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

View PDF 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.12357 [cs.AI]   (or arXiv:2605.12357v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.12357 arXiv-issued DOI via DataCite (pending registration) Submission history From: Jingdi Lei [view email] [v1] Tue, 12 May 2026 16:31:44 UTC (609 KB)