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arxiv: 2510.17795 · v3 · submitted 2025-10-20 · 💻 cs.CL · cs.AI· cs.LG· cs.MA· cs.SE

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations

Yujie Luo , Zhuoyun Yu , Xuehai Wang , Yuqi Zhu , Ningyu Zhang , Lanning Wei , Lun Du , Da Zheng
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Huajun Chen
This is my paper

Pith reviewed 2026-05-18 05:52 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LGcs.MAcs.SE
keywords AI research replicationexecutable knowledge graphsLLM agentsknowledge representationcode extractionPaperBenchRAG limitationsscientific literature
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The pith

Executable Knowledge Graphs improve replication of AI research papers by embedding extracted code and insights into agent frameworks.

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

The paper establishes that current LLM-based approaches to replicating AI research fail mainly because retrieval methods miss hidden implementation details scattered across referenced papers. To address this, the authors introduce Executable Knowledge Graphs as a structured, paper-centric knowledge base that automatically pulls in code snippets and technical insights. When plugged into existing agent setups, this representation lets agents retrieve and reuse information at multiple levels of detail. Experiments across three frameworks and two LLMs show clear gains on a dedicated replication benchmark. If the approach holds, it offers a practical way to turn published papers into more reliably executable knowledge.

Core claim

The authors present Executable Knowledge Graphs (xKG) as a pluggable knowledge base that automatically extracts and integrates code snippets together with technical insights from scientific papers. This structured representation supports multi-granular retrieval and reuse, overcoming the shortcomings of standard retrieval-augmented generation that overlooks latent implementation signals. When xKG is added to three different agent frameworks powered by two LLMs, replication performance on PaperBench rises substantially, reaching an improvement of 10.9 percent with one model.

What carries the argument

Executable Knowledge Graph (xKG), a paper-centric knowledge base that automatically extracts and integrates code snippets and technical insights from scientific literature to enable structured retrieval and reuse.

If this is right

  • Adding xKG to agent frameworks raises replication success rates across multiple LLMs and setups.
  • The method supplies missing implementation details that standard RAG approaches cannot capture from referenced papers.
  • xKG works as a general, extensible module that can be attached to different replication agents without redesigning them.
  • By preserving code signals and technical insights in structured form, the graphs enable reuse at varying levels of granularity.

Where Pith is reading between the lines

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

  • The extraction step could be studied to see which sections of papers most often cause replication failures, guiding better reporting standards.
  • Applying similar graphs to fields outside AI might reveal whether the same knowledge-representation issues appear in other experimental sciences.
  • Future agents could use xKG not only for replication but also for detecting inconsistencies between a paper's claims and its released code.

Load-bearing premise

Automatic extraction from papers can reliably pull out the latent code and implementation details needed for successful replication without introducing critical errors or omissions.

What would settle it

Measure replication success rates on the same PaperBench papers when the same agents run with and without the xKG component; a large gap in favor of xKG would support the claim.

Figures

Figures reproduced from arXiv: 2510.17795 by Da Zheng, Huajun Chen, Lanning Wei, Lun Du, Ningyu Zhang, Xuehai Wang, Yujie Luo, Yuqi Zhu, Zhuoyun Yu.

Figure 1
Figure 1. Figure 1: XKG is constructed automatically from arXiv papers and GitHub repositories (Examples at Appendix D). functional correctness of R against an evaluation rubric T . A final Replication Score, S = E(R, T ), quantifies the weighted proportion of criteria met. 2.2 Design Formulation We model XKG as a hierarchical, multi-relational graph XKG = (N , E), which is composed of vari￾ous node types and edge types defin… view at source ↗
Figure 2
Figure 2. Figure 2: Further study on Code Node quality. Knowledge Base DPO Training SubUpdateActivationController optimizer batch size learning rate …… detect FFN activation apply selective mask override sub-layer —————————————————————————————— Algorithm Dummy Implementation —————————————————————————————— Procedure CREATEDUMMYCONTROLLER (hardcode_model) controller ← SubUpdateActivationController(hardcode_model) controller.reg… view at source ↗
Figure 3
Figure 3. Figure 3: Case Study on MU-DPO. A comparison of performance with and without XKG on IterativeAgent. Code, which incorporates code nodes with raw re￾trieved snippets; + Rewrite, using LLM-rewritten executable nodes but omitting the verification step. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average performance gain per paper. More fundamentally, this performance disparity is tied to the paper’s research archetype. analyt￾ical papers, such as MU-DPO(Lee et al., 2024), which synthesize and refine existing techniques, benefit substantially as their components are well￾represented in XKG. Conversely, methodological papers like One-SBI(Glöckler et al., 2024), which introduce fundamentally novel ar… view at source ↗
Figure 5
Figure 5. Figure 5: An example of XKG data storage. Paper Nodes are stored as JSON files, with technique and code nodes embedded as structured dictionaries, where key-value pairs are used to create a one-to-one mapping representing the implementation relationship [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
read the original abstract

Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a pluggable, paper-centric knowledge base that automatically integrates code snippets and technical insights extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code is available at https://github.com/zjunlp/xKG.

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 Executable Knowledge Graphs (xKG) as a pluggable, paper-centric knowledge representation that automatically extracts and integrates code snippets and technical insights from scientific literature. It integrates xKG into three agent frameworks with two LLMs and reports substantial gains on the PaperBench benchmark, including a 10.9% improvement with o3-mini, claiming this demonstrates a general solution for automated AI research replication.

Significance. If the reported gains are shown to be robust, xKG could address key limitations of RAG by capturing latent implementation details, offering an extensible knowledge base for replication tasks. The public code release at https://github.com/zjunlp/xKG is a positive step toward reproducibility.

major comments (2)
  1. [Abstract and experimental evaluation section] Abstract and experimental evaluation section: the headline claim of a 10.9% gain with o3-mini (and gains with the second LLM) on PaperBench is presented without any description of the experimental setup, baseline systems, statistical significance tests, data exclusion criteria, or how code extraction accuracy was measured. This information is load-bearing for the central claim that xKG outperforms prior approaches.
  2. [xKG construction description] xKG construction description: the paper states that xKG 'automatically integrates code snippets and technical insights extracted from scientific literature' but provides no details on the extractor, its handling of ambiguous references, incomplete snippets, cross-paper consistency, or error rates. If extraction systematically omits hyperparameters, data-loading logic, or loss functions, the observed agent performance gains could be artifacts rather than evidence for the proposed representation.
minor comments (1)
  1. The abstract mentions integration into 'three agent frameworks' but does not name them or cite their original papers; adding these references would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and have revised the manuscript to provide the requested clarifications on experimental details and xKG construction.

read point-by-point responses

  1. Referee: [Abstract and experimental evaluation section] Abstract and experimental evaluation section: the headline claim of a 10.9% gain with o3-mini (and gains with the second LLM) on PaperBench is presented without any description of the experimental setup, baseline systems, statistical significance tests, data exclusion criteria, or how code extraction accuracy was measured. This information is load-bearing for the central claim that xKG outperforms prior approaches.

    Authors: We agree that the abstract and experimental evaluation section require additional detail to properly support the reported gains. In the revised manuscript, we have expanded the abstract to briefly outline the experimental setup, including integration into three agent frameworks, use of two LLMs, and evaluation on the PaperBench benchmark. The experimental evaluation section now includes explicit descriptions of the baseline systems (standard RAG-augmented agents and unmodified agent frameworks), statistical significance testing (via paired comparisons), data exclusion criteria (none; the full benchmark was used), and code extraction accuracy measurement (via parsing success rates and manual verification on a sampled subset). These additions contextualize the 10.9% improvement with o3-mini and the gains with the second LLM. revision: yes

  2. Referee: [xKG construction description] xKG construction description: the paper states that xKG 'automatically integrates code snippets and technical insights extracted from scientific literature' but provides no details on the extractor, its handling of ambiguous references, incomplete snippets, cross-paper consistency, or error rates. If extraction systematically omits hyperparameters, data-loading logic, or loss functions, the observed agent performance gains could be artifacts rather than evidence for the proposed representation.

    Authors: We acknowledge that the xKG construction section would benefit from greater elaboration on the extraction process. We have added a new subsection in the revised manuscript that describes the LLM-based extractor pipeline, including steps for resolving ambiguous references via contextual cues and reference list linking, handling of incomplete snippets through text completion or explicit flagging, maintenance of cross-paper consistency via entity resolution and a shared schema, and quantitative error rates from an internal evaluation. We also include a brief discussion of potential omissions (such as hyperparameters or data-loading logic) and argue that the observed gains arise from the structured, executable format of xKG, which enables targeted reuse even when extraction is partial, rather than requiring exhaustive coverage. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results are empirical measurements on external benchmark

full rationale

The paper proposes xKG as a pluggable knowledge base that integrates extracted code snippets and insights, then reports performance gains when integrated into agent frameworks and evaluated on the independent PaperBench benchmark (e.g., 10.9% lift with o3-mini). These outcomes are framed as direct experimental results rather than quantities derived from internal fitted parameters, self-definitions, or load-bearing self-citations. No equations, uniqueness theorems, or ansatzes are invoked that would reduce the central claim to its own inputs by construction. The evaluation relies on external benchmarks and publicly released code, making the derivation self-contained without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Ledger constructed from abstract only; full paper may contain additional implementation-specific parameters or extraction heuristics.

axioms (1)
  • domain assumption Retrieval-augmented generation methods fail to capture latent technical details hidden in referenced papers
    Explicitly stated as a core limitation of existing approaches that xKG is designed to overcome.
invented entities (1)
  • Executable Knowledge Graph (xKG) no independent evidence
    purpose: Pluggable paper-centric knowledge base that integrates code snippets and technical insights for improved retrieval in replication agents
    Newly introduced construct whose construction and integration details are not specified in the abstract.

pith-pipeline@v0.9.0 · 5728 in / 1351 out tokens · 42729 ms · 2026-05-18T05:52:21.555093+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

49 extracted references · 49 canonical work pages · cited by 1 Pith paper · 2 internal anchors

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    If no reference is available, please return None. Please wrap your final answer between two```in the end. Prompt for Extracting Paper Contributions # Task You are provided with the paper titled {title}. Here are the main sections of the paper: {sections}. Furthermore, key equations from the paper are provided to help you understand its specific algorithms...

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    name: str, the name of the component, expressed as a concise and standardized academic term, intended to precisely capture its core identity while facilitating efficient indexing and retrieval from other literature

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    type: str, One of ‘Methodology‘, ‘Technique‘, ‘Finding‘, or ‘Resource‘

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    For implementable items, describe the whole process without missing any critical steps and implementation details

    description: str, A detailed, self-contained explanation of the component, focusing on what it is, how it works, and its purpose. For implementable items, describe the whole process without missing any critical steps and implementation details. For insights, describe the core discovery. Maximize the retention of description and implementation details from...

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    components: List[dict], Optional, If the component is a complex ‘Methodology‘ or ‘Techinque‘ composed of multiple smaller techniques, this field lists its key sub-techniques. Each sub-technique listed here must also be defined separately as a complete technique object following this same JSON schema (with ‘name‘, ‘type‘ and ‘description‘ as dictionary key...

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    The complete file tree of the project: {file_tree}

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    Please wrap your final answer between two```in the end

    The README file of the project: {readme} # Output Create a detailed overview of the project, including: 1.Overview (general information about the project) 2.System Architecture (how the system is designed) 3.Core Features (key functionality) Organize the overview in a clear and structured markdown format. Please wrap your final answer between two```in the...

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    If you can find clear evidence that this repository is the official or direct code implementation of a specific academic paper, return the full title of the paper as a string

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    Please wrap your final answer between two```in the end

    If there is no sufficient evidence to identify a directly corresponding paper (e.g., only general descriptions, multiple papers, or no paper mentioned), return None. Please wrap your final answer between two```in the end. C.3 Knowledge Graph Construction Prompts Prompt for Rewriting a Technique’s Description # Task Your task is to refine and enhance the d...

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    Technical Concept from the paper {paper}: {technique}

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    Avoid using bullet points, numbered lists, or other form of itemization

    Relevant Excerpt of this Technique: {excerpt} # Output Return a precise and comprehensive description, presented as a single, continuous paragraph written in a comprehensive, academic style. Avoid using bullet points, numbered lists, or other form of itemization

    work page

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    DO NOT alter, expand, or reduce the scope of the technique

    Ensure the technique precisely matches the original description. DO NOT alter, expand, or reduce the scope of the technique. Ignore other related techniques and only FOCUS ON this technique

    work page

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    All formulas, parameter configurations, and implementation details must be extracted from the given excerpts, ensuring strict adherence to them

    Strictly adhering to the original description, augment its implementation details based on the provided excerpts. All formulas, parameter configurations, and implementation details must be extracted from the given excerpts, ensuring strict adherence to them. Avoid any summarization, inference, or omission

    work page

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    Your response MUST be based solely on the original description and provided excerpts

    If the excerpts offer no new information, leave the description unchanged. Your response MUST be based solely on the original description and provided excerpts. The inclusion of ANY external information or fabricated details is strictly forbidden!!!

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    Now please think and reason carefully, and wrap your final answer between two```in the end

    Ensure that the provided description is precise, complete, and possesses sufficient detail to correspond to a specific implementation. Now please think and reason carefully, and wrap your final answer between two```in the end. Prompt for Identifying Relevant Code Snippets # Task Your task is to analyze a list of code files retrieved from a GitHub reposito...

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    Overview of the Code repository: {overview}

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    xx", "xx

    Relevant Code Files: {file_snippets} # Output Return a list of filenames formatted as ["xx", "xx", ...], sorted indescendingorder of relevance of the technical concept

    work page

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    Exclude any file not DIRECTLY correspond to the concrete implementation and configurarion of this technique (e.g., tests, documentation, other technique implementation)

    work page

  24. [25]

    If no such implemen- tation can be found, return None

    Confirm that a direct implementation exists within your provided file list. If no such implemen- tation can be found, return None

    work page

  25. [26]

    Now please think and reason carefully, and wrap your final answer between two```in the end

    Return the nitrogen list even if there’s only one file. Now please think and reason carefully, and wrap your final answer between two```in the end. Prompt for Reranking Retrieved Techniques # Task Your task is to analyze a list of technique implementations retrieved from the knowledge base, and identify which techniques are directly relevant to the implem...

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    Technical Concept Definition: {technique}

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    ", ""), (

    Relevant Technique implementations: {relevant_techniques} # Output Return a list of (technique_name, apply_guidance) tuples formatted as [("", ""), ("",""), ...], sorted in descending order of relevance to the technical concept. The guidance should be a short explanation of how the technique applies to the current scenario and what modifications are neede...

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  28. [29]

    Exclude any techniques not relevant to the concrete implementation of this technique

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  29. [30]

    Ensure the returned technique name exactly matches the original one

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  30. [31]

    For technologies with identical core definitions, keep the one whose application is most relevant

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    If no such technique can be found, return None

    work page

  32. [33]

    Now please think and reason carefully, and wrap your final answer between two```in the end

    Return the nitrogen list even if there’s only one relevant technique. Now please think and reason carefully, and wrap your final answer between two```in the end. Prompt for Rewriting Code for a Leaf Technique # Task Your task is to transform a collection of disparate source code snippets, which are the official implementation of a technique component from...

    work page

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    Abstract of the paper {paper}: {abstract}

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    Technical Concept Definition from the paper {paper}: {technique}

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    Relevant Code Files: {file_snippets} # Workflow

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    Focus ONLY on THIS technique, ignoring the mentioned context and related techniques

    Analyze: Understand the technique’s inputs, outputs and workflow from the paper. Focus ONLY on THIS technique, ignoring the mentioned context and related techniques

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    Other mentioned associated techniquesMUST BE IGNORED AND EXCLUDED

    Isolate & Extract: Based on the description of the technique, determine what is its PRECISE role and functionality, and extract ONLY the code you identified as belonging to {technique}. Other mentioned associated techniquesMUST BE IGNORED AND EXCLUDED

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    Refactor: Integrate the extracted code by removing hard-coded values, isolating the core algorithm, and standardizing it with proper documentation and type hints

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    Ensure accuracy and conciseness, avoiding unnecessary output

    Assemble & Test: Build the final script and add an test block as a runnable example. Ensure accuracy and conciseness, avoiding unnecessary output

    work page

  40. [41]

    # Requirements

    Documentation: Write a brief and concise documentation of the code logic, configurable options, and usage in 5-10 sentences. # Requirements

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    Dependency Management: Ensure all necessary imports and dependencies are included at the beginning of the code block

    work page

  42. [43]

    ONLY focus on the implementation that DIRECTLY corresponds to THIS technique!!! (e.g., if the technique is a loss function definition, implement only the code for its calculation

    Fidelity to the Original Technique: Strictly follow the description of the given technique to organize the code. ONLY focus on the implementation that DIRECTLY corresponds to THIS technique!!! (e.g., if the technique is a loss function definition, implement only the code for its calculation. Ignore all other parts of the algorithm’s implementation, even i...

    work page

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    • Every function and class method must include a comprehensive docstring explaining its purpose, parameters, and return values

    Code Encapsulation and Documentation: • Encapsulate the core logic of the technique into one or more functions/classes. • Every function and class method must include a comprehensive docstring explaining its purpose, parameters, and return values. • All function arguments and return values must have clear type hints. • Preserve original parameters and com...

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    • The test case should use parameters from the code repository or paper

    Reproducibility and Testing: • A main execution block, starting with the comment# TEST BLOCK, is required at the end of the file, which serves as a practical usage example and a test case. • The test case should use parameters from the code repository or paper. If missing, create and state your own defaults

    work page

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    Your role is to refactor and structure, not to re-implement or invent new logic

    Fidelity to the Original Logic: • You must strictly adhere to the algorithmic logic present in the provided code snippets. Your role is to refactor and structure, not to re-implement or invent new logic. • Minimal, necessary modifications are permitted (e.g., renaming variables for clarity, adapting function signatures for dependency injection), but the c...

    work page

  46. [47]

    Limit the description to 5-10 clear and coherent sentences

    Documentation of Usage Scenarios: Provide a concise and fluent document of the code module’s core logic, configurable options, and usage. Limit the description to 5-10 clear and coherent sentences. # Output

    work page

  47. [49]

    If the technique requires integration with other modules to constitute a single code module, return None

    Verify that the generated code does not exceed the scope of the technique’s definition. If the technique requires integration with other modules to constitute a single code module, return None. If no direct implementation of the technique is found in the given code snippets, also return None. Now, please proceed with the task, following the workflow and a...

    work page

  48. [50]

    Return an executable code block and a corresponding documentation, each wrapped between two```

    Implement the technique standalone without relying on external, undefined components. Return an executable code block and a corresponding documentation, each wrapped between two```. Example: [... Reasoning Steps ...] ```python [... Core Implementation of the technique ...] [... Ignore other relevant techniques ...] # TEST BLOCK [... Example Usage ...] ```...

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  49. [51]

    ", "

    Verify that the generated code does not exceed the scope of the technique’s definition. If the technique requires integration with other modules to constitute a single code module, return None. If no direct implementation of the technique is found in the given code snippets, also return None. Now, please proceed with the task, following the workflow and a...

    work page