":"2501.18795","title":"Rope to Nope and Back Again: A New Hybrid Attention Strategy","created":"2025-01-30","cited_by_count":47,"area":null},{"id":"2508.15884","title":"Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search","created":"2025-08-21","cited_by_count":41,"area":null},{"id":"2507.06457","title":"A Systematic Analysis of Hybrid Linear Attention","created":"2025-07-08","cited_by_count":39,"area":null},{"id":"2503.01496","title":"Liger: Linearizing Large Language Models to Gated Recurrent Structures","created":"2025-03-03","cited_by_count":33,"area":null},{"id":"2501.05414","title":"LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation","created":"2025-01-09","cited_by_count":29,"area":null},{"id":"2407.10969","title":"Q-Sparse: All Large Language Models can be Fully Sparsely-Activated","created":"2024-07-15","cited_by_count":25,"area":null},{"id":"2507.19595","title":"Efficient Attention Mechanisms for Large Language Models: A Survey","created":"2025-07-25","cited_by_count":22,"area":null},{"id":"2406.09297","title":"MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding","created":"2024-06-13","cited_by_count":21,"area":null},{"id":"2504.04308","title":"Gating is Weighting: Understanding Gated Linear Attention through In-context Learning","created":"2025-04-06","cited_by_count":12,"area":null},{"id":"2410.03156","title":"MELODI: Exploring Memory Compression for Long Contexts","created":"2024-10-04","cited_by_count":11,"area":null},{"id":"2510.07318","title":"Artificial Hippocampus Networks for Efficient Long-Context Modeling","created":"2025-10-08","cited_by_count":11,"area":null},{"id":"2506.17121","title":"Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?","created":"2025-06-20","cited_by_count":10,"area":null},{"id":"2501.16085","title":"ARFlow: Autoregressive Flow with Hybrid Linear Attention","created":"2025-01-27","cited_by_count":7,"area":null},{"id":"2607.15456","title":"Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers","created":"2026-07-16","cited_by_count":7,"area":null},{"id":"2408.15040","title":"A Survey of Large Language Models for European Languages","created":"2024-08-27","cited_by_count":6,"area":null},{"id":"2410.06577","title":"Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions","created":"2024-10-09","cited_by_count":6,"area":null},{"id":"2510.17196","title":"Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models","created":"2025-10-20","cited_by_count":6,"area":null},{"id":"2512.23852","title":"Trellis: Learning to Compress Key-Value Memory in Attention Models","created":"2025-12-29","cited_by_count":5,"area":null},{"id":"2411.17426","title":"CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2502.00439","title":"UniAttn: Reducing Inference Costs via Softmax Unification for Post-Training LLMs","created":"2025-02-01","cited_by_count":3,"area":null},{"id":"2505.17331","title":"ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training","created":"2025-05-22","cited_by_count":3,"area":null},{"id":"2511.23319","title":"Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models","created":"2025-11-28","cited_by_count":3,"area":null},{"id":"2602.01075","title":"ConvexBench: Can LLMs Recognize Convex Functions?","created":"2026-02-01","cited_by_count":3,"area":null},{"id":"2505.11820","title":"Chain-of-Model Learning for Language Model","created":"2025-05-17","cited_by_count":2,"area":null},{"id":"2508.08446","title":"OverFill: Two-Stage Models for Efficient Language Model Decoding","created":"2025-08-11","cited_by_count":2,"area":null},{"id":"2507.01900","title":"High-Layer Attention Pruning with Rescaling","created":"2025-07-02","cited_by_count":1,"area":null},{"id":"2510.18830","title":"MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2511.03830","title":"Divide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2602.04491","title":"Greedy-Gnorm: A Gradient Matrix Norm-Based Alternative to Attention Entropy for Head Pruning","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2602.17387","title":"DRetHTR: Linear-Time Decoder-Only Retentive Network for Handwritten Text Recognition","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2605.06105","title":"Shallow Prefill, Deep Decoding: Efficient Long-Context Inference via Layer-Asymmetric KV Visibility","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2606.02780","title":"Do Value Vectors in Deep Layers Need Context from the Residual Stream?","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2608.29539","title":"LoGo: Token-Level Dynamic Local-Global Attention","created":"2026-08-30","cited_by_count":1,"area":null},{"id":"2406.06571","title":"SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM","created":"2024-06-03","cited_by_count":0,"area":null},{"id":"2503.03269","title":"Conformal Transformations for Symmetric Power Transformers","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2506.06708","title":"A Survey of Retentive Network","created":"2025-06-07","cited_by_count":0,"area":null},{"id":"2507.23292","title":"SequenceLayers: Sequence Processing and Streaming Neural Networks Made Easy","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2510.13860","title":"ShishuLM : Achieving Optimal and Efficient Parameterization with Low Attention Transformer Models","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2601.08131","title":"Attention Projection Mixing with Exogenous Anchors","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2601.19089","title":"EPAS: Efficient Training with Progressive Activation Sharing","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2601.22630","title":"LINA: Linear Autoregressive Image Generative Models with Continuous Tokens","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.16054","title":"CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2605.18807","title":"Block-Based Double Decoders","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2606.08804","title":"Q-Delta: Beyond Key-Value Associative State Evolution","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2608.18486","title":"WhiteMatter: All-to-All Cross-Layer Connections via KV Source Mixing","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2608.19920","title":"Learning how to Forget: Fine-tuning for Long-Context Sparse Attention","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2609.28006","title":"Shared Global KV with Layer-Specific Local History","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2609.32759","title":"The Extender: A Log-Structured Transformer","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.02383","title":"The Surprising Effectiveness of Shared Memory in Looped Transformers","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.04635","title":"LatentIndex: Cross-Layer Sharing with Layer-Specific Selection for Sparse Attention","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.07591","title":"Recurrent Looped Transformer","created":"2026-10-06","cited_by_count":0,"area":null},{"id":"2610.10114","title":"Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.13735","title":"Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}