Handover of In-Context Learning State Across Session Boundaries

When an LLM conversation exceeds context limits, restarts, or gets handed to another agent, someone must decide what information survives the transition. This paper formalizes that handover problem mathematically, distinguishing exact recovery from statistically sufficient preservation, and proposes a three-part memory record for decisions, summarized evidence, and irreplaceable raw observations. With theoretical bounds from Gaussian regression and nonparametric settings, it quantifies how much memory a task-continuation actually requires. This is directly applicable to multi-agent LLM systems, long-running assistants, and any application needing principled memory compression across session boundaries. Authors: Masahiro Kato, Taka Kato Paper: https://arxiv.org/abs/2608.14528v1