MoNe: Modular Neural Memory for Efficient Long Context Inference
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Computer Science > Artificial Intelligence
arXiv:2608.17616 (cs)
[Submitted on 18 Aug 2026]
Title:MoNe: Modular Neural Memory for Efficient Long Context Inference
Authors:Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy, Sunghyun Park, Hyoungwoo Park, Jeongho Kim, Arash Behboodi, Kyuwoong Hwang, Sungrack Yun
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Abstract:We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in fixed-size segments via test-time learning of fast-weight neural memory networks with layer-localized gradient updates; at inference, the memory generates keys and values from the query tokens alone, with no context tokens re-read. This two-phase design decouples inference cost from context length, achieving $O(N)$ preprocessing and $O(1)$ query cost with peak GPU memory that does not grow with $N$. At 128K tokens, MoNe reduces both compute and peak GPU memory by approximately 80% compared to ICL with only 6.4% parameter overhead. MoNe generalizes to context lengths far beyond the backbone's native window, achieving strong performance on needle-in-a-haystack and word extraction benchmarks from RULER, where ICL degrades sharply.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.17616 [cs.AI] |
| (or arXiv:2608.17616v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.17616
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled MoNe: Modular Neural Memory for Efficient Long Context Inference, by Wonguk Cho and 8 other authors
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