Who cites «Linear Transformers Are Secretly Fast Weight Programmers»
- xLSTM: Extended Long Short-Term MemoryConference on Neural Information Processing Systems (NeurIPS)Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.2024 · 296 citations
- Transformers learn in-context by gradient descentarXiv:2212.07677Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.2022 · 247 citations
- What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
- Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
- BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-HaystackConference on Neural Information Processing Systems (NeurIPS)Yuri Kuratov, Aydar Bulatov, Petr Anokhin, et al.2024 · 88 citations
- Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attentionarXiv:2404.07143Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal2024 · 80 citations
- A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
- Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language ModelingarXiv:2406.07522Liliang Ren, Yang Liu, Yadong Lu, et al.2024 · 59 citations
- Simple linear attention language models balance the recall-throughput tradeoffarXiv:2402.18668Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.2024 · 59 citations
- Kimi Linear: An Expressive, Efficient Attention ArchitecturearXiv:2510.26692Kimi Team, Yu Zhang, Zongyu Lin, et al.2025 · 51 citations
- Kimi K3: Open Frontier IntelligencearXiv:2607.24653Kimi Team, Tongtong Bai, Yifan Bai, et al.2026 · 40 citations
- RWKV-7 "Goose" with Expressive Dynamic State EvolutionarXiv:2503.14456Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.2025 · 23 citations
- Associative Recurrent Memory TransformerarXiv:2407.04841Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.2024 · 8 citations
- Just read twice: closing the recall gap for recurrent language modelsarXiv:2407.05483Simran Arora; Aman Timalsina; Aaryan Singhal; Benjamin Spector; Sabri Eyuboglu; Xinyi Zhao; Ashish Rao; Atri Rudra;, Christopher Re2024 · 8 citations
- StateX: Enhancing RNN Recall via Post-training State ExpansionFindings of the Association for Computational LinguisticsXingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.2026 · 1 citation
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