Yehya Farhat

The work introduces a unified formulation of context optimization that interprets an agent memory system update as an optimization update procedure over the model's context, and on that basis proposes GraphMemory, a lightweight graph-based memory that accumulates, refines, organizes, and connects reusable strategies; each query retrieves only the relevant subgraph, so under bounded retrieval the amount of retrieved memory stays constant as the number of processed examples grows, and experiments show competitive downstream performance while using approximately 81-85% fewer memory-construction tokens than the baselines.

The work introduces a unified formulation of context optimization that interprets an agent memory system update as an optimization update procedure over the model's context, and on that basis proposes GraphMemory, a lightweight graph-based memory that accumulates, refines, organizes, and connects reusable strategies; each query retrieves only the relevant subgraph, so under bounded retrieval the amount of retrieved memory stays constant as the number of processed examples grows, and experiments show competitive downstream performance while using approximately 81-85% fewer memory-construction tokens than the baselines.