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arXiv · 2507.00453

Recurrent Memory-Augmented Transformers with Chunked Attention for Long-Context Language Modeling

Ankit Kashyap
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Abstract

We present a Transformer architecture for long-context language modeling that combines global attention with two biologically inspired components: chunked local attention and a gated FIFO memory mechanism. This unified attention block allows the model to efficiently handle both short-range and long-range dependencies without increasing attention cost quadratically. The memory module persistently stores past token representations using a gated update mechanism inspired by recurrent networks. Rotary positional encoding is applied per attention head to enable directionally disentangled, scale-invariant positional signals. The architecture is implemented entirely from scratch in PyTorch, with no reliance on high-level libraries, enabling transparent and modular experimentation. Our model offers a lightweight and extensible design for tasks such as dialogue modeling, code completion, and document understanding.

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BibTeXRIS

Ankit Kashyap. 2025-07-01. Recurrent Memory-Augmented Transformers with Chunked Attention for Long-Context Language Modeling. https://arxiv.org/abs/2507.00453

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