#1Δ-Mem: Efficient Online Memory for Large Language Models
Researchers introduce δ-mem, a lightweight memory mechanism that compresses historical information into a fixed-size 8x8 state matrix, delivering 1.31x improvement on memory-intensive benchmarks without fine-tuning or expanding the context window. The method works alongside a frozen full-attention backbone using delta-rule learning and low-rank corrections, offering a practical solution to the persistent memory limitations of current LLMs.