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Paper Citation Record · LEDGER
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.
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Source: paper_references, paper_reference_links, observed 2026-07-31T11:28:32.043923Z
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
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68 of 68 outbound references displayed
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Observation 44ffe074-d3e0-4243-9af4-f154e92f16a1 · outbound
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
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
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
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
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
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
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
Evaluating Fuzz Testing for Reinforcement Learning Agents OpenAI Gym
Reference 8
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Observation 97db9a99-b1be-438b-8d19-345fec9bb63c · outbound
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
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
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Rank-biserial correlation,
Reference 12
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Observation 22b6b17f-cca1-4294-8959-a8cc9c677fea · outbound
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
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
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
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
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
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
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Unresolved cited work
Reference 20
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Observation 38e8e80d-e627-4adf-a42a-cfc6c44fb89e · outbound
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
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
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Concept bottleneck models,
Reference 24
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Observation 914bab9f-8bde-41b1-93d5-9e2eecf07b29 · outbound
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
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
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
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
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Todynet: temporal dynamic graph neural network for multivariate time series classification,
Reference 30
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Observation 87b90e74-70a7-421b-87d2-1abcf9db6afc · outbound
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
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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Observation 7bfe0579-bb57-4744-a016-cc123db91682 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Fault diversity in reinforcement learning policy testing,
Reference 33
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Observation b7147398-87de-40c3-9837-6e605891cc05 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Policy testing with mdpfuzz (replicability study),
Reference 34
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Observation 4100263b-55d4-4eeb-873f-968ab2419a96 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Learning, reward, and decision making,
Reference 35
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Observation d07ee855-edd2-493b-95c4-c5f7ca847330 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Mdpfuzz: testing models solving markov decision processes,
Reference 36
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Observation 27526193-f84b-401e-841d-4c3ebbda94dd · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Pytorch: An imperative style, high-performance deep learning library,
Reference 37
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Observation 8b19e5bf-fbd4-4dab-a630-23a5f8519b73 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Deepxplore: automated whitebox testing of deep learning systems,
Reference 38
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Observation 373b76dc-e8ce-42e2-b216-7f588ed9cd01 · outbound
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Unresolved cited work
Reference 40
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Observation 7847a494-7ec7-47d6-bf0d-a74e2fd7d2e9 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Rl baselines3 zoo,
Reference 41
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Observation 5f8c947c-dd42-4d59-a464-79f07c752f27 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Stable-baselines3: Reliable reinforce- ment learning implementations,
Reference 42
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Observation f5bb2e66-c7a3-4552-8fda-769e96ba4333 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Vuzzer: Application-aware evolutionary fuzzing,
Reference 43
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Observation 3531f5c8-04b1-4b63-a906-47cad1f57a25 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Prior- itized experience replay,
Reference 44
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Observation 08f3b321-4ad5-4fe5-820f-45254cae9e74 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Testing rein- forcement learning systems: A comprehensive review,
Reference 45
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Observation 7b324204-5d62-413f-92b0-bc1363edbaa0 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Search-based testing of reinforcement learning,
Reference 46
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Observation f8de37ae-c58d-4516-88b6-72f5a3a17675 · outbound
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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Observation 0e13f864-ed5a-4117-b0d8-29bc8a7bac3d · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents PCLA: A framework for testing autonomous agents in the CARLA simulator,
Reference 48
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Observation bf78c95f-ff79-4c9c-abe3-03dc3096903f · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents $\mu \text{PRL}$: A mutation testing pipeline for deep rein- forcement learning based on real faults,
Reference 49
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Observation 5432d385-0a19-41c6-bf9f-e07e27419d33 · outbound
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
Evaluating Fuzz Testing for Reinforcement Learning Agents Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures,
Reference 51
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Observation bc87c4b4-ffb7-4cd1-9e90-c05fe99ed2b6 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents A Survey of Reinforcement Learning for Software Engineering
Reference 52
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Observation 93072774-5721-4e15-80bf-762beaec7ca3 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Fuzzing with sequence diversity inference for sequential decision- making model testing,
Reference 53
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Observation 92c02899-5335-4e8c-8f17-3c4a1857fdf5 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Wilcoxon signed-rank test,
Reference 54
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Observation c79987c6-a72b-4d7a-a340-000b31aa7002 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Regression fault detection and mitigation in the evolution of deep learning systems,
Reference 55
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Observation ce60d6f8-9e0b-4c61-b7ed-6d1fe8e12891 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Regression fuzzing for deep learning systems,
Reference 56
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Observation b1a82dfe-93ab-40cf-ae69-0fe4a17d2bac · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Mitigating regression faults induced by feature evolution in deep learning systems,
Reference 57
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Observation b7944f7f-7ef3-4dac-9072-78b81813806a · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents A comprehensive study of deep learning model fixing approaches,
Reference 58
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Observation 7bcdd5fd-b297-4fb6-a408-77f48772b196 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Navigating the testing of evolving deep learning systems: An exploratory interview study,
Reference 59
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Observation 0427b5c4-991c-46e6-a128-9b48ebceb815 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents A white-box testing for deep neural networks based on neuron coverage,
Reference 60
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Observation 4db4486a-35ca-4156-89db-3d4b600c2118 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents End-to-end urban driving by imitating a reinforcement learning coach,
Reference 61
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Observation 536228ae-2ba2-4e83-b993-422e8b79c748 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Iden- tifying the failure-revealing test cases in metamorphic testing: A statistical approach,
Reference 62
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Observation a2af511f-0e1c-4213-aa1c-807c0a7c7e14 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Parallel test prioritiza- tion,
Reference 63
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Observation 1854a9fb-cb38-4bdd-ab07-67453af9713b · outbound
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,
Reference 64
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Observation dacbe2dd-2c07-4428-9f58-ba158a1ba7d0 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Robustness testing for multi-agent reinforcement learning: State perturbations on critical agents,
Reference 65
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Observation aa2814de-d5fb-42c6-b546-8f14d41c90f5 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents Fuzzing: A survey for roadmap,
Reference 66
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Observation 71bf83fc-0f88-423c-8889-c4a6f10c33a9 · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents A search-based testing approach for deep reinforcement learning agents,
Reference 67
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Observation 009d053d-2978-44d0-8016-9ebc9a7a200a · outbound
Evaluating Fuzz Testing for Reinforcement Learning Agents SMARLA: A safety monitoring approach for deep reinforcement learning agents,
Reference 68
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