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

2026-05-05

AI Summary

This study addresses the degradation of memory coherence in long-running autonomous AI agents caused by flat memory architectures, which leads to a 14-percentage-point drop in tool execution success within 72 hours. To mitigate this, the authors propose a novel three-tier memory architecture that integrates structured episodic memory, a five-signal weighted retrieval mechanism, attention-driven dynamic updating of cognitive weights, an asynchronous semantic consolidation daemon, and a PPO-based adaptive retrieval strategy. This approach achieves efficient long-term memory management on lightweight devices for the first time, attaining a 38.2% accuracy on the LongMemEval-S benchmark—33 percentage points higher than the full-context baseline—and session-wise recall rates of 68.6%–71.4%, substantially outperforming the BM25-RAG baseline using GPT-4o.

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.

Problem

• memory coherence
• long-running AI agents
• retrieval bottleneck
• flat-file memory systems
• autonomous agents

Innovation

• tiered memory architecture
• weighted retrieval engine
• attention-attributed cognitive update
• asynchronous memory consolidation
• PPO-based retrieval policy