MEMTIER: Tiered Memory Architecture and Retrieval Bottleneck Analysis for Long-Running Autonomous AI Agents

TL;DR

MEMTIER introduces a structured, tiered memory architecture with retrieval and consolidation mechanisms that significantly improve long-term memory coherence and retrieval accuracy in autonomous AI agents, all running locally on consumer hardware.

Contribution

The paper presents MEMTIER, a novel tripartite memory system with structured episodic storage, adaptive retrieval, and semantic consolidation, enhancing long-term memory performance in autonomous AI agents.

Findings

33 percentage point improvement on LongMemEval-S benchmark

Single-session recall exceeds baseline by 20 percentage points

Temporal reasoning and multi-session synthesis metrics significantly improved

Abstract

Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over 72-hour operation windows due to four compounding failure modes in existing flat-file memory systems. We present MEMTIER, a tripartite memory architecture for the OpenClaw agent runtime that introduces a structured episodic JSONL store, a five-signal weighted retrieval engine, an attention-attributed cognitive weight update loop, an asynchronous consolidation daemon promoting episodic facts to a semantic tier, and a PPO-based policy framework for adapting retrieval weights (infrastructure validated; performance gains pending camera-ready). On the full 500-question LongMemEval-S benchmark (Wu et al., 2025), MEMTIER achieves Acc=0.382, F1=0.412 with Qwen2.5-7B on a consumer 6GB GPU - a +33 percentage point improvement over the full-context baseline (0.050 -> 0.382, i.e., 5% -> 38%). With DeepSeek-V4-Flash fact pre-population, single-session recall reaches 0.686-0.714, exceeding the paper's RAG BM25 GPT-4o baseline (0.560) on those categories. Temporal reasoning rises to 0.323 and multi-session synthesis to 0.173, demonstrating that structured semantic pre-population qualitatively changes what lightweight retrieval can achieve. All phases run locally on a consumer laptop with a 6GB GPU.