MemEdit: Fine-Grained Memory Surgery for Expandable Parametric Agent Memory

Parametric memory modules, which are lightweight neural networks pretrained to internalize retrieval patterns, have emerged as a promising alternative to retrieval-augmented generation (RAG) for equipping large language model (LLM) agents with persistent knowledge. However, existing parametric memories operate as *write-only* stores: once knowledge is encoded into parameters, there is no principled mechanism for locating, updating, or erasing individual memory traces without costly retraining. This limitation is critical in long-lived agent scenarios where facts become outdated, user preferences shift, and privacy regulations mandate selective forgetting. We introduce **MemEdit**, a fine-grained *memory surgery* framework that brings the full spectrum of CRUD operations: **C**reate, **R**ead, **U**pdate, **D**elete, to parametric agent memory. MemEdit carries out every operation in four stages: *locate*, *preserve*, *intervene*, and *verify*. A *Memory Attribution* mechanism based on integrated gradients localizes each memory trace to a neuron footprint, which *Query* exposes for inspection. The three write operations share one constrained rank-one update whose output side is confined to the footprint and whose input side is projected away from the keys of preserved memories: (i) *Insert* writes into the null space of existing keys with exact preservation; (ii) *Modify* rewrites a stored value under a locality penalty; and (iii) *Delete* applies projected gradient ascent that leaves all other stored keys unchanged. Every write is journaled as a reversible low-rank delta and verified. When the null space is exhausted, a *Mixture-of-Memory-Experts* (MoME) expansion strategy adds independently editable memory shards, so that editing capacity grows with the number of shards. Experiments on LoCoMo and MemBench benchmarks show that MemEdit achieves 94.6% edit success rate while preserving 97.3% of unrelated memory accuracy, scales to over 10K sequential edits without catastrophic forgetting, and outperforms both non-parametric and parametric baselines in downstream QA.