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October 5, 2025Open Access

Local Linear Attention: An Optimal Interpolation of Linear and Softmax Attention For Test-Time Regression

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Authors

YZYuan ZuoYYYutong YinZZZhichen Zeng

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Overview

Local Linear Attention improves performance in test-time regression tasks, suggesting better associative memory and memory efficiency.

Key Points

  • LLA shows improved performance metrics over traditional linear and softmax attention mechanisms, enhancing memory capabilities.
  • The proposed FlashLLA algorithm reduces computational complexity and is optimized for modern hardware accelerators.
  • Empirical validation confirms LLA's effectiveness in handling non-stationarity in various regression and state tracking tasks.
  • The implementation achieves significant memory efficiency, outperforming strong baselines in test-time training and in-context learning scenarios.

Cite This Study

Zuo et al. (2025) studied this question.

synapsesocial.com/papers/68e25382d6d66a53c2474afahttps://doi.org/10.48550/arxiv.2510.01450
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