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Paper Citation Record · LEDGER

Evaluating Fuzz Testing for Reinforcement Learning Agents

As of 8 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2607.24577.

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

pith.paper-citation-record.v1
2607.24577 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T11:28:32.043923Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

68 of 68 outbound references displayed

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Outbound references

Observation 44ffe074-d3e0-4243-9af4-f154e92f16a1 · outbound

This paper cites A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering,.

Evaluating Fuzz Testing for Reinforcement Learning Agents A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering,

Reference 1

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Observation c9c60b79-1ead-4537-a9fa-4379800ce4f4 · outbound

This paper cites The pursuit of diversity: Multi-objective testing of deep rein- forcement learning agents,.

Evaluating Fuzz Testing for Reinforcement Learning Agents The pursuit of diversity: Multi-objective testing of deep rein- forcement learning agents,

Reference 2

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Observation cd49a837-4d7e-48ac-9f1c-739209c50e6d · outbound

This paper cites Reinforcement learning: An introduction. by richard’s sutton,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Reinforcement learning: An introduction. by richard’s sutton,

Reference 3

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Observation 50b002bf-2096-4071-bab4-4a0563eb1c5e · outbound

This paper cites Controlling the false discovery rate: a practical and powerful approach to multiple testing,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Controlling the false discovery rate: a practical and powerful approach to multiple testing,

Reference 4

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Observation 764c90f8-bb5a-47c0-96c5-02020b452ae8 · outbound

This paper cites Testing the plasticity of re- inforcement learning-based systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Testing the plasticity of re- inforcement learning-based systems,

Reference 5

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Observation 0a97dd8b-bd51-488e-9b54-ee9657ce6573 · outbound

This paper cites Testing of deep reinforcement learning agents with surrogate models,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Testing of deep reinforcement learning agents with surrogate models,

Reference 6

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Observation 67588500-505d-4135-a408-d8f3efac3be6 · outbound

This paper cites Coverage- based greybox fuzzing as markov chain,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Coverage- based greybox fuzzing as markov chain,

Reference 7

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Observation a426def3-8eb5-442d-9a5f-1c5b5d82b7ad · outbound

This paper cites OpenAI Gym.

Evaluating Fuzz Testing for Reinforcement Learning Agents OpenAI Gym

Reference 8

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Observation 97db9a99-b1be-438b-8d19-345fec9bb63c · outbound

This paper cites Exploration by random network distillation,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Exploration by random network distillation,

Reference 9

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Observation c900bdfd-80f8-4fb5-9d2d-0bb9545fad45 · outbound

This paper cites Drlfailuremon- itor: A dynamic failure monitoring approach for deep reinforcement learning system,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Drlfailuremon- itor: A dynamic failure monitoring approach for deep reinforcement learning system,

Reference 10

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Observation af0ddf2b-61f8-4916-bd49-6383a8c6434f · outbound

This paper cites Cohen,Statistical Power Analysis for the Behavioral Sciences, 2nd ed.

Evaluating Fuzz Testing for Reinforcement Learning Agents Cohen,Statistical Power Analysis for the Behavioral Sciences, 2nd ed

Reference 11

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Observation 3db02aee-735d-4bfe-beb6-60971a3b24fa · outbound

This paper cites Rank-biserial correlation,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Rank-biserial correlation,

Reference 12

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Observation 22b6b17f-cca1-4294-8959-a8cc9c677fea · outbound

This paper cites CARLA: an open urban driving simulator,.

Evaluating Fuzz Testing for Reinforcement Learning Agents CARLA: an open urban driving simulator,

Reference 13

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Observation 54fa5259-8586-4778-a32b-a33f2e67dc9d · outbound

This paper cites Prioritized replay for RL post-training,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Prioritized replay for RL post-training,

Reference 14

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Observation 3ebcb2d1-b1f0-4fe6-b42a-2a385576d200 · outbound

This paper cites Reinforcement learning for online testing of autonomous driving systems: a replication and extension study,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Reinforcement learning for online testing of autonomous driving systems: a replication and extension study,

Reference 15

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Observation 6b44c7e2-d5ed-4277-8b39-0a5a14a70da9 · outbound

This paper cites Towards comprehensive testing on the robustness of co- operative multi-agent reinforcement learning,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Towards comprehensive testing on the robustness of co- operative multi-agent reinforcement learning,

Reference 16

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Observation 4629f195-f1d2-4aed-9226-5068c5b7b562 · outbound

This paper cites Many-objective reinforcement learning for online testing of dnn-enabled systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Many-objective reinforcement learning for online testing of dnn-enabled systems,

Reference 17

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Observation 6404fb14-7b42-4ad9-8cd6-c180e9105f80 · outbound

This paper cites Curiosity-driven testing for sequen- JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 13 tial decision-making process,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Curiosity-driven testing for sequen- JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 13 tial decision-making process,

Reference 18

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Observation 492f262a-ce5d-48e3-b8af-3f314b1ae917 · outbound

This paper cites Deep reinforcement learning for drone navigation using sensor data,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Deep reinforcement learning for drone navigation using sensor data,

Reference 19

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Observation 995f34dc-4256-43c4-a7fc-3118b9a1c41e · outbound

This paper cites an unresolved cited work.

Evaluating Fuzz Testing for Reinforcement Learning Agents Unresolved cited work

Reference 20

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Observation 38e8e80d-e627-4adf-a42a-cfc6c44fb89e · outbound

This paper cites A novel DDPG method with prioritized experience replay,.

Evaluating Fuzz Testing for Reinforcement Learning Agents A novel DDPG method with prioritized experience replay,

Reference 21

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Observation d7dae4ec-ee36-4f7a-92ab-2b6d79324a6e · outbound

This paper cites Carl: Learning scalable plan- ning policies with simple rewards,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Carl: Learning scalable plan- ning policies with simple rewards,

Reference 22

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Observation 3ded0e88-f6af-4181-ba80-cffaf8cdbd8f · outbound

This paper cites Residual reinforcement learning for robot control,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Residual reinforcement learning for robot control,

Reference 23

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Observation 1290b556-73be-4ae6-9c5a-34fe8033f748 · outbound

This paper cites Concept bottleneck models,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Concept bottleneck models,

Reference 24

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Observation 914bab9f-8bde-41b1-93d5-9e2eecf07b29 · outbound

This paper cites Anatomy of a robotaxi crash: Lessons from the cruise pedestrian dragging mishap,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Anatomy of a robotaxi crash: Lessons from the cruise pedestrian dragging mishap,

Reference 25

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Observation 766340c1-a397-40cc-b3a3-a0f0018b1a84 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Evaluating Fuzz Testing for Reinforcement Learning Agents Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 26

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Observation aadb2ca4-271d-4f97-a43e-65a79eac7306 · outbound

This paper cites Faster diffusion: Rethinking the role of the encoder for diffusion model inference,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Faster diffusion: Rethinking the role of the encoder for diffusion model inference,

Reference 27

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Observation c0aeab49-5eac-4564-ac4d-c55266fe1c2a · outbound

This paper cites Agentfuzz: Fuzzing for deep reinforcement learning systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Agentfuzz: Fuzzing for deep reinforcement learning systems,

Reference 28

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Observation 5c3aa81b-cb94-43c6-8af5-6b8d1c060b80 · outbound

This paper cites Generative model-based testing on decision-making policies,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Generative model-based testing on decision-making policies,

Reference 29

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Observation a8c1611c-3a85-4c7c-99f3-ce7a7858495f · outbound

This paper cites Todynet: temporal dynamic graph neural network for multivariate time series classification,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Todynet: temporal dynamic graph neural network for multivariate time series classification,

Reference 30

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source=pdf_text observed=2026-07-31T11:28:31.928533Z digest=sha256:898d4b643e1e9e3ae9ccb6430773a11e7b82b2984b119ceda4271c4db005b654

Observation 87b90e74-70a7-421b-87d2-1abcf9db6afc · outbound

This paper cites Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions.

Evaluating Fuzz Testing for Reinforcement Learning Agents Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions

Reference 31

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Observation 905a8932-77b6-4046-bd20-3816d7d9309b · outbound

This paper cites Enhancing multi-agent system testing with diversity-guided exploration and adaptive critical state exploitation,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Enhancing multi-agent system testing with diversity-guided exploration and adaptive critical state exploitation,

Reference 32

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source=pdf_text observed=2026-07-31T11:28:31.934801Z digest=sha256:0e7fe847fb7e06f99124e91f14142e5527b89ac5b1b3f55a98adbfac58e5f07f

Observation 7bfe0579-bb57-4744-a016-cc123db91682 · outbound

This paper cites Fault diversity in reinforcement learning policy testing,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Fault diversity in reinforcement learning policy testing,

Reference 33

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source=pdf_text observed=2026-07-31T11:28:31.937578Z digest=sha256:ab9fa504ba91ad9e9c0dec8cb3846d719494bee937810a7ce6f1f32118dc8d27

Observation b7147398-87de-40c3-9837-6e605891cc05 · outbound

This paper cites Policy testing with mdpfuzz (replicability study),.

Evaluating Fuzz Testing for Reinforcement Learning Agents Policy testing with mdpfuzz (replicability study),

Reference 34

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source=pdf_text observed=2026-07-31T11:28:31.941052Z digest=sha256:76ede6e6f27695af15dca032c1c3bd1f2cdb970c5d03cc10098b2b501c721169

Observation 4100263b-55d4-4eeb-873f-968ab2419a96 · outbound

This paper cites Learning, reward, and decision making,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Learning, reward, and decision making,

Reference 35

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source=pdf_text observed=2026-07-31T11:28:31.944422Z digest=sha256:e3765de50d9b3e6570d3ca3b8c89fea3198a482d5e4d150a54cbc3234bb13c16

Observation d07ee855-edd2-493b-95c4-c5f7ca847330 · outbound

This paper cites Mdpfuzz: testing models solving markov decision processes,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Mdpfuzz: testing models solving markov decision processes,

Reference 36

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source=pdf_text observed=2026-07-31T11:28:31.947793Z digest=sha256:2417e956fafbda6a9602968fffd95faf328086c8c4dc4e9aa5e1f92def91cf8a

Observation 27526193-f84b-401e-841d-4c3ebbda94dd · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Pytorch: An imperative style, high-performance deep learning library,

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source=pdf_text observed=2026-07-31T11:28:31.950719Z digest=sha256:6b768691ddc037ef7263e7a79621463f2cbfa688506cb9d127c199ff56e90183

Observation 8b19e5bf-fbd4-4dab-a630-23a5f8519b73 · outbound

This paper cites Deepxplore: automated whitebox testing of deep learning systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Deepxplore: automated whitebox testing of deep learning systems,

Reference 38

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source=pdf_text observed=2026-07-31T11:28:31.953512Z digest=sha256:b504e352211b8b5f597e4076300d93479b468f6771479cdf76e480f5554bd302

Observation 373b76dc-e8ce-42e2-b216-7f588ed9cd01 · outbound

This paper cites Learning and testing resilience in cooperative multi-agent systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Learning and testing resilience in cooperative multi-agent systems,

Reference 39

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Observation da0b4815-398e-40fc-a881-c2a1d8861be4 · outbound

This paper cites an unresolved cited work.

Evaluating Fuzz Testing for Reinforcement Learning Agents Unresolved cited work

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Observation 7847a494-7ec7-47d6-bf0d-a74e2fd7d2e9 · outbound

This paper cites Rl baselines3 zoo,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Rl baselines3 zoo,

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Observation 5f8c947c-dd42-4d59-a464-79f07c752f27 · outbound

This paper cites Stable-baselines3: Reliable reinforce- ment learning implementations,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Stable-baselines3: Reliable reinforce- ment learning implementations,

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Observation f5bb2e66-c7a3-4552-8fda-769e96ba4333 · outbound

This paper cites Vuzzer: Application-aware evolutionary fuzzing,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Vuzzer: Application-aware evolutionary fuzzing,

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Observation 3531f5c8-04b1-4b63-a906-47cad1f57a25 · outbound

This paper cites Prior- itized experience replay,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Prior- itized experience replay,

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source=pdf_text observed=2026-07-31T11:28:31.970910Z digest=sha256:f3459050ec973e20e5939bf5eb684b3eb0488088b8387a23248ba238433c2020

Observation 08f3b321-4ad5-4fe5-820f-45254cae9e74 · outbound

This paper cites Testing rein- forcement learning systems: A comprehensive review,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Testing rein- forcement learning systems: A comprehensive review,

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Observation 7b324204-5d62-413f-92b0-bc1363edbaa0 · outbound

This paper cites Search-based testing of reinforcement learning,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Search-based testing of reinforcement learning,

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Observation f8de37ae-c58d-4516-88b6-72f5a3a17675 · outbound

This paper cites Learning and repair of deep reinforce- ment learning policies from fuzz-testing data,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Learning and repair of deep reinforce- ment learning policies from fuzz-testing data,

Reference 47

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source=pdf_text observed=2026-07-31T11:28:31.979989Z digest=sha256:647dca3e184c4070835104fbf0b49677face585378e25c68bf653fe1b93105df

Observation 0e13f864-ed5a-4117-b0d8-29bc8a7bac3d · outbound

This paper cites PCLA: A framework for testing autonomous agents in the CARLA simulator,.

Evaluating Fuzz Testing for Reinforcement Learning Agents PCLA: A framework for testing autonomous agents in the CARLA simulator,

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Observation bf78c95f-ff79-4c9c-abe3-03dc3096903f · outbound

This paper cites $\mu \text{PRL}$: A mutation testing pipeline for deep rein- forcement learning based on real faults,.

Evaluating Fuzz Testing for Reinforcement Learning Agents $\mu \text{PRL}$: A mutation testing pipeline for deep rein- forcement learning based on real faults,

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Observation 5432d385-0a19-41c6-bf9f-e07e27419d33 · outbound

This paper cites Does neuron coverage matter for deep reinforcement learning?: A preliminary JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 14 study,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Does neuron coverage matter for deep reinforcement learning?: A preliminary JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 14 study,

Reference 50

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Observation a5bb55b5-45e5-46f9-8a78-393c41404bfa · outbound

This paper cites Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures,

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Observation bc87c4b4-ffb7-4cd1-9e90-c05fe99ed2b6 · outbound

This paper cites A Survey of Reinforcement Learning for Software Engineering.

Evaluating Fuzz Testing for Reinforcement Learning Agents A Survey of Reinforcement Learning for Software Engineering

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Observation 93072774-5721-4e15-80bf-762beaec7ca3 · outbound

This paper cites Fuzzing with sequence diversity inference for sequential decision- making model testing,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Fuzzing with sequence diversity inference for sequential decision- making model testing,

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Observation 92c02899-5335-4e8c-8f17-3c4a1857fdf5 · outbound

This paper cites Wilcoxon signed-rank test,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Wilcoxon signed-rank test,

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Observation c79987c6-a72b-4d7a-a340-000b31aa7002 · outbound

This paper cites Regression fault detection and mitigation in the evolution of deep learning systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Regression fault detection and mitigation in the evolution of deep learning systems,

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Observation ce60d6f8-9e0b-4c61-b7ed-6d1fe8e12891 · outbound

This paper cites Regression fuzzing for deep learning systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Regression fuzzing for deep learning systems,

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Observation b1a82dfe-93ab-40cf-ae69-0fe4a17d2bac · outbound

This paper cites Mitigating regression faults induced by feature evolution in deep learning systems,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Mitigating regression faults induced by feature evolution in deep learning systems,

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Observation b7944f7f-7ef3-4dac-9072-78b81813806a · outbound

This paper cites A comprehensive study of deep learning model fixing approaches,.

Evaluating Fuzz Testing for Reinforcement Learning Agents A comprehensive study of deep learning model fixing approaches,

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Observation 7bcdd5fd-b297-4fb6-a408-77f48772b196 · outbound

This paper cites Navigating the testing of evolving deep learning systems: An exploratory interview study,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Navigating the testing of evolving deep learning systems: An exploratory interview study,

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Observation 0427b5c4-991c-46e6-a128-9b48ebceb815 · outbound

This paper cites A white-box testing for deep neural networks based on neuron coverage,.

Evaluating Fuzz Testing for Reinforcement Learning Agents A white-box testing for deep neural networks based on neuron coverage,

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Observation 4db4486a-35ca-4156-89db-3d4b600c2118 · outbound

This paper cites End-to-end urban driving by imitating a reinforcement learning coach,.

Evaluating Fuzz Testing for Reinforcement Learning Agents End-to-end urban driving by imitating a reinforcement learning coach,

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Observation 536228ae-2ba2-4e83-b993-422e8b79c748 · outbound

This paper cites Iden- tifying the failure-revealing test cases in metamorphic testing: A statistical approach,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Iden- tifying the failure-revealing test cases in metamorphic testing: A statistical approach,

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Observation a2af511f-0e1c-4213-aa1c-807c0a7c7e14 · outbound

This paper cites Parallel test prioritiza- tion,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Parallel test prioritiza- tion,

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Observation 1854a9fb-cb38-4bdd-ab07-67453af9713b · outbound

This paper cites Knowledge transfer from simple to complex: A safe and efficient reinforcement learning framework for au- tonomous driving decision-making,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Knowledge transfer from simple to complex: A safe and efficient reinforcement learning framework for au- tonomous driving decision-making,

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Observation dacbe2dd-2c07-4428-9f58-ba158a1ba7d0 · outbound

This paper cites Robustness testing for multi-agent reinforcement learning: State perturbations on critical agents,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Robustness testing for multi-agent reinforcement learning: State perturbations on critical agents,

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source=pdf_text observed=2026-07-31T11:28:32.035433Z digest=sha256:e03fd9f7252b98d9d4f800fa88d3a540512d4281435d5bc24677ab133ca65e6e

Observation aa2814de-d5fb-42c6-b546-8f14d41c90f5 · outbound

This paper cites Fuzzing: A survey for roadmap,.

Evaluating Fuzz Testing for Reinforcement Learning Agents Fuzzing: A survey for roadmap,

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Observation 71bf83fc-0f88-423c-8889-c4a6f10c33a9 · outbound

This paper cites A search-based testing approach for deep reinforcement learning agents,.

Evaluating Fuzz Testing for Reinforcement Learning Agents A search-based testing approach for deep reinforcement learning agents,

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Observation 009d053d-2978-44d0-8016-9ebc9a7a200a · outbound

This paper cites SMARLA: A safety monitoring approach for deep reinforcement learning agents,.

Evaluating Fuzz Testing for Reinforcement Learning Agents SMARLA: A safety monitoring approach for deep reinforcement learning agents,

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