FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents

arXiv:2608.04095v1 Announce Type: new Abstract: Large language model LLM agents are increasingly used as personalized assistants in highstakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.