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LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining

Summary

An ICML 2026 paper introduces LoKiFormer, an LLM architecture adding Local Fusion Attention and a Knowledge Memory Module to address redundant local modeling and MoE knowledge-computation coupling, achieving 1.33x faster pretraining convergence.

SourcearXiv Machine LearningAuthor: Qiuwu Chen, Zimo Liu, Yuchen Li, Ying Sun, Yifan Zhang, Zhijie Qiu, Zeng You, Ryan Dong, Simeng Ma, Yaofo Chen, Mingkui Tan
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
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[Submitted on 12 Aug 2026]

Title:LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining

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Abstract:Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locality, leading to redundant modeling of sequence-internal local information; (ii) mixture-of-experts (MoE) implicitly couples knowledge storage with computational pathways, hindering flexible access to sequence-external global knowledge. To overcome these limitations, we propose LoKiFormer, a novel LLM architecture that augments the standard decoder with two dedicated modules: 1) Local Fusion Attention (LFA), which incorporates a convolutional fusion to attention, explicitly capturing local patterns and allowing the attention to operate on more informative representations; 2) Knowledge Memory Module (KMM), which introduces a parametric key-value memory that explicitly stores global knowledge in addressable slots, decoupling storage from computation and enabling direct knowledge retrieval. Together, these modules enable LoKiFormer to achieve more efficient and effective integration of information at both levels. Experimental results show that LoKiFormer converges 1.33x faster in pre-training than baseline models, underscoring its superiority over existing LLM architectures.

Comments: Accepted by ICML 2026

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2608.12419 [cs.LG]

(or arXiv:2608.12419v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.12419

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Qiuwu Chen [view email] [v1] Wed, 12 Aug 2026 07:45:11 UTC (1,080 KB)

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Key points and analysis

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Key points

  • Identifies two pretraining inefficiencies: self-attention lacks local inductive bias, and MoE couples knowledge storage with computation.
  • Local Fusion Attention (LFA) adds convolutional fusion to explicitly capture local patterns.
  • Knowledge Memory Module (KMM) uses parametric key-value memory to decouple storage from computation and enable direct knowledge retrieval.
  • Achieves 1.33x faster pretraining convergence than baselines.

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