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arxiv: 2606.28434 · v1 · pith:SC3VQQ2Znew · submitted 2026-06-26 · 💻 cs.SE · cs.AI

SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents

Shuzheng Gao , Wenhao Zeng , Zhaojian Yu , Jianqiao Wangni , Chaozheng Wang , Kai Cai , Shilin He , Michael R. Lyu This is my paper

Pith reviewed 2026-06-30 01:33 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords adaptive memory managementlong-horizon agentssoftware engineering agentscontext compressionreinforcement learningSWE-Benchtrajectory optimization
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The pith

Agents trained with SWE-MeM learn to decide their own memory compression during long software engineering tasks.

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

The paper introduces a training framework called SWE-MeM that equips software engineering agents with a flexible memory tool. Agents use this tool to choose when, what, and how much to compress from their interaction history based on current state, progress, and remaining context. Training relies on synthesized proactive memory-management trajectories paired with Memory-aware GRPO, which splits trajectories and assigns credit at the step level to optimize both compression choices and task resolution together. On the SWE-Bench Verified benchmark this produces resolve rates of 43.4 percent with a 4B model and 60.2 percent with a 30B model while using fewer tokens than prior static or rigid compression baselines.

Core claim

SWE-MeM provides agents a flexible memory tool and trains them on synthesized proactive trajectories with Memory-aware GRPO so that compression decisions become part of the agent's own policy; the joint optimization of memory management and issue resolution through memory-aware trajectory splitting and step-level credit assignment yields higher resolve rates and lower token consumption than existing memory baselines on SWE-Bench Verified.

What carries the argument

A flexible memory tool that lets the agent decide compression timing, content, and granularity on demand, trained end-to-end with Memory-aware GRPO that performs memory-aware trajectory splitting and step-level credit assignment.

If this is right

  • Agents can exceed the performance of static compression workflows while consuming fewer tokens overall.
  • Memory management becomes an integrated part of the agent's policy rather than an external post-processing step.
  • The same training recipe scales across model sizes from 4B to 30B parameters.
  • Joint optimization of memory actions and task success improves both metrics simultaneously.

Where Pith is reading between the lines

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

  • The same adaptive compression approach could apply to other long-context agent domains such as multi-step planning or extended dialogue.
  • Lower token budgets per task could translate directly into reduced inference cost when agents are deployed at scale.
  • If agents can generate their own high-quality memory trajectories, the method might support iterative self-improvement without new human data.

Load-bearing premise

Synthesized proactive memory-management trajectories used in training are representative enough of real agent trajectories to produce effective compression decisions at test time.

What would settle it

Measure whether the trained agent's compression decisions on held-out long-horizon tasks produce the same resolve-rate gains when the test trajectories differ substantially from the synthesized training set.

read the original abstract

Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods typically rely on static compression workflows or impose rigid constraints on compression timing and granularity. Moreover, these approaches fail to jointly optimize memory management and issue resolution capabilities to improve performance while reducing token usage. We present SWE-MeM, a training framework for proactive and on-demand memory management in software engineering agents. SWE-MeM provides a flexible memory tool that lets agents decide when, what, and how to compress based on trajectory state, task progress, and remaining context budget. We train agents with synthesized proactive memory-management trajectories and Memory-aware GRPO, which jointly optimizes memory management and issue resolution through memory-aware trajectory splitting and step-level credit assignment. On SWE-Bench Verified, SWE-MeM achieves 43.4% and 60.2% resolve rate with 4B and 30B models, respectively, outperforming existing memory management baselines in both performance and efficiency.

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 / 2 minor

Summary. The manuscript introduces SWE-MeM, a training framework for long-horizon software engineering agents that enables proactive, on-demand memory management via a flexible memory tool. Agents are trained on synthesized proactive memory-management trajectories using Memory-aware GRPO, which performs memory-aware trajectory splitting and step-level credit assignment to jointly optimize compression decisions and issue resolution. On SWE-Bench Verified the method reports resolve rates of 43.4% (4B model) and 60.2% (30B model), outperforming prior memory-management baselines in both task success and token efficiency.

Significance. If the central empirical claims are supported by the experiments, the work would demonstrate that learned adaptive memory policies can simultaneously raise resolve rates and lower token consumption in long-horizon coding agents, addressing a practical bottleneck that static or rigidly scheduled compression methods have not resolved.

major comments (2)
  1. [Abstract and §3 (Training Procedure)] The headline performance numbers rest on the assumption that synthesized proactive memory-management trajectories produce state distributions, trigger timings, and compression granularities sufficiently close to those arising in real agent rollouts. No quantitative validation (e.g., distributional distances, trigger-condition histograms, or retained-context statistics) is supplied to support this match, which is load-bearing for the transferability of the learned policy.
  2. [§3 (Memory-aware GRPO)] Memory-aware GRPO is described as jointly optimizing memory actions and resolution via trajectory splitting and step-level credit assignment, yet the precise form of the memory-aware reward and the mechanism that prevents degenerate policies (e.g., always compressing to the minimum budget) are not stated explicitly enough to verify correctness of the credit assignment.
minor comments (2)
  1. The abstract should report the exact model sizes and token budgets used by the strongest baselines so that the efficiency claims can be directly compared.
  2. [§2] Notation for the memory tool interface (when/what/how decisions) is introduced in the abstract but not formalized until later; a short table or diagram in §2 would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract and §3 (Training Procedure)] The headline performance numbers rest on the assumption that synthesized proactive memory-management trajectories produce state distributions, trigger timings, and compression granularities sufficiently close to those arising in real agent rollouts. No quantitative validation (e.g., distributional distances, trigger-condition histograms, or retained-context statistics) is supplied to support this match, which is load-bearing for the transferability of the learned policy.

    Authors: We acknowledge that the manuscript does not include quantitative comparisons (such as distributional distances or histograms) between the synthesized trajectories and real agent rollouts. The synthesis procedure was designed to mirror realistic trigger conditions and compression decisions based on task state and budget, and the strong empirical results on SWE-Bench Verified provide indirect support for transfer. However, we agree that explicit validation would strengthen the claims. We will add retained-context statistics, trigger-condition histograms, and a brief distributional comparison in the revised §3. revision: yes

  2. Referee: [§3 (Memory-aware GRPO)] Memory-aware GRPO is described as jointly optimizing memory actions and resolution via trajectory splitting and step-level credit assignment, yet the precise form of the memory-aware reward and the mechanism that prevents degenerate policies (e.g., always compressing to the minimum budget) are not stated explicitly enough to verify correctness of the credit assignment.

    Authors: We will clarify the exact formulation. The memory-aware reward is defined as the sum of a binary resolution indicator and a normalized token-efficiency term, with trajectory splitting performed at memory-action boundaries to enable step-level advantage estimation. Degenerate policies are discouraged by an entropy bonus on memory decisions and a minimum-context baseline that penalizes over-compression when task-relevant information remains. We will expand §3 with the full reward equation and the explicit anti-degeneracy terms. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper describes an empirical training framework (synthesized trajectories + Memory-aware GRPO) whose central claims are resolve rates on the external SWE-Bench Verified benchmark. No equations, self-definitional relations, fitted-input-as-prediction steps, or load-bearing self-citations appear in the supplied text. Performance numbers are measured against an independent test set rather than being forced by construction from the training synthesis procedure itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that the benchmark results generalize beyond the reported models and dataset.

pith-pipeline@v0.9.1-grok · 5726 in / 1163 out tokens · 26274 ms · 2026-06-30T01:33:57.018081+00:00 · methodology

discussion (0)

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Reference graph

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    2000

Showing first 80 references.