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arxiv: 2605.17444 · v1 · pith:YIMYYIHNnew · submitted 2026-05-17 · 💻 cs.SE · cs.AI· cs.CL

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair

Simiao Liu , Li Zhang , Fang Liu , Xiaoli Lian , Yang Liu , Yinghao Zhu This is my paper

Pith reviewed 2026-05-19 23:02 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CL
keywords vulnerability repairLLM agentsmemory augmentationrepository-level repairautomated security fixeshierarchical memorypatch refinement
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The pith

MemRepair equips LLM repair agents with three persistent memory layers so they can reuse past fixes, security patterns, and refinement paths when fixing vulnerabilities across large code repositories.

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

The paper presents MemRepair as a way to turn vulnerability repair into an iterative process that draws on accumulated experience rather than generating each patch from the current code view alone. It combines History-Fix memory for repository conventions, Security-Pattern memory for reusable defenses, and Refinement-Trajectory memory for turning failed attempts into successful revisions. The authors test this on three benchmarks covering multiple languages and show higher success rates than existing agents while keeping repair cost comparable. A sympathetic reader would care because most current agent systems stall on multi-file changes that require remembering what worked before. If the memory layers deliver net gains without new retrieval mistakes, they point toward repair agents that improve with use instead of starting fresh each time.

Core claim

MemRepair formulates repository-level vulnerability repair as an iterative experience-driven process that retrieves from three complementary memory layers—History-Fix, Security-Pattern, and Refinement-Trajectory—inside a dynamic feedback loop, allowing the agent to apply prior fixes, reusable security patterns, and lessons from failed validations to produce more reliable patches.

What carries the argument

The three-layer hierarchical memory (History-Fix for repository conventions, Security-Pattern for defenses, Refinement-Trajectory for failure-to-success paths) retrieved at runtime inside a feedback-driven refinement loop.

If this is right

  • Agents become able to reuse repository-specific repair conventions across similar projects instead of rediscovering them.
  • Security patterns learned from one fix can be applied to prevent analogous vulnerabilities in later tasks.
  • Prior failed validation attempts supply concrete evidence that lets the agent revise semantically invalid patches.
  • The same memory structure supports repair across Python, Go, JavaScript, and C++ without language-specific redesign.

Where Pith is reading between the lines

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

  • External memory could let future agents operate with smaller context windows while still handling long repair histories.
  • The refinement-trajectory layer might transfer to other iterative tasks such as test generation or performance tuning.
  • If retrieval stays accurate, the approach suggests a path toward agents whose repair quality improves over successive tasks on the same codebase.

Load-bearing premise

The three memory layers can be retrieved and applied at runtime without introducing retrieval errors or latency that cancel out the gains on complex multi-file repairs.

What would settle it

A controlled experiment on a new multi-file benchmark where the same base agent with the memory layers disabled shows equal or higher resolution rates and lower average cost than the full MemRepair system.

Figures

Figures reproduced from arXiv: 2605.17444 by Fang Liu, Li Zhang, Simiao Liu, Xiaoli Lian, Yang Liu, Yinghao Zhu.

Figure 1
Figure 1. Figure 1: Overall architecture of MemRepair. MemRepair first runs vulnerability PoC 𝜏𝑣𝑢𝑙𝑛 to obtain runtime failure evidence (e.g., stack traces/sanitizer reports) and uses a structure-aware localizer tool (Iter_grep) to produce a localization object L𝑙𝑜𝑐 (target file, suspicious line range, and a concise trace). Conditioned on L𝑙𝑜𝑐 , the Patcher generates a candidate patch 𝛿 guided by hierarchical memory retrieval.… view at source ↗
Figure 2
Figure 2. Figure 2: Workflow of our experience retrieval mechanism. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Issue localization-to-repair flow on SecBench and Multi-SWE-Bench (C++). [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Venn diagram of the number pf resolved issues on [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can operate reliably at repository scale. Although Large Language Model (LLM)-based agents have recently shown promise for automated vulnerability repair (AVR), most existing systems still treat repair as a single generation step over the currently visible code context. As a result, they lack a persistent mechanism for reusing prior fixes or learning from failed validation attempts, which limits their effectiveness on complex, multi-file repair tasks. We present MemRepair, a memory-augmented agentic framework that formulates vulnerability repair as an iterative, experience-driven process. MemRepair combines three complementary memory layers, i.e., History-Fix, Security-Pattern, and Refinement-Trajectory memories, with a dynamic feedback-driven refinement loop. This design allows the agent to retrieve repository-specific repair conventions, apply reusable security defenses, and exploit prior "failure-to-success" trajectories to revise semantically invalid patches based on runtime evidence. We evaluate MemRepair on three representative repository-level vulnerability repair benchmarks: SEC-Bench, PatchEval (Python, Go, JavaScript), and the C++ subset of Multi-SWE-bench. MemRepair achieves state-of-the-art resolution rates of 58.0%, 58.2%, and 30.58%, respectively, outperforming strong general-purpose agents such as OpenHands and SWE-agent, as well as the specialized AVR tool InfCode-C++, while maintaining competitive repair cost. These results show that persistent, hierarchical repair memory can substantially improve the reliability of agentic vulnerability repair across diverse languages and repository settings.

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

3 major / 1 minor

Summary. The paper proposes MemRepair, a memory-augmented agentic framework for repository-level vulnerability repair. It introduces three memory layers—History-Fix, Security-Pattern, and Refinement-Trajectory—integrated with a refinement loop to enable iterative, experience-driven patch generation. The framework is evaluated on SEC-Bench, PatchEval, and Multi-SWE-bench (C++), achieving claimed state-of-the-art resolution rates of 58.0%, 58.2%, and 30.58% respectively, outperforming baselines like OpenHands, SWE-agent, and InfCode-C++.

Significance. Should the empirical gains hold under rigorous controls for evaluation protocol and memory retrieval accuracy, this work would represent a meaningful advance in agentic repair systems by showing how hierarchical memory can improve handling of complex, multi-file vulnerabilities across languages. It directly targets the limitation of stateless generation in current LLM agents.

major comments (3)
  1. Evaluation section: The reported resolution rates of 58.0% on SEC-Bench, 58.2% on PatchEval, and 30.58% on Multi-SWE-bench C++ are presented without ablation studies that isolate the contribution of each memory layer (History-Fix, Security-Pattern, Refinement-Trajectory) versus the refinement loop or base agent. This leaves the central attribution of gains to the hierarchical memory unverified.
  2. Evaluation section: No retrieval-precision, recall, or error-analysis metrics are provided for the three memory layers. Since the framework relies on runtime retrieval of repository-specific fixes, patterns, and trajectories to succeed on multi-file repairs, the absence of these diagnostics means it is unclear whether the SOTA numbers reflect successful memory use or other factors.
  3. Experiments section: The evaluation protocol lacks detail on statistical significance, number of runs, temperature/prompt controls, and safeguards against data leakage or benchmark contamination. These omissions weaken support for the performance claims against the named baselines.
minor comments (1)
  1. Abstract: The phrase 'competitive repair cost' is used without defining the metric (e.g., LLM calls, tokens, or wall-clock time) or supplying the corresponding baseline numbers for direct comparison.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback on our manuscript. We address each of the major comments below and outline the revisions we will make to strengthen the evaluation sections.

read point-by-point responses

  1. Referee: Evaluation section: The reported resolution rates of 58.0% on SEC-Bench, 58.2% on PatchEval, and 30.58% on Multi-SWE-bench C++ are presented without ablation studies that isolate the contribution of each memory layer (History-Fix, Security-Pattern, Refinement-Trajectory) versus the refinement loop or base agent. This leaves the central attribution of gains to the hierarchical memory unverified.

    Authors: We agree that dedicated ablation studies are necessary to rigorously attribute performance gains to the individual memory layers and the refinement loop. In the revised manuscript, we will add a new subsection in the evaluation with ablations that systematically disable each memory component (History-Fix, Security-Pattern, Refinement-Trajectory) and the refinement loop, reporting the resulting resolution rates on all benchmarks. This will provide direct evidence for the contribution of the hierarchical memory design. revision: yes

  2. Referee: Evaluation section: No retrieval-precision, recall, or error-analysis metrics are provided for the three memory layers. Since the framework relies on runtime retrieval of repository-specific fixes, patterns, and trajectories to succeed on multi-file repairs, the absence of these diagnostics means it is unclear whether the SOTA numbers reflect successful memory use or other factors.

    Authors: We acknowledge this gap in the current evaluation. We will incorporate retrieval precision and recall metrics for each of the three memory layers, computed over the successful repairs. Furthermore, we will add an error analysis section that categorizes failure cases related to memory retrieval inaccuracies versus other issues, such as patch generation errors. These additions will clarify the role of memory retrieval in achieving the reported performance. revision: yes

  3. Referee: Experiments section: The evaluation protocol lacks detail on statistical significance, number of runs, temperature/prompt controls, and safeguards against data leakage or benchmark contamination. These omissions weaken support for the performance claims against the named baselines.

    Authors: We will revise the Experiments section to provide comprehensive details on the evaluation protocol. This includes specifying the number of independent runs (e.g., 3 runs with different seeds), temperature settings used (e.g., 0.2 for generation), prompt engineering controls, and statistical significance testing (such as reporting p-values from McNemar's test for comparisons with baselines). We will also detail safeguards against data leakage, including verification that no benchmark instances were present in the LLM's training data and use of version-controlled repositories for evaluation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework evaluation on public benchmarks

full rationale

The paper introduces MemRepair as a memory-augmented agentic framework with three described memory layers and a refinement loop, then reports empirical resolution rates on SEC-Bench, PatchEval, and Multi-SWE-bench subsets. No equations, fitted parameters, or derivation steps appear in the provided abstract or evaluation summary. Claims rest on experimental comparisons against baselines rather than any self-definitional reduction, fitted-input prediction, or load-bearing self-citation chain. The central results are therefore independent of the inputs by construction and receive a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 3 invented entities

This is an applied systems paper whose central claim rests on the empirical effectiveness of the proposed memory architecture and the representativeness of the three chosen benchmarks; no mathematical axioms or free parameters are stated in the abstract.

invented entities (3)
  • History-Fix memory no independent evidence
    purpose: retrieve repository-specific repair conventions
    New component introduced to store and reuse prior fixes within the same repository.
  • Security-Pattern memory no independent evidence
    purpose: apply reusable security defenses
    New component introduced to hold common security patterns.
  • Refinement-Trajectory memory no independent evidence
    purpose: exploit prior failure-to-success trajectories
    New component introduced to record and reuse revision paths from invalid to valid patches.

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Reference graph

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