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RunningTensor generalizes linear attention to higher-order recurrent states

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced RunningTensor, a novel method that generalizes linear attention and state-space models to higher-order recurrent states. This advancement allows for the representation of more complex interactions by extending the memory tensor from a second-order matrix to an order-o tensor. The RunningTensor maintains linear time complexity with respect to sequence length while increasing working memory capacity, demonstrating improved performance on synthetic associative recall tasks and showing promise for language understanding and retrieval. AI

IMPACT Introduces a new architectural approach that could enhance memory capacity and performance in sequence modeling tasks.

RANK_REASON The cluster describes a new method and theoretical advancement in sequence modeling presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RunningTensor generalizes linear attention to higher-order recurrent states

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The cluster describes a new method and theoretical advancement in sequence modeling presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Luca Herranz-Celotti, Vincent Guigue ·

    RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States

    arXiv:2609.12814v1 Announce Type: new Abstract: Linear attention and state-space models provide linear-time sequence modeling, but their recurrent memory remains a second-order tensor (a matrix), limiting the order of interactions that can be represented in the state. We introduc…

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