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arxiv: 2605.30712 · v1 · pith:FRVWKLNLnew · submitted 2026-05-29 · 💻 cs.CL

ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents

Tao Feng , Chongrui Ye , Tianyang Luo , Jingjun Xu , Xueqiang Xu , Haozhen Zhang , Zhigang Hua , Yan Xie
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Shuang Yang Ge Liu Jiaxuan You
This is my paper

Pith reviewed 2026-06-28 22:59 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM agentsexperience learninggraph memorymodel-agnosticreinforcement learningretrieval copilotagentic environmentsexperience reuse
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The pith

ExpGraph lets frozen LLM executors reuse experiences from a self-evolving graph without any parameter updates.

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

LLM agents typically solve each task from scratch even when prior trajectories contain reusable strategies or warnings. ExpGraph turns those trajectories into nodes of skills and failure lessons inside a growing graph. Retrieval happens through graph diffusion plus a utility-ranked selection, with a lightweight copilot trained by reinforcement learning on direct performance comparisons. Experiments across question answering, math, code generation, and environments like ALFWorld show consistent gains and fewer steps for both small and large executors. The design keeps the executor itself unchanged so any stronger model can be swapped in later.

Core claim

ExpGraph summarizes historical trajectories into reusable skills and failure lessons, organizes them as nodes in a self-evolving experience graph, and retrieves useful experiences through graph diffusion and utility-aware ranking. A lightweight retrieval copilot is trained with reinforcement learning using feedback that compares executor performance with and without retrieved experiences, while the graph is updated online from downstream task outcomes. This enables frozen and replaceable LLM executors to improve through external experience reuse without parameter updates.

What carries the argument

The self-evolving experience graph that stores summarized trajectories as nodes, retrieved via graph diffusion and utility-aware ranking under control of an RL-trained retrieval copilot.

If this is right

  • Executors of different sizes can all benefit from the same external graph without retraining.
  • Average interaction steps drop because retrieved lessons steer the agent away from repeated mistakes.
  • The framework applies equally to static reasoning tasks and multi-step agentic environments.
  • Swapping in a stronger executor later requires no re-processing of past experience data.
  • Graph structure, utility ranking, and the adaptive copilot each contribute measurable value according to ablations.

Where Pith is reading between the lines

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

  • The same graph could serve multiple agents operating in parallel by sharing nodes across instances.
  • Adding explicit conflict detection between opposing lessons would be a natural next extension when experiences contradict.
  • Measuring how quickly the graph grows and stabilizes on long-running agent deployments would test scalability outside the reported benchmarks.

Load-bearing premise

The performance difference observed when the executor runs with versus without retrieved experiences supplies a clean enough signal to train the copilot to select only helpful items across tasks and model sizes.

What would settle it

A controlled test in which the copilot's top-ranked experiences produce no gain or a consistent drop in executor success rate on held-out tasks would show the retrieval mechanism does not work as claimed.

Figures

Figures reproduced from arXiv: 2605.30712 by Chongrui Ye, Ge Liu, Haozhen Zhang, Jiaxuan You, Jingjun Xu, Shuang Yang, Tao Feng, Tianyang Luo, Xueqiang Xu, Yan Xie, Zhigang Hua.

Figure 1
Figure 1. Figure 1: Overview of ExpGraph. ExpGraph enables a frozen and replaceable executor LLM to improve through a self-evolving experience graph and a trainable retrieval copilot. For each incoming task xt, the task is embedded as hxt and passed to the retrieval copilot π t ret, which predicts two adaptive controls: Rt for graph diffusion depth and Wt for the similarity–utility trade-off. Retrieval is performed on the cur… view at source ↗
Figure 2
Figure 2. Figure 2: Zero-shot transfer across different executor shifts. (a) Small-to-large transfer: transfer￾ring the learned experience graph and retrieval copilot from a smaller executor to a larger executor. (b) Large-to-small transfer: transferring experience components from a larger executor to a smaller executor. (c) Non-reasoning-to-reasoning transfer: transferring experience components across execu￾tors with differe… view at source ↗
Figure 3
Figure 3. Figure 3: ExpGraph requires graph structure, utility feedback, and similarity-aware memory management to achieve robust gains. We compare ExpGraph with four ablation variants across five evaluation domains: QA, Reasoning, Coding, ALFWorld, and AppWorld. the effects of similarity-aware experience management, graph-structured experience organization, graph diffusion, and utility-aware ranking. Results are reported in … view at source ↗
read the original abstract

Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse successful strategies or failure lessons from prior experience. Fine-tuning on collected experience can improve reuse, but it is inflexible when stronger or more suitable executors emerge. We propose ExpGraph, a model-agnostic experience learning framework that enables frozen and replaceable LLM executors to improve through external experience reuse without parameter updates. ExpGraph summarizes historical trajectories into reusable skills and failure lessons, organizes them as nodes in a self-evolving experience graph, and retrieves useful experiences through graph diffusion and utility-aware ranking. A lightweight retrieval copilot is trained with reinforcement learning using feedback that compares executor performance with and without retrieved experiences, while the graph is updated online from downstream task outcomes. We evaluate ExpGraph on ExpSuite, covering question answering, mathematical reasoning, code generation, and multi-step agentic environments including ALFWorld and AppWorld. ExpGraph improves over the strongest baseline by 12.2% and 4.7% on static tasks with smaller and larger executors, and by 21.4% and 12.7% in agentic environments, while reducing average interaction steps by 12.7% and 21.6%. Ablations show that graph-structured experience, utility-aware ranking, and adaptive retrieval jointly enable effective experience reuse across diverse tasks and executor models.

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 / 1 minor

Summary. The paper proposes ExpGraph, a model-agnostic framework for LLM agents that summarizes historical trajectories into reusable skills and failure lessons stored as nodes in a self-evolving experience graph. Experiences are retrieved via graph diffusion and utility-aware ranking; a lightweight retrieval copilot is trained with RL whose reward is the performance delta between executor runs with versus without retrieved experiences. The graph is updated online from task outcomes. On the ExpSuite benchmark (QA, math reasoning, code generation, ALFWorld, AppWorld), ExpGraph reports gains of 12.2%/4.7% over the strongest baseline on static tasks (smaller/larger executors) and 21.4%/12.7% on agentic tasks, plus reductions in interaction steps of 12.7%/21.6%. Ablations attribute gains to the graph structure, ranking, and adaptive retrieval.

Significance. If the quantitative claims hold under rigorous evaluation, the model-agnostic external-memory design would be a useful practical advance, allowing frozen or replaceable executors to benefit from accumulated experience without parameter updates. The combination of graph diffusion for retrieval and online graph evolution from downstream outcomes is a concrete strength that supports continuous adaptation across executor sizes and task types.

major comments (2)
  1. [Evaluation section] Evaluation section: the abstract states specific percentage improvements (12.2%, 4.7%, 21.4%, 12.7%) and step reductions but supplies no information on the number of runs per condition, standard deviations, statistical significance tests, or exact baseline implementations and data-exclusion rules. These omissions are load-bearing for the central performance claims.
  2. [Method section (retrieval copilot training)] Method section (retrieval copilot training): the RL reward is defined as the scalar performance difference between executor runs with and without retrieved graph experiences, yet the manuscript provides no description of variance-reduction steps (paired trials, multiple rollouts per experience, or baseline subtraction) or bias controls when the executor itself changes size or sampling behavior. This directly affects whether the reported gains can be attributed to the copilot rather than task stochasticity.
minor comments (1)
  1. [Abstract] The abstract refers to 'ExpSuite' and 'strongest baseline' without a citation or short description of task composition and baseline details; adding these would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on evaluation transparency and RL training details. We will revise the manuscript to address both points by adding the requested statistical and methodological information.

read point-by-point responses

  1. Referee: [Evaluation section] Evaluation section: the abstract states specific percentage improvements (12.2%, 4.7%, 21.4%, 12.7%) and step reductions but supplies no information on the number of runs per condition, standard deviations, statistical significance tests, or exact baseline implementations and data-exclusion rules. These omissions are load-bearing for the central performance claims.

    Authors: We agree these reporting details are necessary to substantiate the performance claims. In the revised manuscript we will expand the Evaluation section (and appendix) to specify the number of runs per condition, include standard deviations, report statistical significance tests, provide precise baseline implementation details, and clarify any data-exclusion rules. revision: yes

  2. Referee: [Method section (retrieval copilot training)] Method section (retrieval copilot training): the RL reward is defined as the scalar performance difference between executor runs with and without retrieved graph experiences, yet the manuscript provides no description of variance-reduction steps (paired trials, multiple rollouts per experience, or baseline subtraction) or bias controls when the executor itself changes size or sampling behavior. This directly affects whether the reported gains can be attributed to the copilot rather than task stochasticity.

    Authors: We appreciate the referee noting this gap in the description. The revised Method section will explicitly describe our variance-reduction approach, which uses paired trials on identical task instances to compute the performance delta, multiple rollouts per condition, and baseline subtraction; it will also detail the normalization procedure applied when executor size or sampling behavior changes to mitigate bias. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical gains measured on external benchmarks

full rationale

The paper's derivation chain consists of an external RL reward signal (performance delta between executor runs with/without retrieved experiences) and empirical evaluation on independent task suites (ExpSuite, ALFWorld, AppWorld). No equations or steps reduce a claimed prediction or result to a quantity defined inside the method itself. The reported improvements (12.2%/4.7% static, 21.4%/12.7% agentic) are obtained by direct comparison to external baselines rather than by construction from fitted parameters or self-citations. The framework therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies insufficient technical detail to enumerate concrete free parameters, background axioms, or new postulated entities; the experience graph is a modeling construct rather than an independently evidenced physical entity.

pith-pipeline@v0.9.1-grok · 5826 in / 1297 out tokens · 28642 ms · 2026-06-28T22:59:14.657528+00:00 · methodology

discussion (0)

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