MEMTIER: Tiered Retrieval, Session-Level Injection, and Typed Consolidation for Long-Running LLM Agents

Abstract

Long-running large language model (LLM) agents accumulate memory across many sessions, yet most agent runtimes store it as flat text. We present MEMTIER, a tiered memory architecture and consolidation framework for an open-source agent runtime and study three questions: what to store, what to inject, and what to keep. First, a tiered episodic–semantic memory reaches an accuracy of 0.382 and an F1 of 0.412 on LongMemEval-S (N = 500) with a 7B model on a 6 GB GPU—a +33-point gain over no retrieval. A controlled single-pipeline ablation shows the gains come from two components—semantic pre-population and two-stage scoping—while the multi-signal retrieval scaffold is, in this setting, an operational pass-through of BM25 top-k; removing it entirely leaves accuracy unchanged. An oracle analysis explains why the system is retrieval-limited: on the diagnostic subset, the correct session is present in the candidate set 98% of the time, so the primary binding constraint is which evidence reaches the reader. A larger generator helps but does not remove the retrieval bottleneck. Second, the bottleneck is injection granularity, not session recall (90.9% session coverage versus 4.5% fact recall@2). Injecting all facts from the top-k retrieved sessions improves multi-session accuracy by +0.120 and knowledge update accuracy by +0.205, and a controlled comparison isolates a structure effect distinct from token quantity. Third, we cast memory consolidation as a Markov decision process, characterize three structural failure modes that render a learned policy uninformative on single-subject benchmarks, and show preliminary evidence on a live agent benchmark that a typed, keyword-based policy recovers near-oracle performance for one memory type (threat pattern memory) without supervision. Type-dependent retention for other memory types is proposed but not yet evaluated. We frame these as related diagnostic studies rather than a single validated pipeline, and, together, they cast agent memory as a pattern recognition problem: recognizing which session patterns carry evidence and which knowledge types to retain.