MGMem: An Efficient, Deterministic, and Provenance-Preserving Framework for Long-Horizon Agent Memory

Long-horizon Large Language Model (LLM) agents need persistent memory that surfaces the right past interaction at the right time across thousands of turns. Current systems index by invoking generative LLMs to rewrite history into summaries, facts, or knowledge graphs—a step that is (a) costly (up to $\sim 50$K LLM calls and $35.83$M completion tokens per build), (b) non-deterministic (undermining reproducibility and giving inconsistent answers across runs), and (c) provenance-erasing (the reader sees synthesized text, not the original turn). We propose MGMem, an LLM-call-free framework that replaces this step with a deterministic discriminative-parser pipeline: recurring mentions across sessions are organized into an incidence graph, question mentions are resolved by a subject-conditioned three-stage resolver, and raw dialogue episodes are returned to the reader. On LoCoMo, MGMem outperforms matched-protocol generated-memory and retrieval baselines at zero generative indexing calls; on the full LongMemEval-S, it remains competitive overall and leads on single-session question types. Anonymous code: https://anonymous.4open.science/r/mentiongraph.