Extending LLM Context via Associative Recurrent Memory
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 pro
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- PossiblePossibly related (embedding) · 62%Breakthrough in long-context efficiency announced →
- PossiblePossibly related (embedding) · 55%RimantasZ/contextspy →
- PossiblePossibly related (embedding) · 54%thu-pacman/chitu →
- PossiblePossibly related (embedding) · 53%Atomic-man007/Awesome_Multimodel_LLM →
- PossiblePossibly related (embedding) · 53%chrisliu298/awesome-llm-unlearning →
- LinkedLinked via arxiv author · 85%Gleb Kuzmin →
“Extending LLM Context via Associative Recurrent Memory”
- LinkedLinked via arxiv author · 85%Ivan Rodkin →
“Extending LLM Context via Associative Recurrent Memory”
- LinkedLinked via arxiv author · 85%Aydar Bulatov →
“Extending LLM Context via Associative Recurrent Memory”