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arxiv: 2605.17721 · v1 · pith:7JHGNWMEnew · submitted 2026-05-18 · 💻 cs.AI

EXG: Self-Evolving Agents with Experience Graphs

Yuxin Jin , Siyuan Zhang , Hanchen Wang , Lu Qin , Ying Zhang , Wenjie Zhang This is my paper

Pith reviewed 2026-05-19 22:14 UTC · model grok-4.3

classification 💻 cs.AI
keywords self-evolving agentsexperience graphLLM agentsstructured memorycross-task reuseonline experienceagent improvement
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The pith

EXG turns agent successes and failures into a connected graph for instant reuse across tasks.

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

The paper presents EXG as a way to organize what an agent learns from its own runs into a graph that links related successes and failures. This structured form replaces scattered reflections or loose memory stores, letting the agent pull in useful past results right when needed for new problems. A sympathetic reader would see this as a step toward agents that keep getting better on the job rather than staying fixed after initial setup. The graph can grow while the agent works and can also be saved for later use as ready-made memory. Experiments on code and reasoning tasks indicate it delivers better results with less wasted effort than earlier approaches.

Core claim

EXG is the first experience graph designed for self-evolving agents, supporting both online, real-time graph growth during execution for immediate cross-task experience reuse, and offline reuse of a consolidated experience graph as an external memory module. This design also enables EXG to serve as a plug-and-play component for existing self-evolving agents, organizing prior experience into a unified experience graph and improving both solution quality and resource efficiency as deployment progresses.

What carries the argument

The experience graph, which explicitly organizes accumulated successes and failures into a structured, relational representation for real-time growth and consolidated reuse.

If this is right

  • Agents gain immediate cross-task reuse from experiences gathered during execution.
  • A consolidated graph can be used offline as external memory to boost later performance.
  • Existing self-evolving agents can adopt the graph as a plug-in to organize their prior experience.
  • Overall performance-efficiency trade-offs improve compared with ad hoc reflection or fragmented memory.

Where Pith is reading between the lines

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

  • The graph approach could be tested in domains beyond code and reasoning, such as tool-use or planning agents, to check whether relational linking scales to longer task chains.
  • If the structure keeps overhead low, it might reduce reliance on periodic retraining by letting agents carry forward lessons in a compact, queryable form.
  • Connections to graph-based memory systems in other AI work could be explored to see whether the same relational pattern supports transfer between entirely different agent types.

Load-bearing premise

Successes and failures accumulated during agent execution can be effectively captured and related in a graph structure that enables immediate and transferable reuse without fragmentation or high overhead.

What would settle it

A direct comparison on the same code generation and reasoning benchmarks showing that agents equipped with the experience graph produce no measurable gains in solution quality or resource efficiency over reflection-only or unstructured-memory baselines.

Figures

Figures reproduced from arXiv: 2605.17721 by Hanchen Wang, Lu Qin, Siyuan Zhang, Wenjie Zhang, Ying Zhang, Yuxin Jin.

Figure 1
Figure 1. Figure 1: Overview of the self-evolving experience graph. (a) Trajectory produces structured cases from agent interactions. (b) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: EXG structured prompt architecture. first initializes a provisional case 𝑐𝑞, which contains the task input and contextual information but does not yet include an output or correctness outcome. Conditioned on 𝑐𝑞, the experience graph G is queried to retrieve relevant prior cases using the graph retrieval and reranking procedures defined in the EXG design. Based on the reranked cases, EXG constructs a set of… view at source ↗
Figure 3
Figure 3. Figure 3: Offline self-evolving via graph reuse. EXG is pre [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average number of LLM calls per task under the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Latency breakdown under the online setting on [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Token usage breakdown on HumanEval and MuSiQue. Each bar shows the total number of tokens con￾sumed, with input tokens stacked below output tokens. a deliberate rise in input tokens (+20.0%), reflecting the injection of experience hints, while output tokens are reduced by 19.3%. Compared to SE-Agent-Lite, which incurs 158,125 total tokens, EXG reduces total token consumption by 20.8%, with a 37.8% reductio… view at source ↗
Figure 8
Figure 8. Figure 8: Learning curves on HumanEval. to improve as more tasks are seen. By around 60 tasks, EXG-based methods reach approximately 85%, already exceeding baseline per￾formance by about 7–10 percentage points. By the end of the task sequence, EXG-based methods achieve a cumulative Pass@2 close to 90%, compared to 75–78% for baseline methods. This corresponds to an absolute late-stage improvement of roughly 12–15 pe… view at source ↗
Figure 9
Figure 9. Figure 9: Learning curves on MuSiQue. exposure increases, baseline methods exhibit limited progression: even after around 60 tasks, their Pass@1 rises only marginally to approximately 36–38%, after which further gains largely diminish. In contrast, EXG-based methods collectively display a qualitatively different trajectory. The core EXG-based method shows a consistent upward trend as experience accumulates, reaching… view at source ↗
read the original abstract

Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic improvement over time. In response, a growing line of work on self-evolving agents explores how agents can improve through experience during deployment, but most existing approaches either rely on ad hoc reflection limited to single-task correction or adopt unstructured memory that accumulates fragmented experience with delayed usability. To address this limitation, we introduce EXG, an experience graph framework for self-evolving agents that explicitly organizes accumulated successes and failures into a structured, relational representation. EXG is the first experience graph designed for self-evolving agents, supporting both online, real-time graph growth during execution for immediate cross-task experience reuse, and offline reuse of a consolidated experience graph as an external memory module. This design also enables EXG to serve as a plug-and-play component for existing self-evolving agents, organizing prior experience into a unified experience graph and improving both solution quality and resource efficiency as deployment progresses. Extensive experiments across code generation and reasoning benchmarks show that EXG attains more favorable performance-efficiency trade-offs than reflection- and memory-based baselines in both online and offline evaluations. Our results suggest that structuring experience as a graph provides a principled foundation for scalable and transferable self-evolving agent behavior.

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

0 major / 3 minor

Summary. The manuscript introduces EXG, an experience graph framework for self-evolving LLM-based agents. It organizes accumulated successes and failures into a structured relational graph that supports online real-time growth during execution for immediate cross-task reuse, as well as offline consolidation and reuse as an external memory module. EXG is presented as a plug-and-play component that can be integrated with existing self-evolving agents to improve solution quality and resource efficiency over time. The authors report extensive experiments on code generation and reasoning benchmarks demonstrating more favorable performance-efficiency trade-offs relative to reflection- and memory-based baselines in both online and offline settings.

Significance. If the experimental results hold, the work provides a concrete, graph-structured mechanism for experience reuse that directly targets fragmentation and delayed usability issues in current self-evolving agent designs. The dual support for online growth and offline external-memory use, combined with the plug-and-play integration claim, could offer a reusable primitive for building more adaptive agents. The emphasis on efficiency alongside performance is a practical strength that distinguishes the contribution from purely reflective or flat-memory approaches.

minor comments (3)
  1. The abstract asserts 'extensive experiments' with favorable trade-offs but does not preview key metrics, baselines, or dataset details; adding a concise summary of the evaluation protocol in the abstract or introduction would improve accessibility.
  2. Clarify the precise node and edge definitions for experience fragments early in the manuscript (ideally with a small illustrative example) to make the graph-construction rules immediately understandable before the algorithmic description.
  3. Ensure that the experimental section includes explicit statements of statistical significance or variance across runs for the reported performance-efficiency trade-offs, as this is necessary to support the cross-baseline claims.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, recognition of the significance of the EXG framework, and recommendation for minor revision. We are pleased that the dual online/offline design and plug-and-play aspects were viewed favorably.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents EXG as a new architectural framework for structuring agent experience into a relational graph, with design choices for online real-time growth and offline consolidation explicitly described as implementation decisions rather than derived predictions. No equations, parameter fits, or self-citations are invoked to force the core claims; the abstract and positioning against ad-hoc reflection rely on stated motivations and reported benchmark outcomes instead of reducing to input definitions or prior author work by construction. The framework is introduced with plug-and-play integration details that remain testable independently of any self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based solely on the abstract, the primary addition is the EXG graph structure itself. Limited details prevent exhaustive enumeration of parameters or axioms.

axioms (1)
  • domain assumption LLM-based agents accumulate usable experience from successes and failures that can be relationally structured for reuse
    Foundational premise enabling the graph design, stated in the problem setup and solution description.
invented entities (1)
  • Experience Graph (EXG) no independent evidence
    purpose: To organize accumulated successes and failures into a structured relational representation for online and offline reuse
    Newly introduced framework component positioned as the core innovation.

pith-pipeline@v0.9.0 · 5778 in / 1246 out tokens · 35600 ms · 2026-05-19T22:14:02.846903+00:00 · methodology

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Reference graph

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