[Submitted on 17 Oct 2026]
Abstract:Long-running assistants often interpret follow-up requests whose referents, constraints, and active goals were established many turns earlier. The central challenge is therefore not only context length, but retrieval intent: a current query such as “use the previous configuration” may omit the project, entity, and version terms needed to locate relevant evidence. Long-context models can recover those terms by replaying the interaction, but repeatedly pay the cost of processing history. Conventional retrieval-augmented generation (RAG) is cheaper, yet assumes an information-sufficient query; conversational rewriting either rereads the history or depends on an explicitly maintained textual memory. We introduce emph{Parametric Session State} (method{}), a reusable session-memory layer that remembers what to search for while keeping factual knowledge external.
Submission history
From: Qingsen Ma [view email]
[v1] Sat, 17 Oct 2026 05:07:32 UTC (31,854 KB)
