Paper Citation Record · LEDGER

Toward Neurosymbolic Program Comprehension

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2502.01806.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01806 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:26:32.233655Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:34:22.014373Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

47 of 47 outbound references displayed

verified exact3
verified fuzzy32
unresolved12
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External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1732006e-5864-400a-92d4-7854082647d7 · outbound

This paper cites Gpt-4 technical report,.

Toward Neurosymbolic Program Comprehension Gpt-4 technical report,

Reference 1

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Source-reported events for the cited work

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Observation ecef56b3-0994-4fce-baf5-f6cc2cf6c5a2 · outbound

This paper cites Copilot website,.

Toward Neurosymbolic Program Comprehension Copilot website,

Reference 2

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Observation 3d2d0f74-7ccc-4529-8238-9accc0680ac9 · outbound

This paper cites Chatgpt,.

Toward Neurosymbolic Program Comprehension Chatgpt,

Reference 3

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Observation d9fd823e-7574-46fb-b446-6086a1ac45a6 · outbound

This paper cites ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design.

Toward Neurosymbolic Program Comprehension ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 4

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Observation a2da1550-3463-43d3-88b2-d6f4f6194720 · outbound

This paper cites Retrieval-based prompt selection for code-related few- shot learning,.

Toward Neurosymbolic Program Comprehension Retrieval-based prompt selection for code-related few- shot learning,

Reference 5

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Observation 0ae5cfbe-55df-44eb-96b8-9b5822e7950a · outbound

This paper cites On the use of chatgpt for code review: Do devel- opers like reviews by chatgpt?.

Toward Neurosymbolic Program Comprehension On the use of chatgpt for code review: Do devel- opers like reviews by chatgpt?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:26:33.091726Z

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Observation e453acae-3705-4bcd-99b5-2576b2fdcf30 · outbound

This paper cites Beyond code generation: An observational study of chatgpt usage in software engineering practice,.

Toward Neurosymbolic Program Comprehension Beyond code generation: An observational study of chatgpt usage in software engineering practice,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:26:33.076704Z

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Observation 85492995-5f4b-44ea-abee-82967fa37399 · outbound

This paper cites Github copilot ai pair programmer: Asset or liability?.

Toward Neurosymbolic Program Comprehension Github copilot ai pair programmer: Asset or liability?

Reference 8

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This paper cites Large language models for software engineering: A sys- tematic literature review,.

Toward Neurosymbolic Program Comprehension Large language models for software engineering: A sys- tematic literature review,

Reference 9

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Observation 7fd04987-ae95-4c85-8e44-276a20310e21 · outbound

This paper cites Towards greener llms: Bringing energy-efficiency to the forefront of llm inference,.

Toward Neurosymbolic Program Comprehension Towards greener llms: Bringing energy-efficiency to the forefront of llm inference,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:26:33.037197Z

Source-reported events for the cited work

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Observation 73549ae8-b156-4366-b838-528b5ee46227 · outbound

This paper cites Will we run out of data? Limits of LLM scaling based on human-generated data.

Toward Neurosymbolic Program Comprehension Will we run out of data? Limits of LLM scaling based on human-generated data

Reference 11

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Source-reported events for the cited work

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Observation ba1ccdd6-8fd2-4636-bccb-74ada5e68cb3 · outbound

This paper cites Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,.

Toward Neurosymbolic Program Comprehension Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,

Reference 12

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Observation 07aa61c6-3fc0-489d-b6d7-f32e608fdf82 · outbound

This paper cites A unified approach to interpreting model predictions,.

Toward Neurosymbolic Program Comprehension A unified approach to interpreting model predictions,

Reference 13

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Source-reported events for the cited work

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Observation daec273a-2530-4251-b6ca-e1b1dfe6bb0e · outbound

This paper cites A value for n-person games,.

Toward Neurosymbolic Program Comprehension A value for n-person games,

Reference 14

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Observation 5234e0c3-c916-44a7-a180-a27125e5b5d3 · outbound

This paper cites Problems with shapley-value-based explanations as feature importance measures,.

Toward Neurosymbolic Program Comprehension Problems with shapley-value-based explanations as feature importance measures,

Reference 15

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Source-reported events for the cited work

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Observation 9b771afc-d0fb-4211-bb71-8c776b6c4a67 · outbound

This paper cites WWW: A unified framework for explaining what, where and why of neural networks by interpretation of neuron con- cepts,.

Toward Neurosymbolic Program Comprehension WWW: A unified framework for explaining what, where and why of neural networks by interpretation of neuron con- cepts,

Reference 16

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Source-reported events for the cited work

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Observation 0a62e6a3-c088-4761-ae74-1152a49d6fa1 · outbound

This paper cites The many shapley values for model explana- tion,.

Toward Neurosymbolic Program Comprehension The many shapley values for model explana- tion,

Reference 17

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Source-reported events for the cited work

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Observation 4e4b3e60-30ae-4fb9-ac36-f4e8f021980c · outbound

This paper cites Interpretability of machine learning-based prediction models in healthcare,.

Toward Neurosymbolic Program Comprehension Interpretability of machine learning-based prediction models in healthcare,

Reference 18

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Source-reported events for the cited work

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This paper cites Navigating the Complexities of AI: The Critical Role of Interpretability and Explainability in Ensuring Transparency and Trust,.

Toward Neurosymbolic Program Comprehension Navigating the Complexities of AI: The Critical Role of Interpretability and Explainability in Ensuring Transparency and Trust,

Reference 19

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Observation 3a5be5b5-5ef3-407c-b68d-a9f9d72d8dfc · outbound

This paper cites Designing and Interpreting Probes with Control Tasks,.

Toward Neurosymbolic Program Comprehension Designing and Interpreting Probes with Control Tasks,

Reference 20

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Observation d66a9432-32db-48b8-97c7-118fcb932b36 · outbound

This paper cites Ast-probe: Recovering abstract syntax trees from hidden representations of pre-trained language models,.

Toward Neurosymbolic Program Comprehension Ast-probe: Recovering abstract syntax trees from hidden representations of pre-trained language models,

Reference 21

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Observation 59e98c08-8725-45e1-b247-58fcac5569da · outbound

This paper cites Probing pretrained models of source codes,.

Toward Neurosymbolic Program Comprehension Probing pretrained models of source codes,

Reference 22

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Toward Neurosymbolic Program Comprehension Probing classifiers: Promises, shortcomings, and ad- vances,

Reference 23

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Observation c5b56676-4cbf-4126-852b-3822955484a6 · outbound

This paper cites Which syntactic capabilities are statistically learned by masked language models for code?.

Toward Neurosymbolic Program Comprehension Which syntactic capabilities are statistically learned by masked language models for code?

Reference 24

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Observation d0c9584e-ee5a-426a-9274-ecd5d60b8b07 · outbound

This paper cites Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations,.

Toward Neurosymbolic Program Comprehension Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations,

Reference 25

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This paper cites Toward a Theory of Causation for Interpreting Neural Code Models ,.

Toward Neurosymbolic Program Comprehension Toward a Theory of Causation for Interpreting Neural Code Models ,

Reference 26

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Observation efb072cc-def6-45c2-a1ed-0b43484b4c00 · outbound

This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

Toward Neurosymbolic Program Comprehension CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 27

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This paper cites Bert-based github issue report classification,.

Toward Neurosymbolic Program Comprehension Bert-based github issue report classification,

Reference 28

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This paper cites Bert for sentiment classification in software engineering,.

Toward Neurosymbolic Program Comprehension Bert for sentiment classification in software engineering,

Reference 29

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Toward Neurosymbolic Program Comprehension Using bert to predict bug-fixing time,

Reference 30

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This paper cites Using a nearest-neighbour, bert-based approach for scalable clone detection,.

Toward Neurosymbolic Program Comprehension Using a nearest-neighbour, bert-based approach for scalable clone detection,

Reference 31

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Observation b172cd1f-b723-4234-82f6-b52d55f617fa · outbound

This paper cites Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks.

Toward Neurosymbolic Program Comprehension Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 32

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This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Toward Neurosymbolic Program Comprehension CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 33

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This paper cites CAT-probing: A metric-based approach to interpret how pre-trained models for programming language attend code structure,.

Toward Neurosymbolic Program Comprehension CAT-probing: A metric-based approach to interpret how pre-trained models for programming language attend code structure,

Reference 34

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This paper cites A Critical Study of What Code-LLMs (Do Not) Learn,.

Toward Neurosymbolic Program Comprehension A Critical Study of What Code-LLMs (Do Not) Learn,

Reference 35

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This paper cites AutoFocus: Interpreting attention-based neural networks by code perturbation,.

Toward Neurosymbolic Program Comprehension AutoFocus: Interpreting attention-based neural networks by code perturbation,

Reference 36

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Observation 21584b34-a13e-4903-9407-959086ec5683 · outbound

This paper cites Looking into Black Box Code Language Models.

Toward Neurosymbolic Program Comprehension Looking into Black Box Code Language Models

Reference 37

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This paper cites DeepCodeProbe: Towards Understanding What Models Trained on Code Learn.

Toward Neurosymbolic Program Comprehension DeepCodeProbe: Towards Understanding What Models Trained on Code Learn

Reference 38

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This paper cites Enhancing SQL Query Generation with Neurosymbolic Reasoning.

Toward Neurosymbolic Program Comprehension Enhancing SQL Query Generation with Neurosymbolic Reasoning

Reference 39

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This paper cites Ns3: neuro-symbolic semantic code search,.

Toward Neurosymbolic Program Comprehension Ns3: neuro-symbolic semantic code search,

Reference 40

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Observation 06ec8b85-189d-4096-b5fd-0b5c52e59b08 · outbound

This paper cites CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution.

Toward Neurosymbolic Program Comprehension CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution

Reference 41

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This paper cites Neuro-Symbolic Program Synthesis.

Toward Neurosymbolic Program Comprehension Neuro-Symbolic Program Synthesis

Reference 42

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This paper cites Programming with a differentiable forth interpreter,.

Toward Neurosymbolic Program Comprehension Programming with a differentiable forth interpreter,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:26:32.689801Z

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This paper cites Learning continuous semantic representations of symbolic expressions,.

Toward Neurosymbolic Program Comprehension Learning continuous semantic representations of symbolic expressions,

Reference 44

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Observation c469bff0-2689-42b6-bf51-18bd2146a201 · outbound

This paper cites An interpretable error correction method for enhancing code-to-code translation,.

Toward Neurosymbolic Program Comprehension An interpretable error correction method for enhancing code-to-code translation,

Reference 45

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Observation dcb59237-1345-48a3-8845-9324387de73e · outbound

This paper cites Semantic Code Repair using Neuro-Symbolic Transformation Networks.

Toward Neurosymbolic Program Comprehension Semantic Code Repair using Neuro-Symbolic Transformation Networks

Reference 46

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This paper cites Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar.

Toward Neurosymbolic Program Comprehension Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar

Reference 47

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Pith citing papers

Observation 2dbc3f5a-3f46-408b-b4a0-69202c2fdd76 · inbound

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization cites this paper.

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization Toward Neurosymbolic Program Comprehension

Reference 58

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Observation 138eb725-05a6-4027-9857-2f63ce48527d · inbound

Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures cites this paper.

Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures Toward Neurosymbolic Program Comprehension

Reference 60

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