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arxiv: 2603.29493 · v4 · submitted 2026-03-31 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

MemFactory: Unified Inference & Training Framework for Agent Memory

Ziliang Guo , Ziheng Li , Bo Tang , Feiyu Xiong , Zhiyu Li
Authors on Pith no claims yet

Pith reviewed 2026-05-13 23:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords memory-augmented agentsLLM frameworksreinforcement learningGRPOMemAgentunified infrastructurememory operationsagent training
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The pith

MemFactory provides a unified modular framework that streamlines the training and inference of memory-augmented agents.

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

The paper introduces MemFactory as a single infrastructure for building, training, and running memory-augmented large language models. It breaks memory operations into atomic reusable parts that combine in a Lego-style manner to form custom agents. Native integration of Group Relative Policy Optimization lets the system refine memory policies using multi-dimensional rewards from the environment. Validation on the MemAgent architecture shows consistent gains across evaluation sets, reaching relative improvements of up to 14.8 percent over base models. The design lowers the effort required to test new memory strategies and supports existing paradigms such as Memory-R1 and RMM.

Core claim

MemFactory is the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. It abstracts the memory lifecycle into atomic, plug-and-play components that enable a Lego-like construction of custom agents. The framework natively integrates Group Relative Policy Optimization to fine-tune internal memory management policies driven by multi-dimensional environmental rewards and provides out-of-the-box support for Memory-R1, RMM, and MemAgent. Empirical tests on the open-source MemAgent architecture with its public training and evaluation data produce average performance gains, with relative improvements reaching 14.8 percent.

What carries the argument

The Lego-like abstraction of memory operations into atomic plug-and-play components together with native Group Relative Policy Optimization for policy fine-tuning.

Load-bearing premise

The modular memory components and GRPO-driven optimization will transfer effectively to agent architectures and tasks beyond the single MemAgent validation case.

What would settle it

Applying the same MemFactory pipeline to a different memory-augmented agent architecture and finding no measurable performance gain over its base model on comparable tasks.

read the original abstract

Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task-specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. Inspired by the success of unified fine-tuning frameworks like LLaMA-Factory, MemFactory abstracts the memory lifecycle into atomic, plug-and-play components, enabling researchers to seamlessly construct custom memory agents via a "Lego-like" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies driven by multi-dimensional environmental rewards. MemFactory provides out-of-the-box support for recent cutting-edge paradigms, including Memory-R1, RMM, and MemAgent. We empirically validate MemFactory on the open-source MemAgent architecture using its publicly available training and evaluation data. Across the evaluation sets, MemFactory improves performance over the corresponding base models on average, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy-to-use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory-driven AI agents.

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.

Referee Report

2 major / 0 minor

Summary. The paper introduces MemFactory as the first unified, highly modular training and inference framework for memory-augmented LLMs. It abstracts memory lifecycle operations (extraction, update, retrieval) into atomic plug-and-play components enabling Lego-like construction of custom agents, natively integrates GRPO for multi-dimensional reward-driven policy optimization, provides out-of-the-box support for Memory-R1, RMM, and MemAgent, and reports average performance improvements with relative gains up to 14.8% when validated on the open-source MemAgent architecture using its public training and evaluation data.

Significance. If the modularity and GRPO integration prove transferable, MemFactory could standardize infrastructure for memory-augmented agents in a manner analogous to LLaMA-Factory for fine-tuning, lowering barriers to RL-based memory policy research and enabling reproducible experimentation across paradigms. The framework's emphasis on atomic components and public-data validation is a positive step toward extensibility, though broader impact hinges on demonstrating that the abstraction does not require architecture-specific re-engineering.

major comments (2)
  1. [Abstract] Abstract: The central claim that MemFactory supplies out-of-the-box support for Memory-R1, RMM, and MemAgent via a Lego-like abstraction is load-bearing for the 'unified framework' assertion, yet the manuscript reports empirical results and implementation details exclusively for the MemAgent architecture; no component counts, custom hooks, or performance numbers are provided for the other two paradigms, leaving cross-paradigm transfer unverified.
  2. [Abstract] Abstract: The reported relative gains of up to 14.8% on MemAgent evaluations are presented without reference to specific baselines, statistical significance tests, data-split protocols, or ablation controls isolating the contribution of GRPO versus the underlying memory components, which weakens the empirical grounding of the framework's advantages.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point-by-point below, indicating where revisions will be made to improve clarity and empirical grounding.

read point-by-point responses

  1. Referee: The central claim that MemFactory supplies out-of-the-box support for Memory-R1, RMM, and MemAgent via a Lego-like abstraction is load-bearing for the 'unified framework' assertion, yet the manuscript reports empirical results and implementation details exclusively for the MemAgent architecture; no component counts, custom hooks, or performance numbers are provided for the other two paradigms, leaving cross-paradigm transfer unverified.

    Authors: The MemFactory abstraction is intentionally paradigm-agnostic, with atomic components for extraction, update, and retrieval designed to enable Lego-like construction across Memory-R1, RMM, and MemAgent without requiring architecture-specific re-engineering. We validate empirically only on MemAgent because it is the sole paradigm among the three with fully open-source code and public training/evaluation data. In revision we will add an explicit component-mapping table and example hook configurations for Memory-R1 and RMM to substantiate the out-of-the-box claim. Full performance numbers for those paradigms cannot be supplied without new experiments. revision: partial

  2. Referee: The reported relative gains of up to 14.8% on MemAgent evaluations are presented without reference to specific baselines, statistical significance tests, data-split protocols, or ablation controls isolating the contribution of GRPO versus the underlying memory components, which weakens the empirical grounding of the framework's advantages.

    Authors: We agree that these details are necessary. The 14.8% figure represents the largest relative improvement versus the base MemAgent model (without GRPO) across the public evaluation sets. In the revised manuscript we will expand both the abstract and the experimental section to name the exact baselines, report statistical significance (paired t-tests), describe the data splits, and include ablations that isolate GRPO's contribution from the memory components. revision: yes

standing simulated objections not resolved
  • Empirical performance numbers, component counts, and custom hooks for Memory-R1 and RMM, as no such experiments were conducted in the original study.

Circularity Check

0 steps flagged

No circularity: framework description with external public-data validation

full rationale

The manuscript presents MemFactory as a modular abstraction layer for memory operations and GRPO integration, then reports average performance gains (up to 14.8%) on the publicly released MemAgent training/evaluation sets. No equations, parameter-fitting steps, or derivation chains appear in the provided text. The empirical results are therefore not constructed from self-defined quantities or self-citations; they are direct measurements against an external benchmark. Claims of Lego-like modularity and support for Memory-R1/RMM are architectural assertions rather than mathematical reductions, so no load-bearing circularity is present.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that memory operations can be usefully decomposed into independent atomic components and that GRPO can be applied directly to internal memory policies without additional architectural changes.

axioms (1)
  • domain assumption Memory lifecycle operations can be abstracted into atomic plug-and-play components without loss of necessary functionality
    Invoked in the description of the Lego-like architecture.

pith-pipeline@v0.9.0 · 5575 in / 1165 out tokens · 28338 ms · 2026-05-13T23:58:31.147572+00:00 · methodology

discussion (0)

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Reference graph

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