Cited by: Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

15 citation edges inside this catalog. A title match is weaker evidence than an identifier match. Zero entries does not mean no citations on the web.

• Unlimiformer: Long-Range Transformers with Unlimited Length Input
  arxiv:2305.01625 · evidence: title
• Metalearning Continual Learning Algorithms
  arxiv:2312.00276 · evidence: title
• Zoology: Measuring and Improving Recall in Efficient Language Models
  arxiv:2312.04927 · evidence: id
• Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
  arxiv:2404.07143 · evidence: title
• TransformerFAM: Feedback attention is working memory
  arxiv:2404.09173 · evidence: title
• xLSTM: Extended Long Short-Term Memory
  arxiv:2405.04517 · evidence: title
• Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
  arxiv:2502.05171 · evidence: title
• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
  arxiv:2506.13585 · evidence: title
• AlphaGo Moment for Model Architecture Discovery
  arxiv:2507.18074 · evidence: title
• Nested Learning: The Illusion of Deep Learning Architectures
  arxiv:2512.24695 · evidence: title
• The Topological Trouble With Transformers
  arxiv:2604.17121 · evidence: title
• A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets
  arxiv:2607.02303 · evidence: id
• BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
  arxiv:2608.09888 · evidence: title
• Recirculation
  arxiv:2608.17981 · evidence: title
• The most cited neural nets of the 20th century (Schmidhuber)
  url:people.idsia.ch/~juergen/most-cited-neural-nets.html · evidence: title