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Source note3 min read

Extending LLM Context via Associative Recurrent Memory

Source

arXiv

arxiv.org/abs/2607.11614v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov, Yuri Kuratov, Lyudmila Rvanova, Mikhail Katkov, Ilia Sochenkov, Misha Tsodyks, Timothy Baldwin, Mikhail Burtsev, Artem Shelmanov
  • Venue: arXiv: cs.CL
  • Published: 2026-07-13
  • Collected: 2026-07-14
  • URL: http://arxiv.org/abs/2607.11614v1
  • PDF: https://arxiv.org/pdf/2607.11614v1
  • DOI: UNKNOWN

Plain-Language Brief

In this research, the authors propose a new method called Associative Recurrent Memory Transformer (ARMT) to extend the context length of large language models (LLMs). This is crucial for many real-world applications as standard transformers have limitations due to quadratic compute and linear memory scaling. The ARMT is designed to enable long-context processing, maintain constant memory scaling, and improve efficiency. The authors make three main contributions: creating two domain-specific datasets, proposing a training recipe for the ARMT, and demonstrating that ARMT-augmented models can process longer inputs without performance degradation, generalize better to out-of-distribution context lengths, and require 30% fewer FLOPs while maintaining performance within the original context window.

What the Document Says

  • The authors aim to extend the context length of large language models (LLMs).
  • Standard transformers are constrained by quadratic compute and linear memory scaling.
  • The Associative Recurrent Memory Transformer (ARMT) is proposed as a solution for long-context processing in LLMs with constant memory scaling and better efficiency.
  • Two domain-specific long-context datasets are constructed for evaluation.
  • A training recipe is proposed for ARMT-based context extension, including continued pre-training, synthetic data generation, curriculum learning, and selective memory integration.
  • The study shows that ARMT-augmented models can process inputs beyond their original limits without performance degradation.
  • These models generalize better to out-of-distribution context lengths.
  • They require 30% fewer FLOPs while maintaining performance within the original context window.

Key Concepts Mentioned

  • large language models (LLMs)
  • Associative Recurrent Memory Transformer (ARMT)
  • quadratic compute scaling
  • linear memory scaling
  • long-context processing
  • domain-specific datasets
  • continued pre-training
  • synthetic long-context data generation
  • curriculum learning
  • selective integration
  • FLOPs

Claims Made by the Document

  • ARMT enables long-context processing in LLMs.
  • ARMT maintains constant memory scaling and improves efficiency.
  • The proposed training recipe improves ARMT performance.
  • ARMT-augmented models can process inputs beyond their original limits without degrading performance.
  • ARMT-augmented models generalize more effectively to out-of-distribution context lengths.
  • ARMT requires 30% fewer FLOPs while maintaining performance within the original context window.

Evidence Used

  • The text describes the ARMT and its benefits.
  • The text explains the training recipe and its components.
  • The text presents the results of the experimental study.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling. In this work, we investigate the Associative Recurrent Memory Transformer (ARMT) as a practical approach for enabling long-context processing in LLMs, constant memory scaling, and better efficiency. We make three main contributions. First, we construct two domain-specific long-context datasets designed to evaluate realistic workloads, focusing on narrow-domain fine-tuning scenarios. Second, we propose a comprehensive training recipe for ARMT-based context extension, combining continued pre-training, synthetic long-context data generation, curriculum learning, and selective integration of associative memory into chosen model layers. Third, we present an extensive experimental study demonstrating that ARMT-augmented models: (i) process inputs well beyond their original context limits without degrading performance relative to in-limit baselines; (ii) generalize more effectively to out-of-distribution context lengths; and (iii) need 30% less FLOPs while preserving baseline performance within the original context window.