Memp: Exploring Agent Procedural Memory

Fei Huang; Huajun Chen; Jialong Wu; Ningyu Zhang; Pengjun Xie; Runnan Fang; Shuofei Qiao; Xiaobin Wang; Yuan Liang

arxiv: 2508.06433 · v4 · submitted 2025-08-08 · 💻 cs.CL · cs.AI· cs.LG· cs.MA

Memp: Exploring Agent Procedural Memory

Runnan Fang , Yuan Liang , Xiaobin Wang , Jialong Wu , Shuofei Qiao , Pengjun Xie , Fei Huang , Huajun Chen

show 1 more author

Ningyu Zhang

This is my paper

Pith reviewed 2026-05-18 23:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LGcs.MA

keywords procedural memoryLLM agentsmemory repositorytrajectory distillationagent learningTravelPlannerALFWorld

0 comments

The pith

Agents with a learnable procedural memory distilled from trajectories achieve higher success rates and efficiency on analogous tasks, and the memory transfers from stronger to weaker models.

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

The paper introduces Memp to equip LLM agents with procedural memory that can be learned from experience, updated over time, and maintained lifelong. It distills past trajectories into fine-grained step-by-step instructions and higher-level script-like abstractions, then tests strategies for building, retrieving, and updating this memory repository under a dynamic regimen that corrects and deprecates content. Experiments on TravelPlanner and ALFWorld show that refining the repository produces steady gains in task success and efficiency. Memory built with a stronger model can be migrated to improve results on a weaker model.

Core claim

Memp distills agent trajectories into both detailed step-by-step instructions and higher-level script-like abstractions to create an evolving procedural memory repository. Strategies for Build, Retrieval, and Update are combined with a dynamic regimen that continuously refines, corrects, and deprecates contents as new experience arrives. On TravelPlanner and ALFWorld, agents using the refined repository reach steadily higher success rates and greater efficiency on analogous tasks. Procedural memory constructed from a stronger model retains value and produces substantial gains when migrated to a weaker model.

What carries the argument

The Memp procedural memory repository that stores distilled instructions and abstractions from trajectories and manages build, retrieval, update, and dynamic deprecation.

If this is right

Refining the memory repository produces steadily higher success rates on analogous tasks.
Task completion becomes more efficient as the memory evolves.
Procedural memory built from a stronger model yields substantial performance gains when migrated to a weaker model.
The dynamic regimen keeps memory contents aligned with accumulating agent experience.

Where Pith is reading between the lines

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

This approach could reduce the need for hand-crafted prompts or static parameters in agent design.
Procedural memory might support knowledge sharing across different agent models and task domains.
The method could be extended to test generalization in longer-horizon or multi-domain settings beyond the current benchmarks.

Load-bearing premise

Distilling trajectories into step-by-step instructions and script-like abstractions produces memory that generalizes to new analogous tasks without harmful biases or outdated procedures.

What would settle it

If agents equipped with the refined memory repository show no gains or lower success rates and efficiency than baseline agents on new analogous tasks in TravelPlanner or ALFWorld, the central claim would not hold.

read the original abstract

Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions, and explore the impact of different strategies for Build, Retrieval, and Update of procedural memory. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and ALFWorld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model can also yield substantial performance gains. Code is available at https://github.com/zjunlp/MemP.

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

Memp adds dual-granularity procedural memory for agents with dynamic updates and shows benchmark gains, but task overlap controls are missing.

read the letter

The main takeaway is that Memp distills agent trajectories into both fine-grained step-by-step instructions and higher-level script-like abstractions, then manages them through explicit build, retrieve, and update steps plus deprecation rules so the memory evolves with new experience. This combination of granularities and the lifelong regimen is the concrete addition over prior memory work in agents. They demonstrate the approach on TravelPlanner and ALFWorld, where refining the repository leads to higher success rates and better efficiency, and they show that memory built on a stronger model still helps when moved to a weaker one. Releasing the code is useful for anyone who wants to try it directly. The results line up with the central claim without obvious circularity or self-referential fitting. On the weaker side, the reported success rates come without error bars, and there are no ablations that isolate the dynamic update or deprecation rules. The stress-test point about task overlap also holds: the paper does not describe explicit partitioning, similarity thresholds, or held-out task sets between the trajectories used to build memory and the evaluation tasks. Without that, some of the steady gains could come from retrieving near-duplicate procedures rather than robust abstraction, which would affect how much weight to give the refinement and cross-model results. This work is for people building LLM agents who need something more structured than static prompts or raw trajectory storage. A reader focused on practical memory mechanisms for agents would find the strategies and benchmark numbers worth looking at. It deserves peer review because the mechanism is clearly described, the experiments use standard benchmarks, and the code is available, even though tighter controls and more analysis would make the generalization claims stronger.

Referee Report

3 major / 2 minor

Summary. The paper introduces Memp, a framework for endowing LLM agents with learnable procedural memory by distilling past trajectories into fine-grained step-by-step instructions and higher-level script-like abstractions. It explores Build, Retrieval, and Update strategies together with a dynamic regimen for continuous correction and deprecation, and reports empirical results on TravelPlanner and ALFWorld showing steadily improving success rates and efficiency as the memory repository is refined, plus positive transfer when memory built by a stronger model is migrated to a weaker one.

Significance. If the generalization claims hold after addressing controls for task overlap, the work offers a concrete, reproducible mechanism for making agent procedural memory updatable and cross-model transferable rather than statically engineered. The public code release at https://github.com/zjunlp/MemP is a clear strength that supports verification of the reported gains on standard benchmarks.

major comments (3)

[§5] §5 (Experimental Setup and Results): The reported success rates on TravelPlanner and ALFWorld lack error bars or statistics from multiple random seeds, so the magnitude and reliability of the steady gains attributed to memory refinement cannot be assessed.
[§4.1 and §5.2] §4.1 (Build strategy) and §5.2 (Evaluation): No similarity thresholds, task-overlap metrics, or explicit held-out partitioning between the trajectories used to populate the memory repository and the evaluation tasks are described. Without these controls, apparent generalization could be driven by retrieval of near-duplicate procedures rather than robust abstractions, directly affecting both the refinement curves and the cross-model migration results.
[§4.3 and §5.3] §4.3 (Dynamic Update) and §5.3 (Ablations): The paper provides no ablation isolating the contribution of the dynamic correction, deprecation, and update rules; therefore the claim that the repository “evolves in lockstep with new experience” rests on an untested component of the central architecture.

minor comments (2)

[§3] Notation for the three memory levels (step-by-step vs. script-like) is introduced informally in §3; a small table or explicit definition would improve clarity when discussing retrieval.
[Figure 2] Figure 2 (memory evolution diagram) would benefit from an additional panel showing an example of a deprecated entry to illustrate the dynamic regimen.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to strengthen the empirical sections as suggested.

read point-by-point responses

Referee: [§5] §5 (Experimental Setup and Results): The reported success rates on TravelPlanner and ALFWorld lack error bars or statistics from multiple random seeds, so the magnitude and reliability of the steady gains attributed to memory refinement cannot be assessed.

Authors: We agree that multiple random seeds and error bars are necessary to assess reliability. In the revised manuscript we will rerun the key experiments on both benchmarks with at least three distinct random seeds and report means together with standard deviations in the tables and figures of §5. revision: yes
Referee: [§4.1 and §5.2] §4.1 (Build strategy) and §5.2 (Evaluation): No similarity thresholds, task-overlap metrics, or explicit held-out partitioning between the trajectories used to populate the memory repository and the evaluation tasks are described. Without these controls, apparent generalization could be driven by retrieval of near-duplicate procedures rather than robust abstractions, directly affecting both the refinement curves and the cross-model migration results.

Authors: We acknowledge the importance of these controls. We will revise §4.1 to explicitly document the similarity thresholds used during retrieval and will add quantitative task-overlap metrics. We will also clarify in §5.2 that evaluation tasks were partitioned to be held-out from the trajectories used to populate the memory repository, thereby supporting the generalization claims. revision: yes
Referee: [§4.3 and §5.3] §4.3 (Dynamic Update) and §5.3 (Ablations): The paper provides no ablation isolating the contribution of the dynamic correction, deprecation, and update rules; therefore the claim that the repository “evolves in lockstep with new experience” rests on an untested component of the central architecture.

Authors: We agree that an isolated ablation would better substantiate the role of the dynamic update rules. In the revised version we will add an ablation in §5.3 that compares the full system against a variant without the correction, deprecation, and update mechanisms, thereby quantifying their contribution to the observed performance gains. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical derivation chain

full rationale

The paper advances an empirical framework for procedural memory in LLM agents via Build/Retrieval/Update strategies that distill trajectories into instructions and abstractions, evaluated on external benchmarks TravelPlanner and ALFWorld. Success-rate gains and cross-model migration results are reported as outcomes of these strategies plus a dynamic update regimen; none of these reduce by construction to fitted parameters, self-definitions, or self-citation chains that tautologically reproduce the same quantities. The central claims rest on observable task performance rather than internal consistency alone, rendering the reported chain self-contained against the stated benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach relies on the unstated premise that past trajectories contain reusable procedural knowledge that can be reliably extracted and that the chosen update rules do not degrade performance; no explicit free parameters or invented entities are named in the abstract, but the dynamic regimen implicitly introduces thresholds for correction and deprecation.

axioms (1)

domain assumption Past agent trajectories contain extractable, reusable procedural knowledge that generalizes to new analogous tasks.
Invoked when the paper states that distilling trajectories improves performance on TravelPlanner and ALFWorld.

pith-pipeline@v0.9.0 · 5731 in / 1335 out tokens · 30597 ms · 2026-05-18T23:58:56.884786+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions, and explore the impact of different strategies for Build, Retrieval, and Update of procedural memory.
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Empirical evaluation on TravelPlanner and ALFWorld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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