To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models

Apple Machine Learning Journal / 3/27/2026

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Key Points

  • The paper “To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models” presents a method showing how enabling tool use can improve sequence length generalization in state space models.
  • It focuses on the generalization challenge where models often perform well on seen lengths but degrade on longer/unseen lengths.
  • The work is positioned in the research areas of methods/algorithms and tools/platforms/frameworks, indicating both algorithmic and practical integration aspects.
  • The authors (Eran Malach, Omid Saremi, Sinead Williamson, and others) publish the study as an arXiv/ICLR-related paper dated March 2026.
  • Overall, the contribution suggests that augmenting state space models with tool-use mechanisms can extend their effective operating range to longer contexts.
State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation, enabled by fixed-size memory and linear scaling of computational complexity. We begin this work by showing a simple theoretical result stating that SSMs cannot accurately solve any “truly long-form” generation problem (in a sense we formally define), undermining their main competitive advantage. However, we show that this limitation can be mitigated by allowing SSMs interactive access to external tools. In fact, we…

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