[2605.12357] $δ$-mem: Efficient Online Memory for Large Language Models

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Computer Science > Artificial Intelligence

arXiv:2605.12357 (cs)

[Submitted on 12 May 2026]

Title:$δ$-mem: Efficient Online Memory for Large Language Models

Authors:Jingdi Lei, Di Zhang, Junxian Li, Weida Wang, Kaixuan Fan, Xiang Liu, Qihan Liu, Xiaoteng Ma, Baian Chen, Soujanya Poria<br>View a PDF of the paper titled $\delta$-mem: Efficient Online Memory for Large Language Models, by Jingdi Lei and 9 other authors

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Abstract:Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and often fails to ensure effective context utilization. We propose $\delta$-mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. $\delta$-mem compresses past information into a fixed-size state matrix updated by delta-rule learning, and uses its readout to generate low-rank corrections to the backbone's attention computation during generation. With only an $8\times8$ online memory state, $\delta$-mem improves the average score to $1.10\times$ that of the frozen backbone and $1.15\times$ that of the strongest non-$\delta$-mem memory baseline. It achieves larger gains on memory-heavy benchmarks, reaching $1.31\times$ on MemoryAgentBench and $1.20\times$ on LoCoMo, while largely preserving general capabilities. These results show that effective memory can be realized through a compact online state directly coupled with attention computation, without full fine-tuning, backbone replacement, or explicit context extension.

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Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2605.12357 [cs.AI]

(or<br>arXiv:2605.12357v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.12357

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Jingdi Lei [view email]<br>[v1]<br>Tue, 12 May 2026 16:31:44 UTC (609 KB)

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