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Δ-Mem: Augmenting LLMs with Efficient Online Memory

Δ-Mem is a lightweight memory mechanism that augments frozen LLM backbones with a compact online state. It uses a fixed-size state matrix, updated by delta-rule learning, to generate low-rank corrections for attention computation during generation. This approach significantly improves performance on memory-heavy tasks without costly context expansion or full model fine-tuning.