arxiv: 2601.01885 · v2 · submitted 2026-01-05 · 💻 cs.CL

Recognition: no theorem link

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents

Yi Yu , Liuyi Yao , Yuexiang Xie , Qingquan Tan , Jiaqi Feng , Yaliang Li , Libing Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-16 18:29 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM agentsmemory managementlong-term memoryshort-term memoryreinforcement learningtool useagent policylong-horizon tasks

0 comments

The pith

LLM agents can learn to manage long-term and short-term memory as a single unified policy by treating operations like store and retrieve as tool actions.

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

The paper proposes a framework where LLM agents decide on their own when to store, retrieve, update, summarize, or discard information instead of relying on fixed rules or separate controllers. Current methods split long-term memory from short-term memory and use heuristics, which restricts adaptation during long tasks. By making memory operations available as actions the agent can choose, the model trains end-to-end to balance both types of memory. A three-stage progressive reinforcement learning process with step-wise rewards handles the sparse signals that come from memory decisions. Experiments across five benchmarks and multiple model sizes show gains in task success, memory quality, and context efficiency.

Core claim

Agentic Memory integrates long-term and short-term memory directly into the agent's policy by exposing store, retrieve, update, summarize, and discard as tool actions. The agent therefore decides autonomously what and when to remember. Training uses a three-stage progressive reinforcement learning schedule together with step-wise GRPO to manage the discontinuous rewards created by memory operations.

What carries the argument

Memory operations exposed as tool-based actions inside the agent's policy, trained end-to-end with three-stage progressive reinforcement learning.

If this is right

Higher task success rates on long-horizon benchmarks compared with separate-memory baselines
Higher-quality long-term memory summaries stored during episodes
Lower token usage in context windows while maintaining performance
Consistent improvements when the same method is applied to different LLM backbones
Removal of the need for hand-designed heuristics or auxiliary memory controllers

Where Pith is reading between the lines

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

The same tool-action approach could be applied to other internal agent states such as planning or belief tracking
Longer task horizons might become feasible if memory quality continues to improve with scale
Real-world agent deployments could reduce engineering effort by replacing multiple memory modules with a single learned policy
The progressive training schedule may transfer to other sparse-reward agent skills beyond memory

Load-bearing premise

Treating memory operations as tool actions and training them with progressive reinforcement learning is enough for the model to discover effective unified management without external rules.

What would settle it

A controlled comparison in which a baseline that keeps separate heuristic memory modules matches or exceeds AgeMem performance on the same five long-horizon benchmarks would falsify the claim that unified tool-action training is necessary.

Figures

Figures reproduced from arXiv: 2601.01885 by Jiaqi Feng, Libing Wu, Liuyi Yao, Qingquan Tan, Yaliang Li, Yi Yu, Yuexiang Xie.

**Figure 2.** Figure 2: Memory Quality scores for different methods [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Average prompt token counts under different [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Ablation study on LTM, STM, and RL components (Qwen2.5-7B-Instruct). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Training convergence curves on Qwen2.5-7B [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Short-term memory (STM) management tools for conversational context management. These tools enable [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Long-term memory (LTM) management tools for persistent storage. These tools provide add, update, and [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Main training procedure of AgeMem. For clarity, we split the algorithm into two parts: the rollout phase [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Ablation study results for Qwen3-4B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗

**Figure 10.** Figure 10: Training convergence curves on Qwen3-4B [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

read the original abstract

Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. In this paper, we propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy. AgeMem exposes memory operations as tool-based actions, enabling the LLM agent to autonomously decide what and when to store, retrieve, update, summarize, or discard information. To train such unified behaviors, we propose a three-stage progressive reinforcement learning strategy and design a step-wise GRPO to address sparse and discontinuous rewards induced by memory operations. Experiments on five long-horizon benchmarks demonstrate that AgeMem consistently outperforms strong memory-augmented baselines across multiple LLM backbones, achieving improved task performance, higher-quality long-term memory, and more efficient context usage.

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

AgeMem folds LTM and STM into the agent's policy by exposing memory ops as tool actions and training them end-to-end with three-stage progressive RL plus GRPO.

read the letter

The main point is that this paper treats memory management as something the LLM agent learns to do on its own instead of relying on separate heuristics or extra controllers. Memory operations become tool actions the model can pick, and a three-stage progressive RL schedule with step-wise GRPO handles the sparse rewards that come from those choices. That setup is the clearest departure from prior separate LTM/STM pipelines mentioned in the abstract and stress-test note. The experiments on five long-horizon benchmarks show consistent gains over strong baselines across multiple LLM backbones, with better task success, higher-quality long-term memory, and more efficient context use. The stress-test confirms the results trace to the described training procedure without hidden dependencies or inconsistencies, which is the strongest part of the evidence. A soft spot is that the paper could still benefit from fuller ablations on how much each progressive stage contributes versus simpler RL schedules. Reproducibility details on exact baselines and hyperparameter choices would also help readers verify the gains are not sensitive to small implementation choices. This work is for researchers building LLM agents that need to run over many steps without context blow-up. Anyone focused on practical RL for agent memory or long-horizon planning will get concrete method details and benchmark numbers they can build on. It deserves a serious referee because the core formulation is executable, the claims line up with the reported results, and the approach directly targets a known bottleneck without obvious internal problems.

Referee Report

0 major / 3 minor

Summary. The manuscript presents Agentic Memory (AgeMem), a unified framework for LLM agents that integrates long-term memory (LTM) and short-term memory (STM) management directly into the agent's policy by exposing operations such as store, retrieve, update, summarize, and discard as tool-based actions. Training uses a three-stage progressive reinforcement learning strategy with a step-wise GRPO objective to address sparse rewards from memory actions. Experiments on five long-horizon benchmarks show consistent outperformance over memory-augmented baselines across multiple LLM backbones, with gains in task success, long-term memory quality, and context efficiency.

Significance. If the reported gains hold under scrutiny, the work provides a meaningful advance by replacing heuristic or auxiliary-controller memory systems with an end-to-end learned policy, enabling more adaptive memory decisions in long-horizon agent tasks. The combination of tool-action formulation and progressive RL offers a concrete path toward autonomous unified memory management that could improve both performance and efficiency in LLM agents.

minor comments (3)

[Abstract] Abstract: the phrase 'strong memory-augmented baselines' is used without naming the primary comparators; adding the top two or three (e.g., by name and citation) would improve immediate readability.
[§3.2] §3.2: the step-wise GRPO formulation is described at a high level; a short pseudocode block or explicit reward decomposition would aid reproducibility for readers implementing the method.
[Results] Table 2 or equivalent results section: report standard errors or p-values alongside mean scores to substantiate the 'consistent outperformance' claim.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of AgeMem and the recommendation for minor revision. The summary correctly identifies the core contribution: exposing memory operations as tool actions within the agent's policy and training them end-to-end with three-stage progressive RL and step-wise GRPO.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes Agentic Memory (AgeMem) as a framework that exposes memory operations as tool actions within the agent's policy and trains unified LTM/STM behaviors via a three-stage progressive RL strategy using step-wise GRPO. Central claims of outperformance are grounded in experimental comparisons on five long-horizon benchmarks across multiple LLM backbones, with no mathematical derivations, equations, or parameter-fitting steps that reduce to inputs by construction. No self-citations form a load-bearing chain for uniqueness theorems or ansatzes, and the training procedure is presented with sufficient detail to trace reported gains to the described method rather than hidden heuristics. The derivation chain is therefore self-contained and empirically validated.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the framework relies on standard LLM and RL components whose details are not provided.

pith-pipeline@v0.9.0 · 5497 in / 1066 out tokens · 43563 ms · 2026-05-16T18:29:26.602479+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · cited by 11 Pith papers

[1]

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others

Meminsight: Autonomous memory augmenta- tion for llm agents.arXiv preprint arXiv:2503.21760. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300. Mohit Shri...

work page arXiv 2024
[2]

all”: Summarize all non-system messages. •“N

Llm-based multi-agent reinforcement learn- ing: Current and future directions.arXiv preprint arXiv:2405.11106. Hongcheng Wang, Yinuo Huang, Sukai Wang, Guanghui Ren, and Hao Dong. 2025a. Grpo-ma: Multi-answer generation in grpo for stable and ef- ficient chain-of-thought training.arXiv preprint arXiv:2509.24494. Ruoyao Wang, Peter Jansen, Marc-Alexandre C...

work page arXiv 2022
[3]

Read the provided conversation rounds carefully

work page
[4]

Identify the main topics, actions, results, and open issues

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[5]

Write a clear, factual summary in natural language

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[6]

Input: - Conversation content: [CONVERSATION_TEXT] Output: - A concise yet comprehensive summary of the above conversation span

Do NOT include greetings, filler text, or redundant phrasing. Input: - Conversation content: [CONVERSATION_TEXT] Output: - A concise yet comprehensive summary of the above conversation span. Let’s start the conversation summarization. The agent learns to invoke summarization proac- tively before context overflow occurs, balancing information preservation ...

work page
[7]

Inside it, explain your reasoning, plan your next step, and decide whether you need to call a tool or provide a final answer

**Think & Plan** Always start with a <think>...</think> block. Inside it, explain your reasoning, plan your next step, and decide whether you need to call a tool or provide a final answer

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[8]

name" and

**Tool Calls** If you decide to use one or more tools, follow your <think> block with a <tool_call >...</tool_call> block. - You may call **one or multiple tools** in a single step. - List multiple tool calls as elements of a JSON array. - Each tool call must include "name" and " arguments". - Example: <tool_call>[{{"name": "Retrieve_memory", " arguments"...

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[9]

**Final Answer** When you no longer need tools and are ready to present your final output, follow your last <think> block with an <answer>...</ answer> block containing the full response

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[10]

<tool_call>

**Mutual Exclusivity Rule** After **each <think> block**, you must choose exactly **one** of the following: - a "<tool_call>" block (if you need tools), **or** - an "<answer>" block (if you are ready to respond). You must **never** include both "<tool_call>" and "<answer>" immediately after the same "<think>" block

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[11]

<think>" ->

**Iterative Solving** You may repeat this sequence as needed: "<think>" -> "<tool_call>" -> "<think>" -> "< tool_call>" ... -> "<think>" -> "<answer>" until the problem is completely solved. ## Response Format (Strict) Your full output must follow these rules: - Every reasoning step must appear inside <think > tags. - Every tool usage must appear inside o...

work page 2020
[12]

Are all expected facts covered by the predictions?

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[13]

Are the predicted facts actually relevant to answering the question?

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[14]

TN” is the token number, and “TC

Are there any irrelevant facts in the predictions? Score on a scale of 0.0 to 1.0: - 1.0: Perfect match - all expected facts are correctly identified, no irrelevant facts - 0.8-0.9: Mostly correct with minor omissions or one irrelevant fact - 0.6-0.7: Partially correct - some relevant facts identified but missing important ones - 0.4-0.5: Some correct ele...

work page arXiv 2025