-mem: Efficient Online Memory for Large Language Models
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 -mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. -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 online memory state, -mem improves the average score to that of the frozen backbone and that of the strongest non--mem memory baseline. It achieves larger gains on memory-heavy benchmarks, reaching on MemoryAgentBench and 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.