Δ-Mem: Efficient Online Memory for Large Language Models

Researchers from multiple institutions introduced δ-Mem, an efficient online memory system for LLMs targeting long-term assistants and agent pipelines. The method addresses the inefficiency of naive context expansion by selectively accumulating and reusing historical information. Practitioners building multi-turn agents or persistent assistants should watch this, though benchmark details remain pending full paper access.