A study investigates session handover in applications using large language models, formulating handover as transfer of a task-relative in-context learning state. It distinguishes exact recovery from preservation of target distribution, characterizes the coarsest deterministic sufficient handover under an exogeneity condition, and proposes a three-part record storing decisions, task-justified statistics, and original observations.
The paper examines how to continue a task in a new session when context limits, application restarts, or agent handoffs occur. It formalizes handover as transferring an in-context learning state and provides theoretical results on sufficient handover and bit requirements. A three-part record is proposed to balance exactness and compression.
The work introduces a formal framework for session handover based on predictive equivalence and exogeneity, yielding a fixed-length bit requirement for sufficient handover. It analyzes the cost of writing before the downstream query is known and demonstrates exact finite-dimensional results in Gaussian linear regression.
This research addresses practical challenges in long-running LLM applications, agent handoffs, and context window limits. The proposed record structure could inform implementation of state transfer mechanisms in multi-agent systems and persistent AI assistants.
Efficient session handover can reduce token costs and improve continuity in enterprise AI workflows, agent-based automation, and customer support systems. The theoretical guarantees may support reliable state transfer in regulated or high-stakes applications.
Observable next signals include follow-up papers applying the handover framework to non-linear models, empirical evaluations of the three-part record in real-world agent systems, and potential adoption of handover protocols in LLM application frameworks.