arXiv AI October 5, 2026 By Yehya Farhat, Michael Desmond, Anastasios Kyrillidis

Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning

The paper introduces GraphMemory, a lightweight graph-based memory system designed to improve token efficiency in test-time continual learning for large language models. By retrieving only relevant subgraphs for each query, GraphMemory keeps the amount of retrieved memory constant as more examples are processed, avoiding the token cost and performance degradation of traditional shared-context approaches. Experiments demonstrate that GraphMemory achieves competitive downstream performance while using roughly 81‑85% fewer memory‑construction tokens than baseline methods.

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