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state_space_models [2026/03/30 21:00] – Remove redundant References section agentstate_space_models [2026/03/30 21:01] (current) – Restore References section agent
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 Typical hybrid ratios range from 1 attention layer per 3–7 SSM layers, with the optimal ratio remaining an active research question. Typical hybrid ratios range from 1 attention layer per 3–7 SSM layers, with the optimal ratio remaining an active research question.
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 +===== References =====
 +
 +  - Albert Gu, Karan Goel, Christopher Ré. "Efficiently Modeling Long Sequences with Structured State Spaces" (S4). ICLR 2022. [[https://arxiv.org/abs/2111.00396|arXiv:2111.00396]]
 +  - Albert Gu, Tri Dao. "Mamba: Linear-Time Sequence Modeling with Selective State Spaces". Dec 2023. [[https://arxiv.org/abs/2312.00752|arXiv:2312.00752]]
 +  - Tri Dao, Albert Gu. "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality" (Mamba-2). May 2024. [[https://arxiv.org/abs/2405.21060|arXiv:2405.21060]]
 +  - Soham De et al. "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models". 2024. [[https://arxiv.org/abs/2402.19427|arXiv:2402.19427]]
 +  - Bo Peng et al. "RWKV-7 Goose". 2025. [[https://arxiv.org/abs/2503.14456|arXiv:2503.14456]]
 +  - Team Jamba. "Jamba: A Hybrid Transformer-Mamba Language Model". ICLR 2025.
 +  - Samy Jelassi et al. "Repeat After Me: Transformers are Better than State Space Models at Copying". ICML 2024.
 +  - AI21 Labs. "The Rise of Hybrid LLMs". [[https://www.ai21.com/blog/rise-of-hybrid-llms/|ai21.com]]
  
 ===== See Also ===== ===== See Also =====
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   * [[inference_optimization|Inference Optimization]]   * [[inference_optimization|Inference Optimization]]
   * [[on_device_agents|On-Device Agents]]   * [[on_device_agents|On-Device Agents]]
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