Paper Citation Record · LEDGER

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.07029.

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

pith.paper-citation-record.v1
2607.07029 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact15
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch14

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation caf5a865-d208-4c04-b425-e0261c1c35ef · outbound

This paper cites Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.385392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da82d4c8-effa-4e82-9a23-8f19c921ba11 · outbound

This paper cites The Oracle Problem in Software Testing: A Survey.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies The Oracle Problem in Software Testing: A Survey

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.234045Z

Source-reported events for the cited work

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Observation c9bd782a-8fe7-465d-a784-10ee201663e8 · outbound

This paper cites Deep Surrogate Assisted Generation of Environments.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Deep Surrogate Assisted Generation of Environments

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.393853Z

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Observation ad71c645-281a-4253-9ba6-14701466f238 · outbound

This paper cites OpenAI Gym.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies OpenAI Gym

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.371786Z

Source-reported events for the cited work

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Observation 260e7ecf-0d82-4930-ac1c-e94382ba3244 · outbound

This paper cites an unresolved cited work.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Unresolved cited work

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.228159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9db4714-4a03-41a6-940b-45aa808469c3 · outbound

This paper cites Available: https://doi.org/10.1145/3143561.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Available: https://doi.org/10.1145/3143561

Reference 6

Resolution: verified exact; doi, observed 2026-07-09T21:36:34.230253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74af621a-5a90-48f4-9725-bdb20b6a7b37 · outbound

This paper cites Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.391347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f87c555-8002-4fec-b3a0-d87b9a42259a · outbound

This paper cites Validating a Deep Learning Framework by Metamorphic Testing.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Validating a Deep Learning Framework by Metamorphic Testing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:36:35.028120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f89bbb25-9e32-4dcd-972b-1a3a073b1c7a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Explaining and Harnessing Adversarial Examples

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.379788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81b76868-21e1-4f39-b1e2-c040f20c627c · outbound

This paper cites Mungojerrie: Reinforcement Learning of Linear-Time Objectives.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Mungojerrie: Reinforcement Learning of Linear-Time Objectives

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.371123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8727732-ddb6-49ca-a3c9-82873016dadb · outbound

This paper cites In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.218558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6240d8a6-600c-4a10-86d1-204d47bdf497 · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Adversarial Attacks on Neural Network Policies

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.384909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 806f99fd-23ea-4527-a2ff-46babc82f411 · outbound

This paper cites Delving into adversarial attacks on deep policies.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Delving into adversarial attacks on deep policies

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.382970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0aef7773-2766-4344-a304-8def45c8ee33 · outbound

This paper cites Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.387878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 364fac91-eafb-48a0-91c4-6aece6e37ba9 · outbound

This paper cites Quentin Mazouni, Helge Spieker, Arnaud Gotlieb, and Mathieu Acher.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Quentin Mazouni, Helge Spieker, Arnaud Gotlieb, and Mathieu Acher

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-09T21:36:34.197901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16fa1f24-6c1d-4891-82fb-94e7a182b0ff · outbound

This paper cites A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.225208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c007bb95-7da6-474c-8b51-c277a8ee37c0 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Playing Atari with Deep Reinforcement Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.390202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c9c20db-5b1f-4ed8-bfa3-a2caab0397c4 · outbound

This paper cites Kaiser, Lifeng Hu, and Leon Wu.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Kaiser, Lifeng Hu, and Leon Wu

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:36:35.025871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 606e7017-ebf9-4103-b221-6a8e70609cde · outbound

This paper cites Kohei Ohashi, Kosuke Nakanishi, Wataru Sasaki, Yuji Yasui, and Shin Ishii.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Kohei Ohashi, Kosuke Nakanishi, Wataru Sasaki, Yuji Yasui, and Shin Ishii

Reference 21

Resolution: verified exact; doi, observed 2026-07-09T21:36:34.208729Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 566324ae-41dd-465a-85ab-2f9823eb4094 · outbound

This paper cites GPT-4 Technical Report.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies GPT-4 Technical Report

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.365647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e66f82f5-c027-4146-abc2-1d91041a53c8 · outbound

This paper cites Justin K.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Justin K

Reference 23

Resolution: verified exact; doi, observed 2026-07-09T21:36:34.232201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d689eb50-3447-4432-b039-797872be9976 · outbound

This paper cites K., Soros, L.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies K., Soros, L

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.221579Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1af89e23-6334-423f-9196-6b5240957e03 · outbound

This paper cites 2014.2372785.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies 2014.2372785

Reference 25

Resolution: malformed identifier; doi_truncated, observed 2026-07-09T21:36:34.206494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7bcc971-a0fc-4772-a36a-e1aaec02c258 · outbound

This paper cites doi: 10.1016/j.jss.2020.110574.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies doi: 10.1016/j.jss.2020.110574

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-09T21:36:34.231270Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17a65687-c646-4eca-8e25-c36f6ce9c348 · outbound

This paper cites doi: 10.1145/3679006.3685071.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies doi: 10.1145/3679006.3685071

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-09T21:36:34.215121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7fed9421-a676-400b-96cc-aed92b03ce9e · outbound

This paper cites Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.387491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15eacb63-33c4-437b-8d85-e5d865200b57 · outbound

This paper cites https://doi.org/10.1109/fg.2018.00021.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies https://doi.org/10.1109/fg.2018.00021

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.224275Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d08c3bda-34a5-4565-84f1-144d0ff5137c · outbound

This paper cites Intriguing properties of neural networks.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Intriguing properties of neural networks

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.392533Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78baf1f1-5cb3-4047-af45-f1d504b5e563 · outbound

This paper cites Kazuma Tsuji, Ken’ichiro Tanaka, and Sebastian Pokutta.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Kazuma Tsuji, Ken’ichiro Tanaka, and Sebastian Pokutta

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.382598Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a407fc0-4fa5-420f-bacb-ecb72d61997a · outbound

This paper cites URLhttps://doi.org/10.1093/comjnl/25.4.465.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies URLhttps://doi.org/10.1093/comjnl/25.4.465

Reference 33

Resolution: verified exact; doi, observed 2026-07-09T21:36:34.237096Z

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Observation 8bf67191-4491-4e86-bf54-e32852da6ae5 · outbound

This paper cites MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.228213Z

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Observation d9ebfa90-e119-4415-af00-de9cbd345d38 · outbound

This paper cites doi: 10.1109/TSE.2023.3269804.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies doi: 10.1109/TSE.2023.3269804

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T21:36:34.211970Z

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Pith citing papers

No inbound Pith citation observations are available.