{"as_of":"2026-08-16T15:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:76c35fedb69151e272cdc7adeacd06d51dcee8d12d03f652295ee6ebf23418e0","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T22:53:16.357358Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T10:43:05.730568Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03051","snapshot_observed_at":"2026-08-04T10:43:05.730568Z","title":"Less is more: A stealthy and efficient adversarial attack method for drl-based autonomous driving policies,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.09041","last_updated":"2026-06-05T03:00:50Z","snapshot_observed_at":"2026-08-16T03:17:16.311904Z","submitted_at":"2025-10-10T06:21:36Z","title":"Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T10:43:05.730568Z"},"links":{"cited_paper":"/paper/2412.03051","citing_paper":"/paper/2510.09041"},"observation_digest":"sha256:3e64be90a0d36e601a8af4f0704ae92cf79523ab5a867f9d44ffad5359fec493","observation_id":"7e87388b-3d36-4dd8-b891-c3a48ce72d77","resolution":{"observed_at":"2026-08-04T10:43:05.730568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.03051/citation-record","integrity":"/paper/2412.03051/integrity","json":"/paper/2412.03051/citation-record.json","paper":"/paper/2412.03051"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.185888Z","title":"What might be the economic implications of autonomous vehicles?","venue":null,"work_id":"24bdc916-64d9-40cc-8639-ab8a3d6ba228","year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.087569Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:9e92a530668722a0e8dd9d8b0bb508866daa5ee7ecf3c2e1e4ca6449bca5e7e1","observation_id":"b11c8c9c-cb82-4f7a-bbc8-192c26dce07d","resolution":{"observed_at":"2026-08-11T22:53:19.191343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.094171Z","title":"Learning naturalistic driving environment with statistical realism,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.094171Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:30d24d67d91e2d9fdf1e864bf036bc9b717f9554fe4800b9503d9294867884e4","observation_id":"8475f697-ff78-4060-b78c-870f66908606","resolution":{"observed_at":"2026-08-11T22:53:16.094171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.112696Z","title":"Trustworthy safety improvement for autonomous driving using reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.112696Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:4d780346556e94ff468585f3ea739d1ebffc9f61edf7f637169a4e2963c5f720","observation_id":"90220378-2b7e-4f5b-9683-efd0d818fee5","resolution":{"observed_at":"2026-08-11T22:53:16.112696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.118527Z","title":"Towards Robust Decision-Making for Autonomous Driving on Highway,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.118527Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:53fd00ff91b9bb3e19ced490e8266a42ed27afb44cc31ab3860e12a04dcef919","observation_id":"e4dbd387-9c19-4e69-99c6-6f964b853ea0","resolution":{"observed_at":"2026-08-11T22:53:16.118527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s42154-023-00231-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.486067Z","title":"Deep Reinforcement Learning Based Decision -Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments,","venue":null,"work_id":"71f343fa-50e4-4618-9bde-b64545ea9abe","year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.124240Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:72fe673d1b74c02945ce0349f3ace184bb3dd02c51959afd39a8a31e79d2afa9","observation_id":"2ece139f-7a54-4b8b-a07d-19a7d8a69d92","resolution":{"observed_at":"2026-08-11T22:53:16.491701Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.32854","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.003214Z","title":"Deep multi -agent reinforcement learning for highway on -ramp merging in mixed traffic,","venue":null,"work_id":"328596c9-3989-4e8d-9c7f-8dff79024db4","year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.130238Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:9c166a2cf97d8f115711db3a81c3505f64869f6d3ff1b47cd178535ad281e7fb","observation_id":"2fd2b63b-be4b-4618-980e-290fc6710472","resolution":{"observed_at":"2026-08-11T22:53:19.017855Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.136213Z","title":"Reinforcement Learning -Based Multi-Lane Cooperative Control for On -Ramp Merging in Mixed - Autonomy Traffic,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.136213Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:d49134992eb13ed7fb998d3e547f458fefcc04845bf71a43d17c620ff26868a3","observation_id":"4e8f9b43-a0c1-4b2d-8411-29915875cee0","resolution":{"observed_at":"2026-08-11T22:53:16.136213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s42154-023-00235-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.463844Z","title":"On -Ramp Merging for Highway Autonomous Driving: An Application of a New Safety Indicator in Deep Reinforcement Learning,","venue":null,"work_id":"afa7ec88-8d8d-48fd-8ca5-21a80759794a","year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.141874Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:d0324ea51425c584ba08585ebe3c99f09d81c10c1ad4da4896d2915b0f520985","observation_id":"ece84379-6a1c-458e-ba49-7c618f6f0696","resolution":{"observed_at":"2026-08-11T22:53:16.470623Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.148991Z","title":"Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.148991Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:d5e046b63028846ae0f0308ad5f02c7c5039aa17874fb2ca39a307bb6509eaa4","observation_id":"3fc8d7dc-b432-4e0f-84ca-4c4d0db32548","resolution":{"observed_at":"2026-08-11T22:53:16.148991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.154952Z","title":"Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.154952Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:6a71f61df97e229b3a35c1c1040d18bf1c6d344d8cce94489e9bfccb3f6e68bd","observation_id":"2320ef4b-56d8-4def-95af-96b3691cdba3","resolution":{"observed_at":"2026-08-11T22:53:16.154952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.169390Z","title":"Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation ,","venue":null,"work_id":"4c4ecabf-e85e-4679-8fa3-353633ab2498","year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.161075Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:071ef1bb578844402499274f98e13afef78eac63a03b02aa3a7eb3f4e4e69a9f","observation_id":"5c70ba85-4274-4bd2-83fe-fe2470617e37","resolution":{"observed_at":"2026-08-11T22:53:19.174606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.168998Z","title":"Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.168998Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:e5fae979b2e7227277c8649a003c2e237f74a0d8b1e5e30957a2e15706fc6b61","observation_id":"c02804fc-3a67-4031-a80d-40b9cc736abc","resolution":{"observed_at":"2026-08-11T22:53:16.168998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.175311Z","title":"Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi -agent Autonomous Driving Policies,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.175311Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:7c95620b30cdab013266eafae44ebd679002be0f59c82732e98002005f0850c6","observation_id":"7f7bc2ed-6026-443d-94b7-976cd07d6007","resolution":{"observed_at":"2026-08-11T22:53:16.175311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10462-024-11014-8","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.441387Z","title":"Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective,","venue":null,"work_id":"8662b83c-ba3c-43b6-abc9-b856962f4f41","year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.182004Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:b248866dbea7eb374409ba4d273bc42b818e5cbe98d8a13c78313348d4659743","observation_id":"697e850e-969d-4b7e-b76c-bba462e933a2","resolution":{"observed_at":"2026-08-11T22:53:16.449293Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.187738Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.187738Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:69d14f91943e58e0eac4a166c771cef09291a061725826b22ffeaaf2d17593c6","observation_id":"76ec5fdf-f239-4e7c-ad14-3307c24aa3f6","resolution":{"observed_at":"2026-08-11T22:53:16.187738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.150049Z","title":"Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,","venue":null,"work_id":"0c1f0df5-e6ef-453e-9bc1-d1ff51886499","year":2017},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.194405Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:1fa60b688729b8510df54cf9ef4cfe2bb4987b3ebda592816fb4605778150d5c","observation_id":"f3efb81e-0b83-426f-8f83-8b177a14bb1d","resolution":{"observed_at":"2026-08-11T22:53:19.156027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.200918Z","title":"ATS -O2A: A state-based adversarial attack strategy on deep reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.200918Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:8248f29861ac220e3f98bd4015633a0584da7741214603f60114a87999382c8f","observation_id":"68e4093f-272f-47fe-9c6d-190c7b324994","resolution":{"observed_at":"2026-08-11T22:53:16.200918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.207960Z","title":"Stealthy and Efficient Adversa rial Attacks against Deep Reinforcement Learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.207960Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:e6d04a8ed33d3dbb2ec707b343c9e5bb009f5964514d88fd5a4d3c5e4e7f2492","observation_id":"f0786f87-fea2-4387-9212-74e20bb490d5","resolution":{"observed_at":"2026-08-11T22:53:16.207960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.216054Z","title":"Attacking Deep Reinforcement Learning with Decoupled Adversarial Policy,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.216054Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:ac26fbf8d363067b48f79e7c1fed4031329b14878d4fb218a8f9c47e16600fe3","observation_id":"90927e0a-0877-4833-a0a1-68f91b9586cc","resolution":{"observed_at":"2026-08-11T22:53:16.216054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-11T22:53:16.233993Z","title":"Proximal Policy Optimization Algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.233993Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:124a8044b5c8e6080cca3cf72462e96b2143b04532fbfe570d1b5ca7a8c6c78e","observation_id":"2f620d3c-7e2c-4126-ae3f-3c97bd542164","resolution":{"observed_at":"2026-08-11T22:53:16.233993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.130931Z","title":"Microscopic Traffic Simulation using SUMO,","venue":null,"work_id":"cd081dfe-f8b3-4a93-846a-e84dec32c9d1","year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.241080Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:b47ce8265268b9a1301ec1cfdf95a1d470be6263585ee903f50c0b8d7a8f2a4d","observation_id":"0013f968-9a90-4688-812b-da5306d64f86","resolution":{"observed_at":"2026-08-11T22:53:19.137474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.252995Z","title":"Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.252995Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:7ccaf376e31362a817435dc075874d6e5da644dabb95ebe78c72aee88247dede","observation_id":"08986940-af2b-4411-9eea-63c699b9cee2","resolution":{"observed_at":"2026-08-11T22:53:16.252995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.259149Z","title":"Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.259149Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:8ca03073c78926985cd0a06dfe81214b11f596341185b6331565ac3f1094c6fa","observation_id":"a23f573c-8bca-43c3-9a49-d82753364ceb","resolution":{"observed_at":"2026-08-11T22:53:16.259149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.264414Z","title":"Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.264414Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:e36db3d979ef4a4275b69a719987831d7ed1d2b4f17adecc4f6bb30ffaaac4e8","observation_id":"166b7973-7771-4e74-91eb-e4bb434e362d","resolution":{"observed_at":"2026-08-11T22:53:16.264414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.269465Z","title":"Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.269465Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:413e8a7372662db374d20b5e5b80f236027d07cb908f5243a16dfbb6e8a58bb9","observation_id":"03a06724-96d3-4aed-ba24-e47564bdca2d","resolution":{"observed_at":"2026-08-11T22:53:16.269465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.274775Z","title":"End-to-end Autonomous Driving: Challenges and Frontiers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.274775Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:75485e9854e35ab75f2f608dd5d20d3afa9910f5ae05eeb87b70bfd7c29713e5","observation_id":"d69cc415-15a6-44fd-9cc3-ff8182724ff6","resolution":{"observed_at":"2026-08-11T22:53:16.274775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.280039Z","title":"An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.280039Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:33beab9e83fccb11c7a5bb9b00b4851ed68c22c875f7ad625e53fffab59b7f1f","observation_id":"b94efaa3-83ac-4c9d-84e9-a27aff117633","resolution":{"observed_at":"2026-08-11T22:53:16.280039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.285723Z","title":"Robust Decision Making for Autonomous Vehicles at Highway On -Ramps: A Constrained Adversarial Reinforcement Learning Approach,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.285723Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:a929313c1f40814d31aa422368bcabbb28e10d03d14b83b30e5d79237f8dc34b","observation_id":"51bba38a-5151-4444-b47b-19db329a3b69","resolution":{"observed_at":"2026-08-11T22:53:16.285723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.291254Z","title":"Explainable Deep Adversaria l Reinforcement Learning Approach for Robust Autonomous Driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.291254Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:50b0f89e040cbec27aa75e01a3833e7714e6caa3206d7d11a5f6b5ea8a3b610d","observation_id":"fa4a3b44-f42a-42a0-9dc9-3d27282699ca","resolution":{"observed_at":"2026-08-11T22:53:16.291254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.34188","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:17.111534Z","title":"Adversarial Stress Test for Autonomous Vehicle Via Series Reinforcement Learning Tasks With Reward Shaping,","venue":null,"work_id":"2d3cc110-8d4c-4027-bb3d-51c67d9f927e","year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.295895Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:c6f68c7db71064793be878518684ba6f87e64f3aa527d31d96e9b6e53756a15f","observation_id":"7d91819c-14b7-4938-bcc6-cb295ab2142b","resolution":{"observed_at":"2026-08-11T22:53:17.122957Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16996","last_updated":"2024-11-26T00:00:27Z","snapshot_observed_at":"2026-08-16T03:17:34.734337Z","submitted_at":"2024-11-26T00:00:27Z","title":"CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16996","snapshot_observed_at":"2026-08-11T22:53:16.300595Z","title":"CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios for Safety Hardening,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.300595Z"},"links":{"cited_paper":"/paper/2411.16996","citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:ea9f3c9749a3d5d45a3d40325b3c307d259c78e77b4e7b562930a4f41635a623","observation_id":"b01b521c-83a0-4337-ae62-6b7272b1c512","resolution":{"observed_at":"2026-08-11T22:53:16.300595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.307313Z","title":"Robust Lane Change Decision Making for Autonomous Veh icles: An Observation Adversarial Reinforcement Learning Approach,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.307313Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:4a211d3ef903c1ce07e5bff03c7a46abe1805fbe9fc85292fd279685d0be807f","observation_id":"bba7b09d-34f6-459e-ae71-806b0d88bf02","resolution":{"observed_at":"2026-08-11T22:53:16.307313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.312293Z","title":"Improved Robustness and Safety for Auton omous Vehicle Control with Adversarial Reinforcement Learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.312293Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:472bb653446d7ec450a239fac9031a8cc9072fae2d2c56080a75bd0e19012653","observation_id":"af53e424-1c21-4d1c-a6d7-1631a87162f7","resolution":{"observed_at":"2026-08-11T22:53:16.312293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.34133","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.813501Z","title":"Stealthy Black- Box Attack With Dynamic Threshold Against MARL -Based Traffic Signal Control System,","venue":null,"work_id":"2fcd1391-1242-4bf8-af46-fd77c0f44f3b","year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.316922Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:33904606f4847c28cbd293b494435884f41dc7b1057a6862fb6c300e3d253dec","observation_id":"396b8c9a-323d-4ff7-a7b2-943fe0ce3e19","resolution":{"observed_at":"2026-08-11T22:53:16.823907Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.321838Z","title":"Energy- Constrained Safe Path Planning for UAV -Assisted Data C ollection of Mobile IoT Devices,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.321838Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:54aa8f978b225d340783675c7692abba7179a021e575953c6821f457a171203f","observation_id":"8f47f27e-5c6e-440e-998f-cce39ca3c06a","resolution":{"observed_at":"2026-08-11T22:53:16.321838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.113422Z","title":"Soft Actor-Critic: Off- Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,","venue":null,"work_id":"7c41ae40-cdb6-4356-a4fa-41db0ffd251c","year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.326314Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:1ce69aefdc11560891dbca5c24ed6911eefc044a17cc5f73da89fcfa3b589210","observation_id":"b6bb50bb-5285-4f88-8ff8-aea66b02184c","resolution":{"observed_at":"2026-08-11T22:53:19.119487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.093579Z","title":"Addressing Function Approximation Error in Actor-Critic Methods,","venue":null,"work_id":"4db2455f-c27c-4bc7-b148-0e61561032bd","year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.330953Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:d5090cc9f713585b1201f616e5be40789c74dad9445c4bcbdaa500c8bbb5809e","observation_id":"3913350e-6b21-4e39-b462-316659879542","resolution":{"observed_at":"2026-08-11T22:53:19.099487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.337257Z","title":"Fear -Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.337257Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:46741f0c6f9155e1f7d2978596f58ac41363d693c2da8caee2c8292f59322efd","observation_id":"ea5bcd94-1ed1-4fd9-840e-c91062dbd8c9","resolution":{"observed_at":"2026-08-11T22:53:16.337257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.074562Z","title":"Stable -baselines3: Reliable reinforcement learning implementations,","venue":null,"work_id":"ca20d6a9-9da7-4189-a8ba-26b19c86d796","year":2021},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.341938Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:90960609630d5b86c79cdd3ecc0325464bb2cd43dc05875a1fb237ee2a341883","observation_id":"47118770-75cf-4d46-8811-14d3ae18e1df","resolution":{"observed_at":"2026-08-11T22:53:19.080056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.053821Z","title":"Explaining and Harnessing Adversarial Examples,","venue":null,"work_id":"6d8ab4cd-b902-4a8b-90bb-b85a3d311b72","year":2015},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.349640Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:82d2296f66d69a95f3f63c5ceba63a6e6a7d1e20ea523944277602a37024627a","observation_id":"d354080e-6296-458b-8dc8-cded3cb2e7dc","resolution":{"observed_at":"2026-08-11T22:53:19.059758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:19.032041Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks,","venue":null,"work_id":"53bc6af1-17e9-4218-b577-128d1ee7a16b","year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.357358Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:3e3023e907d93869cba64f514c12b0d3375cd5e18fed9983358448ced6af03e7","observation_id":"6c563cd6-9b94-438a-9bbd-cd08a2072939","resolution":{"observed_at":"2026-08-11T22:53:19.037640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:53:16.247706Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies","version":1},"reference_index":2582,"source":"pdf_text","source_observed_at":"2026-08-11T22:53:16.247706Z"},"links":{"citing_paper":"/paper/2412.03051"},"observation_digest":"sha256:575d9636618b06d90f857c5fbb6b020bd887b7e1e7247956171d02ddbbcd1734","observation_id":"254a6498-93ae-4a85-a27e-f57c2cb4b430","resolution":{"observed_at":"2026-08-11T22:53:16.247706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.03051","last_updated":"2024-12-04T06:11:09Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T03:17:51.505076Z","submitted_at":"2024-12-04T06:11:09Z","title":"Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":27,"verified_exact":3,"verified_fuzzy":9},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2412.03051."}