$δ$-mem: Efficient Online Memory for Large Language Models
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914559755812864 |
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| author | Lei, Jingdi Zhang, Di Li, Junxian Wang, Weida Fan, Kaixuan Liu, Xiang Liu, Qihan Ma, Xiaoteng Chen, Baian Poria, Soujanya |
| author_facet | Lei, Jingdi Zhang, Di Li, Junxian Wang, Weida Fan, Kaixuan Liu, Xiang Liu, Qihan Ma, Xiaoteng Chen, Baian Poria, Soujanya |
| contents | 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 $δ$-mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. $δ$-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, $δ$-mem improves the average score to $1.10\times$ that of the frozen backbone and $1.15\times$ that of the strongest non-$δ$-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12357 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | $δ$-mem: Efficient Online Memory for Large Language Models Lei, Jingdi Zhang, Di Li, Junxian Wang, Weida Fan, Kaixuan Liu, Xiang Liu, Qihan Ma, Xiaoteng Chen, Baian Poria, Soujanya Artificial Intelligence 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 $δ$-mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. $δ$-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, $δ$-mem improves the average score to $1.10\times$ that of the frozen backbone and $1.15\times$ that of the strongest non-$δ$-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. |
| title | $δ$-mem: Efficient Online Memory for Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.12357 |