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arxiv: 2604.14004 · v1 · submitted 2026-04-15 · 💻 cs.AI · cs.CL

Recognition: unknown

Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents

Kangsan Kim, Mengye Ren, Minki Kang, Sung Ju Hwang, Taeil Kim, Yanlai Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:38 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords memory transfer learningcoding agentscross-domain transfermeta-knowledge transferabstraction levelself-evolutionagent memory
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The pith

Cross-domain memory improves coding agent performance by 3.7% by sharing high-level meta-knowledge instead of specific code.

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

The paper tests whether memories built while solving coding tasks in one domain can help agents solve tasks in other domains. It pools memories from six different benchmarks and stores them in four formats that range from detailed execution traces to abstract lessons. Results show a consistent average gain, driven mostly by reusable strategies such as validation and testing routines rather than copied solutions. This matters because current memory systems for coding agents are usually confined to one narrow type of problem and therefore miss common programming practices that appear across domains. The work supplies empirical rules for deciding which kinds of memories are safe to share and which ones are too specific to transfer.

Core claim

Agents that draw on a single shared memory pool collected from multiple coding domains improve their performance on each individual domain. The improvement comes from transferring abstract meta-knowledge such as validation routines, while low-level execution traces frequently cause negative transfer because they are too tied to their original task.

What carries the argument

Memory Transfer Learning (MTL) using a unified memory pool across heterogeneous domains and four graded memory representations that vary in abstraction level.

If this is right

  • Gains grow larger when the shared memory pool is expanded.
  • Memories collected by one model can still help a different model.
  • Memory design should favor high-level abstractions over raw traces to avoid negative transfer.
  • Single-domain memory systems leave performance on the table by ignoring cross-domain commonalities.

Where Pith is reading between the lines

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

  • The same abstraction-transfer pattern may appear in other agent domains such as scientific code or automated theorem proving where meta-strategies recur.
  • Agents could be trained to convert their own experiences into abstract form before storing them, increasing future transfer value.
  • Memory-pool scaling suggests that very large multi-domain collections could produce larger gains than the modest average reported here.

Load-bearing premise

The six chosen benchmarks cover enough variety of real coding problems and the four memory formats represent the range from concrete to abstract without selection bias in the observed transfer effects.

What would settle it

Repeating the full set of transfer experiments on a new coding benchmark whose problem type is absent from the original six would show whether the 3.7% gain and the advantage of abstract over concrete memories still hold.

Figures

Figures reproduced from arXiv: 2604.14004 by Kangsan Kim, Mengye Ren, Minki Kang, Sung Ju Hwang, Taeil Kim, Yanlai Yang.

Figure 1
Figure 1. Figure 1: Conceptual overview of Memory Transfer Learning. Unlike (A) memory-less agents or (B) single-domain self-evolving agents, (C) our approach utilizes a shared memory pool from heterogeneous coding tasks. (D) In the evaluation on diverse benchmarks, MTL outperforms a self-evolving approach. in self-evolving agents by enabling the extraction of reusable workflows and transferable insights from past inferences … view at source ↗
Figure 2
Figure 2. Figure 2: Illustrative examples of four memory formats. We utilize Trajectory, Workflow, Summary, and Insight formats to analyze how different levels of information abstraction affect cross-task transferability. guage models that leverage runtime execution information for function-level code generation. Beyond a single file level editing, CodeAgent (Zhang et al., 2024), RepoA￾gent (Luo et al., 2024), RLCoder (Wang e… view at source ↗
Figure 3
Figure 3. Figure 3: Breakdown of Memory Transfer Contribution. Trans￾ferred memory mainly contributes through meta-knowledge. 4.2. Mechanism of Memory Transfer Learning 4.2.1. HOW DOES MEMORY TRANSFER LEARNING BENEFIT THE AGENTS? To investigate the operational mechanisms of memory trans￾fer learning, we inspect the inference outcomes using LLM and manual case studies. First, we collect the trajectories of the instances in whi… view at source ↗
Figure 4
Figure 4. Figure 4: t-SNE Visualization of Memory Formats. The leftmost plot shows task embeddings, followed by three different memory types to the right. Each color represents a specific benchmark used in experiments. While task and workflow embeddings are clustered within each domain, the insight embeddings are sparse and intermingled, reflecting their task-agnostic nature. Trajectory Workflow Summary Insight 0 5 3.09 4.02 … view at source ↗
Figure 5
Figure 5. Figure 5: Embedding Distribution Analysis DBI and LISI reveal weaker separation and stronger mixing with higher abstraction. specific, containing raw command-level actions, whereas Summary and Insight memories are more abstract and gen￾eralized. These properties are clearly reflected in the em￾bedding space visualizations shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Memory Scaling Larger memory pools and more do￾mains lead to better performance through increased diversity. ratios of 1/4, 2/4, and 3/4 of the original size. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Workflow Generation Prompt for a Success Trajectory 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Workflow Generation Prompt for a Failed Trajectory Summary Generation Prompt for a Success Trajectory ## Task Description You are given a **successful command trajectory** for a code-editing task - a sequence of actions and Bash commands that correctly achieved its goal. Your goal is to produce a **structured summary** that captures both what was done and why it worked, so this trajectory can be used as a … view at source ↗
Figure 9
Figure 9. Figure 9: Summary Generation Prompt for a Success Trajectory 17 [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Summary Generation Prompt for a Failed Trajectory Insight Generation Prompt for a Success Trajectory You are an expert in repository-level code editing. You will be given a user query, the corresponding trajectory that represents how an agent successfully accomplished the task. ## Guidelines You need to extract and summarize useful insights in the format of memory items based on the agent's successful tra… view at source ↗
Figure 11
Figure 11. Figure 11: Insight Generation Prompt for a Success Trajectory 18 [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Insight Generation Prompt for a Failed Trajectory 19 [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
read the original abstract

Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as runtime environments and programming languages, that exist across diverse real-world coding problems. To address this limitation, we investigate \textbf{Memory Transfer Learning} (MTL) by harnessing a unified memory pool from heterogeneous domains. We evaluate performance across 6 coding benchmarks using four memory representations, ranging from concrete traces to abstract insights. Our experiments demonstrate that cross-domain memory improves average performance by 3.7\%, primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code. Importantly, we find that abstraction dictates transferability; high-level insights generalize well, whereas low-level traces often induce negative transfer due to excessive specificity. Furthermore, we show that transfer effectiveness scales with the size of the memory pool, and memory can be transferred even between different models. Our work establishes empirical design principles for expanding memory utilization beyond single-domain silos. Project page: https://memorytransfer.github.io/

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 / 3 minor

Summary. The manuscript introduces Memory Transfer Learning (MTL) for coding agents, in which a unified memory pool drawn from heterogeneous domains is used to improve performance over single-domain baselines. Experiments across 6 coding benchmarks and 4 memory representations (spanning concrete traces to abstract insights) report a 3.7% average gain, attributed mainly to transfer of meta-knowledge such as validation routines; high-level abstractions transfer positively while low-level traces often produce negative transfer. The work also reports that transfer effectiveness scales with memory-pool size and that memories can be transferred across different models, yielding empirical design principles for cross-domain memory use.

Significance. If the central empirical findings hold after addressing controls, the paper supplies concrete evidence that abstraction level governs cross-domain memory transfer in coding agents and that meta-knowledge generalizes more readily than task-specific traces. This could inform the design of more scalable, less domain-siloed memory systems for LLM-based agents and provides a reproducible benchmark suite for future memory-transfer studies.

major comments (2)
  1. [§4 (main experimental results)] §4 (main experimental results): The 3.7% average improvement is attributed to cross-domain meta-knowledge transfer enabled by abstraction, yet the unified heterogeneous pool necessarily increases total memory volume and candidate diversity relative to single-domain baselines. The manuscript itself states that transfer effectiveness scales with pool size, but no control experiment matches pool size, example count, and retrieval budget across conditions; without it the observed benefit cannot be unambiguously ascribed to domain heterogeneity or the concrete-to-abstract spectrum rather than simply having more retrieval candidates.
  2. [§5 (analysis of transferred content)] §5 (analysis of transferred content): The claim that gains arise 'primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code' is central to the interpretation of why abstraction helps, yet the manuscript provides no quantitative breakdown (e.g., fraction of retrieved memories classified as meta vs. task-specific, or ablation removing meta-memories) that would support the 'primarily' qualifier.
minor comments (3)
  1. [Methods] The four memory representations are described only at a high level; a concise table listing their exact formats, token budgets, and retrieval mechanisms would improve reproducibility.
  2. [Results] Statistical significance of the 3.7% average gain and of per-benchmark differences is not reported; adding p-values or confidence intervals would strengthen the results section.
  3. [Abstract / Conclusion] The project page URL is given but the manuscript does not indicate whether code, prompts, and raw logs are released there; explicit data-availability statement is needed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of experimental controls and quantitative support for our claims. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: §4 (main experimental results): The 3.7% average improvement is attributed to cross-domain meta-knowledge transfer enabled by abstraction, yet the unified heterogeneous pool necessarily increases total memory volume and candidate diversity relative to single-domain baselines. The manuscript itself states that transfer effectiveness scales with pool size, but no control experiment matches pool size, example count, and retrieval budget across conditions; without it the observed benefit cannot be unambiguously ascribed to domain heterogeneity or the concrete-to-abstract spectrum rather than simply having more retrieval candidates.

    Authors: We agree that matching pool size, example count, and retrieval budget is essential to isolate the contribution of domain heterogeneity from mere increases in memory volume. The revised manuscript will include a new control experiment in which single-domain baselines are augmented with additional in-domain memories (drawn from the same source distributions) to equalize total pool size and retrieval budget with the heterogeneous condition. This will allow direct comparison and clarify whether the observed gains stem from cross-domain transfer or pool scale. revision: yes

  2. Referee: §5 (analysis of transferred content): The claim that gains arise 'primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code' is central to the interpretation of why abstraction helps, yet the manuscript provides no quantitative breakdown (e.g., fraction of retrieved memories classified as meta vs. task-specific, or ablation removing meta-memories) that would support the 'primarily' qualifier.

    Authors: We acknowledge that the current version lacks the requested quantitative breakdown and ablation to substantiate the 'primarily' qualifier. In the revision, we will expand §5 with an automated classification of retrieved memories into meta-knowledge categories (e.g., validation routines, error-handling patterns, abstraction principles) versus task-specific code, reporting the proportions of each type across the six benchmarks. We will also add an ablation study that removes meta-memories from the pool and measures the resulting change in performance gains to quantify their contribution. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical study with direct benchmark measurements

full rationale

The paper reports experimental results from running coding agents on six benchmarks with four memory representations. Performance deltas (e.g., the 3.7% average improvement) are obtained by direct execution and comparison rather than any derivation, fitted parameter, or self-referential definition. No equations, uniqueness theorems, or ansatzes appear; claims about abstraction and meta-knowledge transfer rest on observed outcomes, not on a chain that reduces to its own inputs. Self-citations, if present, are not load-bearing for any derivation. The study is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that coding tasks share enough infrastructural foundations (runtime environments, languages) to allow beneficial memory transfer; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Coding tasks across domains share infrastructural foundations such as runtime environments and programming languages that enable memory transfer.
    Explicitly stated in the abstract as the motivation for investigating MTL.

pith-pipeline@v0.9.0 · 5502 in / 1161 out tokens · 26573 ms · 2026-05-10T12:38:44.629492+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

13 extracted references · 3 canonical work pages

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    A survey on in-context learning

    Association for Computational Linguistics. doi: 10.18653/v1/2024.emnlp-main.64. URL https:// aclanthology.org/2024.emnlp-main.64/. Fang, J., Peng, Y ., Zhang, X., Wang, Y ., Yi, X., Zhang, G., Xu, Y ., Wu, B., Liu, S., Li, Z., et al. A comprehensive sur- vey of self-evolving ai agents: A new paradigm bridging foundation models and lifelong agentic systems...

    work page doi:10.18653/v1/2024.emnlp-main.64 2024

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    Universal language model fine-tuning for text classification

    PMLR, 2019. Howard, J. and Ruder, S. Universal language model fine-tuning for text classification.arXiv preprint arXiv:1801.06146, 2018. Hui, B., Yang, J., Cui, Z., Yang, J., Liu, D., Zhang, L., Liu, T., Zhang, J., Yu, B., Lu, K., et al. Qwen2. 5-coder technical report.arXiv preprint arXiv:2409.12186, 2024. 10 Memory Transfer Learning: How Memories are Tr...

    work page arXiv 2019

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    MA-EgoQA: Question answer- ing over egocentric videos from multiple embodied agents.arXiv preprint arXiv:2603.09827, 2026

    URL https://openreview.net/forum? id=VTF8yNQM66. Kim, K., Park, G., Lee, Y ., Yeo, W., and Hwang, S. J. Videoicl: Confidence-based iterative in-context learning for out-of-distribution video understanding. InProceed- ings of the Computer Vision and Pattern Recognition Conference, pp. 3295–3305, 2025. Kim, K., Yang, Y ., Kim, S., Yeo, W., Lee, Y ., Ren, M....

    work page arXiv 2025

  4. [4]

    - Context: Used when memories reinforced the pattern of making small changes and checking them immediately (e.g., ”edit-test-repeat” loop)

    Iterative Workflow Discipline - Definition: Guiding the agent to follow a structured, step-by-step development process (e.g., inspect edit run verify) rather than attempting risky one-shot solutions. - Context: Used when memories reinforced the pattern of making small changes and checking them immediately (e.g., ”edit-test-repeat” loop)

  5. [5]

    Algorithmic Strategy Transfer - Definition: Providing specific algorithmic approaches or data structures suitable for the problem class. - Context: Used when the agent recalled mathematical formulas, dynamic programming approaches, combinatorial logic, or specific heuristics (e.g., ”O(n) single-pass,” ”backtracking with pruning”)

  6. [6]

    - Context: Used when memories prompted the agent to write repro.py, use assert, or create local checks to validate logic before submission

    Test Driven Verification - Definition: Encouraging the creation of reproduction scripts, smoke tests, or minimal harnesses when official tests are missing or too heavy. - Context: Used when memories prompted the agent to write repro.py, use assert, or create local checks to validate logic before submission

  7. [7]

    - Context: Used when dealing with missing packages, compilation flags, bash vs sh differences, or cross-compilation toolchains

    Environmental Adaptation - Definition: Helping the agent navigate specific system constraints, build tools, or OS-level idiosyncrasies. - Context: Used when dealing with missing packages, compilation flags, bash vs sh differences, or cross-compilation toolchains

  8. [8]

    - Context: Used when the agent explicitly avoided actions that caused failures in retrieved memories (e.g., ”avoid blind text patching,” ”do not guess outputs”)

    Anti-Pattern Avoidance - Definition: Acting as a cautionary guardrail against known failure modes or brittle approaches. - Context: Used when the agent explicitly avoided actions that caused failures in retrieved memories (e.g., ”avoid blind text patching,” ”do not guess outputs”)

  9. [9]

    - Context: Used when memories guided the agent to handle empty inputs, normalize heterogeneous data types, or enforce strict input sanitization

    Input Validation and Robustness - Definition: Ensuring the solution correctly handles edge cases, data normalization, and defensive parsing. - Context: Used when memories guided the agent to handle empty inputs, normalize heterogeneous data types, or enforce strict input sanitization

  10. [10]

    - Context: Used when the agent needed to preserve legacy behavior, match specific output schemas (JSON/Y AML), or integrate correctly with a framework like Django or React

    API and Interface Compliance - Definition: Ensuring the code adheres to existing function signatures, class structures, or external library contracts. - Context: Used when the agent needed to preserve legacy behavior, match specific output schemas (JSON/Y AML), or integrate correctly with a framework like Django or React

  11. [11]

    - Context: Used when memories reinforced using specific completion tokens (e.g., ”COMPLETETASK...”), single-command constraints, or specific output formats

    Interaction Protocol Adherence - Definition: Ensuring the agent complies with the specific formatting and submission rules of the benchmark environment. - Context: Used when memories reinforced using specific completion tokens (e.g., ”COMPLETETASK...”), single-command constraints, or specific output formats

  12. [12]

    - Context: Used when the agent utilized robust heredoc patterns, correct quoting to avoid shell interpolation, or atomic file writes

    File and Syntax Management - Definition: Providing safe techniques for file manipulation and code injection to prevent syntax errors during generation. - Context: Used when the agent utilized robust heredoc patterns, correct quoting to avoid shell interpolation, or atomic file writes

  13. [13]

    goal": "Describe when this workflow can be applied

    Repository Exploration Tactics - Definition: Guiding the agent on how to effectively locate relevant code or resources within a large codebase. - Context: Used when memories suggested using grep, find, or inspecting specific asset files (like package.json or paper abstracts) before writing code. 15 Memory Transfer Learning: How Memories are Transferred Ac...