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arxiv: 2605.20833 · v1 · pith:ICMJAEEWnew · submitted 2026-05-20 · 💻 cs.CL

MemGym: a Long-Horizon Memory Environment for LLM Agents

Wujiang Xu , Yu Wang , Kai Mei , Kaiqu Liang , Zhenting Wang , Mingyu Jin , Han Zhang , Shi-Xiong Zhang
show 3 more authors
Wenyue Hua Sambit Sahu Dimitris N. Metaxas
This is my paper

Pith reviewed 2026-05-21 04:55 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM agentsmemory benchmarkslong-horizon tasksagentic memorymemory evaluationtool-use agentscoding agents
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The pith

MemGym isolates memory performance in LLM agents by decoupling it from reasoning, retrieval, and tool-use abilities.

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

The paper presents MemGym as a benchmark that evaluates memory in long-horizon LLM agent tasks across realistic scenarios such as coding, research search, tool-use dialogue, and computer use. It unifies existing agent environments under a single interface and generates synthetic pipelines for coding and deep-research tracks that control length and verify alignment at each stage. The core innovation is the use of memory-isolated scores, which rank memory strategies without interference from other agent skills. This design aims to produce memory systems that transfer more effectively to practical agent execution than those tested in prior chat-focused benchmarks. A lightweight reward model is also introduced to enable scalable evaluation on complex coding environments without full rollouts.

Core claim

MemGym unifies agent gyms and memory-grounded pipelines behind one memory-reasoning interface across five tracks in four regimes, and reports memory-isolated scores that separate memory performance from reasoning, retrieval, and tool-use so that memory strategies can be ranked without those confounders.

What carries the argument

Memory-isolated scoring that decouples memory performance from reasoning, retrieval, and tool-use ability across unified agent regimes.

If this is right

  • Memory strategies can be compared and improved independently of other agent capabilities.
  • Synthetic data generation enables controllable and verifiable evaluation pipelines for coding and research tasks.
  • Lightweight reward models can substitute for expensive full-environment rollouts in coding benchmarks.
  • Unified interfaces across regimes support consistent memory evaluation in tool-use, search, coding, and computer-use settings.

Where Pith is reading between the lines

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

  • The isolated scoring approach could highlight memory architectures that generalize across agent domains.
  • Length-controllable pipelines might allow systematic study of how memory demands scale with task horizon.
  • Adoption could shift development focus toward memory mechanisms that survive without perfect reasoning or retrieval.

Load-bearing premise

The synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios.

What would settle it

If rankings of memory strategies produced by memory-isolated scores on MemGym fail to predict which strategies yield higher task success rates when tested in full agent rollouts, the decoupling claim would not hold.

Figures

Figures reproduced from arXiv: 2605.20833 by Dimitris N. Metaxas, Han Zhang, Kai Mei, Kaiqu Liang, Mingyu Jin, Sambit Sahu, Shi-Xiong Zhang, Wenyue Hua, Wujiang Xu, Yu Wang, Zhenting Wang.

Figure 1
Figure 1. Figure 1: MEMGYM unifies five evaluation tracks across four agentic regimes (tool-use dialogue, multi-turn search, coding, computer use) behind a shared interface that separates memory from reasoning and supports memory-isolated scoring with explicit memory rewards. pretraining. (iii) Evaluation cost: A single SWE-Gym rollout requires Docker infrastructure and tens of execution steps, placing systematic memory desig… view at source ↗
Figure 2
Figure 2. Figure 2: MEMGYM architecture. Five environments share a memory module that wraps the prompt to the policy LLM, so the same strategy runs on any environment unchanged. Trajectories feed a replay-augmentation pipeline producing SAFE/HARMFUL labels for MEMRM; MEMGYM-DR and MEMGYM-CODEQA come from in-house pipelines (Section 3.4). into the same per-step contract described in Section 3.2; the two construction pipelines … view at source ↗
Figure 3
Figure 3. Figure 3: Memory strategies on the two synthetic-memory benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: MEMRM training dynamics on SWE-Gym compression events (Qwen3-1.7B-Base + QLoRA, 32K context, 600 steps, 8×A100-40GB). release this held-out split as part of the MEMGYM artifacts so other groups can evaluate their own gates against the same examples. MEMRM also shows partial generalization beyond the training distribution along two axes. Within the coding domain, the gate transfers to memory strategies that… view at source ↗
read the original abstract

Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-turn chat scenarios, overlooking the dynamic memory formation that occurs during extended agent execution. Consequently, the memory systems they produce transfer poorly to realistic agentic environments, such as coding and web navigation. We present MemGym, a benchmark for agentic memory that unifies existing agent gyms and in-house memory-grounded pipelines behind one memory-reasoning interface. MemGym spans five evaluation tracks grouped into four agentic regimes: tool-use dialogue (tau2-bench), multi-turn deep-research search (MEMGYM-DR), coding (SWE-Gym and MEMGYM-CODEQA), and computer use (WebArena-Infinity). MemGym reports memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use ability, so memory strategies can be ranked without those confounders. Our synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios. To make evaluation on coding environments academically tractable, we train MemRM, a lightweight reward model (Qwen3-1.7B fine-tuned with QLoRA) that scores compression quality as a fast scalar read in place of full Docker rollouts.

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

1 major / 1 minor

Summary. The manuscript introduces MemGym, a unified benchmark for long-horizon memory in LLM agents spanning tool-use dialogue (tau2-bench), multi-turn deep-research (MEMGYM-DR), coding (SWE-Gym and MEMGYM-CODEQA), and computer use (WebArena-Infinity). It claims to deliver memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use confounders, enabled by length-controllable synthetic pipelines and, for coding tracks, a lightweight MemRM reward model (Qwen3-1.7B with QLoRA) that replaces full Docker rollouts with scalar compression-quality scores.

Significance. If the claimed decoupling holds and MemRM correlates with downstream agent success, MemGym would provide a valuable, standardized platform for ranking memory strategies in realistic agentic settings without the usual confounds. The length-controllable, ablation-verified synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR represent a concrete strength for controlled, reproducible evaluation.

major comments (1)
  1. [MemRM training and evaluation section] The memory-isolation claim for the coding tracks (SWE-Gym and MEMGYM-CODEQA) is load-bearing and rests on MemRM serving as a faithful proxy for full long-horizon execution outcomes. The manuscript must report explicit validation metrics (e.g., Pearson correlation or rank agreement) between MemRM scores and actual agent success metrics such as bug reproduction or task completion after many steps; if MemRM was trained primarily on surface-level compression features rather than downstream execution labels, the reported isolation for these regimes is incomplete.
minor comments (1)
  1. [Abstract] The abstract states that the synthetic pipelines are 'ablation-verified at every stage' without naming the ablations or pointing to the relevant results table or figure; a one-sentence example or cross-reference would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We are grateful to the referee for their insightful review of our paper on MemGym. We address the major comment regarding the validation of MemRM in detail below.

read point-by-point responses

  1. Referee: [MemRM training and evaluation section] The memory-isolation claim for the coding tracks (SWE-Gym and MEMGYM-CODEQA) is load-bearing and rests on MemRM serving as a faithful proxy for full long-horizon execution outcomes. The manuscript must report explicit validation metrics (e.g., Pearson correlation or rank agreement) between MemRM scores and actual agent success metrics such as bug reproduction or task completion after many steps; if MemRM was trained primarily on surface-level compression features rather than downstream execution labels, the reported isolation for these regimes is incomplete.

    Authors: We concur that demonstrating the correlation between MemRM scores and actual agent success metrics is crucial for validating the memory-isolation claims in the coding tracks. The manuscript positions MemRM as a lightweight model trained to assess compression quality, thereby approximating the utility of the memory for long-horizon coding tasks without requiring resource-intensive Docker executions. Nevertheless, to more rigorously support this proxy, we will augment the revised manuscript with explicit validation results, including Pearson correlation and rank correlation metrics between MemRM predictions and ground-truth task completion outcomes from full rollouts on a held-out set. We will also provide details on the training labels to clarify that they are derived from compression quality assessments aligned with execution success. This revision will be incorporated as additional tables and text in the MemRM section. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark design and proxy model are independent methodological choices

full rationale

The paper introduces MemGym as a new benchmark unifying agent gyms and synthetic pipelines, with memory-isolated scores achieved through explicit track design that separates memory from reasoning/retrieval/tool-use. No equations, derivations, or self-referential definitions appear. MemRM is presented as a trained QLoRA proxy for tractable Docker-free evaluation on coding tracks, not as a fitted parameter renamed as a prediction or a self-definitional loop. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are described. The construction remains self-contained against external benchmarks and does not reduce any central claim to its own inputs by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The paper rests on the domain assumption that prior benchmarks overlook dynamic memory formation during extended execution; it introduces the new benchmark and reward model entities without external falsifiable evidence supplied in the abstract.

axioms (1)
  • domain assumption Existing memory benchmarks overlook the dynamic memory formation that occurs during extended agent execution.
    Stated directly in the abstract as the core motivation for the new benchmark.
invented entities (2)
  • MemGym benchmark no independent evidence
    purpose: Unified memory-reasoning interface for long-horizon agent evaluation
    Newly presented in this work to combine existing gyms and pipelines.
  • MemRM reward model no independent evidence
    purpose: Lightweight scalar scorer for compression quality in place of full Docker rollouts
    Trained specifically for this benchmark using Qwen3-1.7B with QLoRA.

pith-pipeline@v0.9.0 · 5809 in / 1415 out tokens · 48645 ms · 2026-05-21T04:55:20.992108+00:00 · methodology

discussion (0)

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