arxiv: 2605.03675 · v1 · submitted 2026-05-05 · 💻 cs.AI

Recognition: unknown

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

Bronislav Sidik , Lior Rokach

Authors on Pith no claims yet

Pith reviewed 2026-05-07 04:09 UTC · model grok-4.3

classification 💻 cs.AI

keywords tiered memoryautonomous agentsmemory coherenceepisodic storesemantic consolidationretrieval enginelong-running agentsPPO adaptation

0 comments

The pith

MEMTIER's tiered memory architecture improves long-running AI agent accuracy from 5% to 38% on the LongMemEval-S benchmark using only a 6GB consumer GPU.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Long-running autonomous AI agents suffer from a memory coherence problem in which tool-execution success rates degrade 14 percentage points over 72-hour windows because of four compounding failure modes in flat-file memory systems. MEMTIER introduces a tripartite architecture consisting of a structured episodic JSONL store, a five-signal weighted retrieval engine, an attention-attributed cognitive weight update loop, an asynchronous consolidation daemon that promotes episodic facts to a semantic tier, and a PPO-based policy for adapting retrieval weights. On the full 500-question LongMemEval-S benchmark the system reaches 0.382 accuracy and 0.412 F1 with Qwen2.5-7B on a 6GB GPU, a 33-point gain over the full-context baseline of 0.050. With DeepSeek-V4-Flash fact pre-population, single-session recall climbs to 0.686-0.714, exceeding the paper's RAG BM25 GPT-4o baseline of 0.560, while temporal reasoning and multi-session synthesis also improve. A reader would care because the results indicate that structured tiered memory can sustain agent performance over extended operation periods on ordinary local hardware.

Core claim

The central claim is that a tripartite memory architecture—structured episodic JSONL store, five-signal weighted retrieval engine, asynchronous consolidation daemon promoting facts to a semantic tier, and PPO-based weight adaptation—eliminates the compounding failure modes of flat memory systems. This enables Qwen2.5-7B to reach 0.382 accuracy and 0.412 F1 on the full LongMemEval-S benchmark on a 6GB consumer GPU (versus 0.050 for full-context), with recall of 0.686-0.714 when facts are pre-populated and corresponding gains in temporal reasoning (0.323) and multi-session synthesis (0.173).

What carries the argument

The tripartite memory architecture with its episodic JSONL store, five-signal weighted retrieval engine, asynchronous consolidation daemon that promotes episodic facts to a semantic tier, and PPO-based policy framework for adapting retrieval weights.

If this is right

Tool-execution success rates remain stable instead of degrading 14 percentage points over 72-hour operation windows.
Temporal reasoning performance reaches 0.323 and multi-session synthesis reaches 0.173.
Single-session recall reaches 0.686-0.714 with fact pre-population, surpassing the paper's RAG BM25 GPT-4o baseline of 0.560.
All components run locally on a consumer 6GB GPU, removing the need for large context windows or remote compute.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The architecture could be ported to other agent runtimes if the infrastructure-validated components are reproduced.
The gains from combining local tiered retrieval with selective external pre-population point to practical hybrid local-cloud designs for lightweight agents.
Further breakdown of the five retrieval signals would show which cues drive the largest share of the temporal and multi-session improvements.

Load-bearing premise

The observed accuracy and recall lifts are produced by the tripartite architecture, five-signal engine, consolidation daemon, and PPO adaptation rather than by benchmark-specific choices, model selection, or the external DeepSeek pre-population step.

What would settle it

A controlled ablation on the same LongMemEval-S benchmark that removes the consolidation daemon and five-signal retrieval engine while retaining all other variables and measures whether accuracy falls back to the 0.050 full-context baseline level.

Figures

Figures reproduced from arXiv: 2605.03675 by Bronislav Sidik, Lior Rokach.

**Figure 1.** Figure 1: The MEMTIER multi-agent retrieval pipeline. Episodic logs are agent-private; distilled semantic facts are project-shared, enabling cross-agent knowledge transfer while preventing context contamination. 3.1 Phase 1a: Episodic JSONL Store Each agent session writes structured entries to a daily JSONL file at ~/.openclaw/workspace/ memory/episodic/YYYY-MM-DD.jsonl. The entry schema includes: id, timestamp, ses… view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MEMTIER puts forward a concrete tripartite memory system for long-running agents and reports large benchmark lifts on consumer hardware, but the gains are pending camera-ready and the setup leaves room for confounding from pre-population and baseline choices.

read the letter

The paper's core contribution is a tiered memory architecture for the OpenClaw runtime that combines a structured episodic JSONL store, five-signal weighted retrieval, attention-attributed weight updates, an asynchronous consolidation daemon, and PPO policy adaptation. It targets the documented 14-point degradation in tool success over 72-hour runs and tests on the full 500-question LongMemEval-S benchmark. The reported numbers with Qwen2.5-7B on a 6GB GPU show accuracy rising from 0.05 to 0.382 and F1 to 0.412, with further recall gains to 0.686-0.714 when DeepSeek-V4-Flash pre-population is added. Temporal reasoning and multi-session scores also improve. This is new as an integrated package aimed at practical, local deployment rather than isolated RAG tweaks or larger models. The focus on consumer hardware and explicit failure modes in flat memory systems is useful for people actually building agents that need to run for days. The architecture description is detailed enough that an implementer could start from it. The soft spots sit mainly in the experimental claims. The abstract flags performance gains as pending camera-ready and infrastructure as only validated, with no error bars, ablation tables, or full protocol details visible. The strongest recall figures rely on external DeepSeek pre-population while the RAG BM25 GPT-4o baseline does not, and the full-context baseline runs on the same limited GPU where context likely cannot fit. Without isolating runs that hold pre-population and model fixed, it is difficult to attribute the full delta to the five-signal engine or PPO loop. This paper is aimed at researchers and engineers working on memory coherence in autonomous agents. A reader who needs ideas for tiered stores and retrieval weighting on modest hardware will find concrete starting points, even if they must treat the exact numbers as provisional. It deserves a serious referee because the problem is real, the approach is implementable, and the benchmark is public. I would send it to peer review with the clear expectation that the camera-ready version supply ablations, matched baselines, and complete experimental details.

Referee Report

2 major / 2 minor

Summary. The paper introduces MEMTIER, a tripartite memory architecture for long-running autonomous AI agents addressing memory coherence degradation over extended operation. It comprises a structured episodic JSONL store, a five-signal weighted retrieval engine, an attention-attributed cognitive weight update loop, an asynchronous consolidation daemon promoting facts to a semantic tier, and a PPO-based policy framework for adapting retrieval weights. On the full 500-question LongMemEval-S benchmark, it claims Acc=0.382 and F1=0.412 with Qwen2.5-7B on a 6GB consumer GPU (+33pp over full-context baseline of 0.050), with single-session recall of 0.686-0.714 using DeepSeek-V4-Flash pre-population (exceeding RAG BM25 GPT-4o baseline of 0.560), plus gains in temporal reasoning (0.323) and multi-session synthesis (0.173). All components run locally; infrastructure is validated but performance gains are flagged as pending camera-ready.

Significance. If the empirical claims hold after full validation, MEMTIER could meaningfully advance practical memory systems for resource-limited autonomous agents by showing how tiered episodic-to-semantic storage combined with adaptive retrieval and consolidation can mitigate coherence loss. The PPO adaptation and daemon mechanisms target specific failure modes not addressed by flat-file or standard RAG approaches. The consumer-GPU results and benchmark scale add practical relevance, though the pending status and external pre-population step temper the assessed impact.

major comments (2)

[Experimental evaluation] Experimental evaluation section: The headline +33pp accuracy lift (0.050 to 0.382) and recall range 0.686-0.714 are presented as resulting from the tripartite architecture, five-signal engine, consolidation daemon, and PPO loop, yet the high-recall numbers explicitly depend on DeepSeek-V4-Flash fact pre-population while the RAG BM25 GPT-4o baseline (0.560) does not use it; no ablation removing pre-population or applying it uniformly to baselines is described. This directly affects the central claim that the reported deltas are caused by MEMTIER components rather than the external pre-population or hardware-aware baseline degradation on the 6GB Qwen2.5-7B setup.
[Abstract and Evaluation] Abstract and §4 (Evaluation): The manuscript states concrete benchmark numbers (Acc=0.382, F1=0.412, temporal 0.323) while qualifying that 'performance gains [are] pending camera-ready' and 'infrastructure validated only,' with no error bars, full protocol, data exclusion rules, or ablation details provided. This renders the soundness of the 500-question LongMemEval-S results load-bearing for the paper's contribution and requires completion before the claims can be assessed.

minor comments (2)

[Architecture description] The five-signal retrieval engine and PPO policy are described at a high level without explicit equations for signal weighting or the reward formulation; adding these would clarify how the adaptation loop operates.
[Related work] Missing references to prior tiered-memory or long-context agent works (e.g., standard RAG variants or episodic memory papers) beyond the cited LongMemEval-S benchmark.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's insightful comments on our work introducing MEMTIER for addressing memory coherence in long-running autonomous AI agents. The feedback highlights important aspects of the experimental evaluation that require clarification and completion. We respond to each major comment below and commit to the necessary revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Experimental evaluation] Experimental evaluation section: The headline +33pp accuracy lift (0.050 to 0.382) and recall range 0.686-0.714 are presented as resulting from the tripartite architecture, five-signal engine, consolidation daemon, and PPO loop, yet the high-recall numbers explicitly depend on DeepSeek-V4-Flash fact pre-population while the RAG BM25 GPT-4o baseline (0.560) does not use it; no ablation removing pre-population or applying it uniformly to baselines is described. This directly affects the central claim that the reported deltas are caused by MEMTIER components rather than the external pre-population or hardware-aware baseline degradation on the 6GB Qwen2.5-7B setup.

Authors: We clarify that the reported accuracy of 0.382 on the full 500-question LongMemEval-S benchmark is achieved without the DeepSeek-V4-Flash pre-population step. The pre-population is specifically used to evaluate single-session recall, where we report 0.686-0.714. The RAG BM25 GPT-4o baseline of 0.560 is presented for comparison on the same categories without pre-population. We acknowledge the lack of explicit ablations for the pre-population component. In the revised manuscript, we will add ablations that (1) apply the pre-population uniformly across MEMTIER and all baselines, and (2) evaluate MEMTIER without pre-population for the recall metrics. This will help isolate the contributions of the tripartite architecture, five-signal retrieval, consolidation daemon, and PPO adaptation from the pre-population. We will also update the text to clearly distinguish these experimental conditions. revision: yes
Referee: [Abstract and Evaluation] Abstract and §4 (Evaluation): The manuscript states concrete benchmark numbers (Acc=0.382, F1=0.412, temporal 0.323) while qualifying that 'performance gains [are] pending camera-ready' and 'infrastructure validated only,' with no error bars, full protocol, data exclusion rules, or ablation details provided. This renders the soundness of the 500-question LongMemEval-S results load-bearing for the paper's contribution and requires completion before the claims can be assessed.

Authors: We recognize that the manuscript qualifies the performance results as pending full camera-ready validation, with only infrastructure validated at present. To address this, the revised version will include the completed experimental evaluation with error bars for all metrics (Acc, F1, temporal reasoning, multi-session synthesis), a detailed description of the full protocol, data exclusion rules, and comprehensive ablation studies. We are in the process of finalizing these experiments to ensure the claims are supported by rigorous, reproducible results. The abstract and evaluation section will be updated accordingly to reflect the completed validation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical architecture and benchmark results with no derivations or self-referential fitting

full rationale

The paper describes a tripartite memory architecture, five-signal retrieval engine, consolidation daemon, and PPO policy at a high level but presents no equations, first-principles derivations, or fitted parameters. All reported metrics (Acc=0.382, F1=0.412, recall 0.686-0.714) are direct empirical comparisons on the external LongMemEval-S benchmark against baselines (full-context, RAG BM25 GPT-4o). No self-citation chains, uniqueness theorems, or ansatzes are invoked to justify core claims; the manuscript explicitly flags performance gains as pending camera-ready. The derivation chain is therefore self-contained as an engineering description plus benchmark evaluation, with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is an empirical systems paper with no explicit mathematical axioms, free parameters fitted to data, or invented physical entities. The new components are engineering constructs whose correctness is asserted via benchmark results rather than derivation.

pith-pipeline@v0.9.0 · 5575 in / 1570 out tokens · 95177 ms · 2026-05-07T04:09:29.186448+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

Works this paper leans on

14 extracted references · 7 canonical work pages · 5 internal anchors

[1]

arXiv preprint arXiv:2501.08956 , year =

Wu, Di and others , title =. arXiv preprint arXiv:2501.08956 , year =

work page arXiv
[2]

International Conference on Learning Representations (ICLR) , year =

Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan , title =. International Conference on Learning Representations (ICLR) , year =
[3]

MemGPT: Towards LLMs as Operating Systems

Packer, Charles and Fang, Vivian and Patil, Shishir G. and Wooders, Kevin and Gonzalez, Joseph E. , title =. arXiv preprint arXiv:2310.08560 , year =

work page internal anchor Pith review arXiv
[4]

arXiv preprint , year =

Liu, Hao and others , title =. arXiv preprint , year =
[5]

arXiv preprint , year =

Sun, Wei and others , title =. arXiv preprint , year =
[6]

Retrieval-Augmented Generation for Knowledge-Intensive

Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K. Retrieval-Augmented Generation for Knowledge-Intensive. Advances in Neural Information Processing Systems (NeurIPS) , year =
[7]

Proximal Policy Optimization Algorithms

Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg , title =. arXiv preprint arXiv:1707.06347 , year =

work page internal anchor Pith review arXiv
[8]

Evaluating Very Long-Term Conversational Memory of LLM Agents

Maharana, Adyasha and Das, Divyansh and Tulyakov, Sergey and Bansal, Mohit and Dernoncourt, Franck and Fang, Yuwei , title =. arXiv preprint arXiv:2402.17753 , year =

work page internal anchor Pith review arXiv
[9]

arXiv preprint , year =

Anonymous , title =. arXiv preprint , year =
[10]

NeurIPS 2026 Agent Safety Workshop (under review) , year =

Anonymous , title =. NeurIPS 2026 Agent Safety Workshop (under review) , year =

2026
[11]

arXiv preprint arXiv:2412.19437 , year =

work page internal anchor Pith review arXiv
[12]

Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity

Anonymous , title =. arXiv preprint arXiv:2604.09588 , year =

work page internal anchor Pith review Pith/arXiv arXiv
[13]

Proceedings of the Third Text REtrieval Conference (TREC-3) , pages =

Robertson, Stephen and Walker, Steve and Jones, Susan and Hancock-Beaulieu, Micheline and Gatford, Mike , title =. Proceedings of the Third Text REtrieval Conference (TREC-3) , pages =. 1994 , url =

1994
[14]

arXiv preprint arXiv:2106.14807 , year =

Lin, Jimmy , title =. arXiv preprint arXiv:2106.14807 , year =

work page arXiv