Parametric Session States Recover Retrieval Intent without Replaying History

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 Parametric Session State (), a reusable session-memory layer that remembers what to search for while keeping factual knowledge external. An offline writer compiles the ordered interaction together with answer-free retrieval supervision into a fixed-shape, session-specific LoRA state. At query time, the state briefly produces a compact, answer-free retrieval clue; the system then retrieves inspectable source text, unloads the state, and lets the frozen base model answer from that evidence. Across controlled and sessionized public evaluations, correct PSS states improve target-evidence retrieval for underspecified follow-ups. On the registered controlled and public tracks, matched-wrong and shuffled controls attribute the gains to the corresponding session, and the retrieval benefit transfers across model families and heterogeneous backends. A three-seed carrier confirmation further isolates the contribution of ordered history beyond the supplied retrieval target and shows that the parameter carrier outperforms a compute-matched persistent text memory under dense semantic retrieval. These results establish as an effective retrieval-supervised memory carrier for RAG: it restores missing session scope, supports strong evidence access across diverse systems, and preserves an external, auditable knowledge store.