arXiv:2610.33047v1 Announce Type: new 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}
arXiv：2610.33047v1 发布类型：新 摘要：长期运行的助手通常会理解那些其指代对象、约束条件以及主动目标早在数轮对话前就已经确定的后续请求。因此，核心挑战不仅在于上下文长度，还在于检索意图：例如，“使用先前的配置”这样的当前查询可能会省略定位相关证据所需的项目、实体和版本等术语。长上下文模型可以通过重放交互过程来恢复这些术语，但同时也需要反复承担处理历史记录的开销。传统的检索增强生成（RAG）方法成本较低，却假定查询本身已包含充分的信息；而对话式改写要么重新读取历史，要么依赖于显式维护的文本记忆。我们提出了“参数化会话状态”。
