Transition-Parametric Memory for Language Agents: Reversible Compiled Memory Fields

Long-horizon language agents can reuse past experience, but combining record-level editability with compact memory access remains challenging. Textual memories typically require retrieval and in-context processing, while experience absorbed into shared parameters can be difficult to audit or selectively remove. We formulate **transition-parametric memory**, which preserves each decision transition as an identifiable record with a separate parametric contribution. We introduce **Reversible Compiled Memory Fields** (RCMF) to aggregate these contributions into a fixed-shape field queried by the current agent state to modulate a frozen language model. Online field reads avoid textual replay and have computational cost independent of memory count. Individual records can be inserted or removed through additive updates without retraining the model or recompiling other records. Experiments on AppWorld, ALFWorld, and WebShop show favorable task performance against multiple baselines. Core implementation code is provided in Appendix D.