{"as_of":"2026-08-19T21:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f100db9f222e1eea329a6f90e6e8d5228a2d0ea8756a0be4ab534cd671e55c8e","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:51:58.380456Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2507.04464/citation-record","integrity":"/paper/2507.04464/integrity","json":"/paper/2507.04464/citation-record.json","paper":"/paper/2507.04464"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:52:02.835401Z","title":"Systematic literature review: Anomaly detection in connected and autonomous vehicles,","venue":null,"work_id":"443765d5-382b-4421-a7c5-79b040066da5","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.645728Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:c6dcc84c57bd1bf99576d02cef7edf5458cf111084ea0418cb0bf551c19da023","observation_id":"dca75b93-edfc-4751-9128-c234fd8a16f2","resolution":{"observed_at":"2026-08-06T19:52:02.839415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.821126Z","title":"On evaluating black-box explainable ai methods for enhancing anomaly detection in autonomous driving systems,","venue":null,"work_id":"ed6c7d9d-bb22-4b94-9824-b0d08b9e4f9e","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.708815Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:8795b1a5add795e0899b645ede475f027826fbaa920d2864ae8b790e5666d00e","observation_id":"12f59924-f8de-43b8-bf00-ea62e602ad66","resolution":{"observed_at":"2026-08-06T19:52:02.825650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:54.793786Z","title":"Anomaly detection in autonomous driving: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.793786Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:8c497acde1e28d4341a69b9a726749aae06c843038468bf3916647662bf87948","observation_id":"cc2c3574-1447-41c6-84af-c34584d7c8d0","resolution":{"observed_at":"2026-08-06T19:51:54.793786Z","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":"2026-08-06T19:52:02.798282Z","title":"Outlier-robust inverse reinforcement learning and reward-based detec- tion of anomalous driving behaviors,","venue":null,"work_id":"21cc06d9-995e-416e-a7be-2205f65419b5","year":2022},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.855692Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:cf8c708257c3066a39bc1bcaffe14d82ac4d9196e5c9f31efb3fbba9bca3c8f7","observation_id":"66c928e3-0a4c-42b0-986b-53a75bcd968b","resolution":{"observed_at":"2026-08-06T19:52:02.802806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.784774Z","title":"Sensor data based anomaly detection in autonomous vehicles using modified convolutional neural network","venue":null,"work_id":"eb7551e1-ce57-4e92-bed4-066a258ee234","year":2022},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.910658Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:f70f8e2729c774b30e1e8edfee0c54e6143561c451ab0be0007de9d9f0539064","observation_id":"b9b25c6f-85dd-4ce0-a87f-fdbcd590b820","resolution":{"observed_at":"2026-08-06T19:52:02.788846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.771342Z","title":"Machine learning- based real-time anomaly detection using data pre-processing in the telemetry of server farms,","venue":null,"work_id":"fe4fbaf7-ec1d-4bc8-83af-881a8bd0f960","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:54.966991Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:423b8ae8be6ce76a6a8824c6f4808bb794b3a6d9a78acc1aa820a836c48e8a7f","observation_id":"bde912d6-3c31-477f-bfc8-6849cc6a517b","resolution":{"observed_at":"2026-08-06T19:52:02.775563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.756283Z","title":"Introduction to anomaly detection,","venue":null,"work_id":"14549856-434d-4300-9a99-fe07eccbd280","year":2025},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.021182Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:3a04128179075bbbea69b7c1d103dfd8c00dfa0afb32fc1977e17a33c2c1ae5c","observation_id":"a8f28002-92d8-4b42-87d9-980649df3f98","resolution":{"observed_at":"2026-08-06T19:52:02.760854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.741205Z","title":"Anomaly detection in connected and autonomous vehicle trajectories using lstm autoencoder and gaussian mixture model,","venue":null,"work_id":"0e2ac86e-8530-446c-acf6-fb9607b8ad26","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.074203Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:dff2780d81ba75dc5fc5458c99134a6449fdfbde4b45e712974a4c2ea4378243","observation_id":"e4fc8920-724c-4ead-a22c-c443f4b41266","resolution":{"observed_at":"2026-08-06T19:52:02.745494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.726943Z","title":"Sequential anomaly detection using inverse reinforcement learning,","venue":null,"work_id":"c65c18da-e252-4ef8-b8a3-46d272b30a39","year":2019},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.183969Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:22164a294a9787f3a3b335cb1a0ae6573bcbf03daae02915999b82fe8666233f","observation_id":"9a2a8885-8e52-423d-8814-561d9bcb4299","resolution":{"observed_at":"2026-08-06T19:52:02.731545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.711375Z","title":"Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,","venue":null,"work_id":"db9d835b-7870-4f22-b06d-91fa5a389774","year":2022},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.250287Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:7fbb834c3511b39a175486cecad37eca2311c5f2072f1183b0fd082ab2ef0097","observation_id":"61799466-026c-4be6-9d3d-7b53bbf4eb41","resolution":{"observed_at":"2026-08-06T19:52:02.716437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.696466Z","title":"Outlier detection by active learning,","venue":null,"work_id":"a2b4814d-fad7-4d26-994c-cafa3756ce2f","year":2006},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.333907Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:0e8a8a568276272dfccf478121c3c9fda96adda68b515319631823c7f1c20d5a","observation_id":"08677221-2b65-43d6-b3b6-1ac60e07e66a","resolution":{"observed_at":"2026-08-06T19:52:02.701095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.681465Z","title":"Neural batch sampling with reinforcement learning for semi-supervised anomaly detection,","venue":null,"work_id":"21ca5d6d-937c-4ab8-8efe-b6d100180da4","year":2020},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.453140Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:07743bded7a1eaff6a710f92d021061270bc3be8760181b1ba4a0fe41e4e80be","observation_id":"fc8ff844-2515-4cfd-8fbb-35b84b45f7fe","resolution":{"observed_at":"2026-08-06T19:52:02.686684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.493510Z","title":"Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets,","venue":null,"work_id":"cf50f00a-fa81-4a44-ad89-ba662f004ba9","year":2009},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.544209Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:d2bdea9aa9052086a2dc10153199b19e3735a78028cd9adcf3844393fe48bdb6","observation_id":"29a1789e-5864-40f4-a4a5-5b69af222f94","resolution":{"observed_at":"2026-08-06T19:52:02.657708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:02.144792Z","title":"Distance-based outliers: algorithms and applications,","venue":null,"work_id":"be74f051-cb57-4e75-8c8e-8068637cf23e","year":2000},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.619546Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:47212b104dac421c66fda263ab3ddb45b898ac6cb15ad9932564a754054eb09f","observation_id":"af0d9a0a-01ce-4671-b672-834c2db97be5","resolution":{"observed_at":"2026-08-06T19:52:02.276215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:01.791208Z","title":"Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,","venue":null,"work_id":"f41c6a82-2bfa-4a30-a39a-d6c83048d96e","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.690107Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:0d99952c3437647d237b25e9959189adcacfc3646b328248b3aedbcb41a9e2d8","observation_id":"8b879e11-16a7-446d-8ad2-09ca0d0923c1","resolution":{"observed_at":"2026-08-06T19:52:01.974936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:55.740971Z","title":"Lof: identifying density-based local outliers,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.740971Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:918a69a658babe3511817452d28b5d45869e017d2253f0134e771f789734b3cc","observation_id":"dd053cc8-58c7-4822-b03e-b1e473fd638a","resolution":{"observed_at":"2026-08-06T19:51:55.740971Z","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":"2026-08-06T19:52:01.566124Z","title":"Anomaly detection using local kernel density estimation and context-based regression,","venue":null,"work_id":"adf1c624-bf97-497f-b950-eba1569edfee","year":2018},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.805148Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:0e26392a743474604ab3cc266ad51531012f17f719d721db2621622ecede8e6c","observation_id":"2d5d4740-60e6-4984-a11a-21ae6c357557","resolution":{"observed_at":"2026-08-06T19:52:01.630834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.15939","last_updated":"2024-02-27T00:07:47Z","snapshot_observed_at":"2026-08-16T15:04:39.233886Z","submitted_at":"2023-08-30T10:35:36Z","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.15939","snapshot_observed_at":"2026-08-06T19:51:55.913470Z","title":"Bootstrap fine-grained vision-language alignment for unified zero-shot anomaly localization,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.913470Z"},"links":{"cited_paper":"/paper/2308.15939","citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:a5fd0ed64c0338ac5ff4b3f8c361e14b7448dc5804d6a385f50a2d40aa1a71d9","observation_id":"063436d4-c97c-46b5-84d5-29326d29da57","resolution":{"observed_at":"2026-08-06T19:51:55.913470Z","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":"2026-08-06T19:51:55.992108Z","title":"Discovering cluster-based local outliers,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:55.992108Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:51a4275a3a2a5e4f2fa67a9a8dcc5e583b8657df2014c338ece93395e823edb4","observation_id":"58585494-753e-491d-81aa-0f4a6d28483b","resolution":{"observed_at":"2026-08-06T19:51:55.992108Z","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":"2026-08-06T19:51:56.099665Z","title":"Isolation forest,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.099665Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:35af69f4a657d16cdb9c4e77ab3e395dd897ec7923b160bc64aff36d9c33fde9","observation_id":"4c51ef1f-4847-4e8a-83fb-31d60f04c18f","resolution":{"observed_at":"2026-08-06T19:51:56.099665Z","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":"2026-08-06T19:52:01.464782Z","title":"Guided cost learning: Deep inverse optimal control via policy optimization,","venue":null,"work_id":"7810666c-e890-4f18-93b3-06b5f462c23d","year":2016},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.232021Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:b6e3cd8922fc384d4bab32fe68e63f67a62871431bc058c484f129f0d21b8f0e","observation_id":"8e56f471-e5d7-4aea-bfb4-c493ef27ef32","resolution":{"observed_at":"2026-08-06T19:52:01.494173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:01.325368Z","title":"Anomaly detection and correction of optimizing autonomous systems with inverse reinforcement learning,","venue":null,"work_id":"651c2d00-4d5d-4db1-a023-fb9276c3dfa3","year":2022},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.367025Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:e48fbd7ee34988266cbc1aef2f633092439e29d3b6a04a690cc6e60a9540fc2e","observation_id":"2587af8c-79ba-4856-a69b-ed501b15ff01","resolution":{"observed_at":"2026-08-06T19:52:01.386732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07865","last_updated":"2024-09-26T12:18:49Z","snapshot_observed_at":"2026-08-16T13:53:07.515524Z","submitted_at":"2024-05-13T15:53:18Z","title":"AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07865","snapshot_observed_at":"2026-08-06T19:51:56.510037Z","title":"Anovox: A benchmark for multimodal anomaly detection in autonomous driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.510037Z"},"links":{"cited_paper":"/paper/2405.07865","citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:ce5dfd557a1fe38d51dd0665d73f14b3119903dd32cc7198a013276db25b2aa2","observation_id":"6cb214b3-9842-4b0d-81a3-b75722c744c3","resolution":{"observed_at":"2026-08-06T19:51:56.510037Z","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":"2026-08-06T19:52:01.160831Z","title":"Modernisation of carla - can we define the what, and how and where?","venue":null,"work_id":"d89efd27-b75e-4fb1-ae23-6687fdec3c71","year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.662672Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:1ca4f31ad8656fc722963da0b17586d7a55cebbbcf9a6d764ebec0a1b4f812f1","observation_id":"c8f882b4-c2a8-4ab7-9806-4b5ed941f4da","resolution":{"observed_at":"2026-08-06T19:52:01.252833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:01.060360Z","title":"Detecting anomalies in semantic segmentation with prototypes,","venue":null,"work_id":"0209e2eb-9d1f-4ac2-960f-3c2e8bf5aee2","year":2021},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.824244Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:44d7f48167a159d1a99e69243bca37475dfa4ad7d85ba36ef57a050f2e962049","observation_id":"86acf290-798e-4990-9fc5-240ff954919e","resolution":{"observed_at":"2026-08-06T19:52:01.109599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.943329Z","title":"Augmenting anomaly detection for autonomous vehicles with symbolic rules,","venue":null,"work_id":"ea10b458-f7b0-4ea5-8025-e00648fb1ead","year":2019},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.892913Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:90f4f6f6b45a52edd5816ee6193f889451e38ef1f49142f99de9aba77208c77f","observation_id":"30d25551-80b6-4321-8318-3e3718b28325","resolution":{"observed_at":"2026-08-06T19:52:00.988552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.822407Z","title":"Over-the-air: How we remotely compromised the gateway, bcm, and autopilot ecus of tesla cars,","venue":null,"work_id":"84e16642-e063-48a9-a039-1d5d29e292b4","year":2018},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:56.974276Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:95eeb878766b463fe4169ac08e0b79d9137277f9dd5764956826f94afe707008","observation_id":"2294bc39-9c8d-4ba7-929c-35d64dcf0c52","resolution":{"observed_at":"2026-08-06T19:52:00.874593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.708432Z","title":"Free-fall: Hacking tesla from wireless to can bus,","venue":null,"work_id":"d818504e-aa79-492f-814d-7fc5cfbb001f","year":2017},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.054474Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:54bc8685c48c75ded1a559f8dd6b33c9669ae743b8336d9cbf92db8c2d3663bf","observation_id":"828a2344-fdc4-4e5c-a1dc-5508dde598b9","resolution":{"observed_at":"2026-08-06T19:52:00.759373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.590793Z","title":"Orads: One class rule-based anomaly detection system in autonomous vehicles,","venue":null,"work_id":"04481f15-b69d-4f38-a612-ccad02c64ab4","year":2025},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.129345Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:293dcb433f3c929ea9750d421ea5982e1e61b5ca2e5e5909bd425e7e61c6ce95","observation_id":"28f32057-081c-4e02-a975-b641dbfc4d8f","resolution":{"observed_at":"2026-08-06T19:52:00.639699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.485379Z","title":"An association rules-based approach for anomaly detection on can-bus,","venue":null,"work_id":"29e93e34-3392-48a0-9f26-96ef83d1f994","year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.176560Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:50c5453339d91fed9616b82c2e89ccf7af7052e420397b392cc06ac2505a439f","observation_id":"88ae22dc-5271-49bf-bb71-89a79d786613","resolution":{"observed_at":"2026-08-06T19:52:00.525217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.365478Z","title":"Sce- narionet: Open-source platform for large-scale traffic scenario simulation and modeling,","venue":null,"work_id":"3fe85bb6-e44a-40bd-8a7b-fbc9f1216e2e","year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.220453Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:d4c3ef21c6eb297d1ac98dad42abab1fd77345591b85b7b0339f3d7a624f5833","observation_id":"0fcc518e-8739-46a6-9964-1476cfb98755","resolution":{"observed_at":"2026-08-06T19:52:00.417664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:52:00.242145Z","title":"Standards for passenger comfort in automated vehicles: Acceleration and jerk,","venue":null,"work_id":"73d382f6-43f6-447a-a4e7-bed887fb0186","year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.301171Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:cc1ba1b1c2cba9c60d065f6ff24adf3c2cb7d9b2476d82871b5256ed4f27c86e","observation_id":"c1d4013e-3807-4a06-9135-a99095f3141e","resolution":{"observed_at":"2026-08-06T19:52:00.311788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:57.367018Z","title":"Maximum entropy inverse reinforcement learning","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.367018Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:088c9aa4b22cf9156368aee8f1847401b4f541e2581b280d8142c1653b64cd93","observation_id":"f4708d05-2d04-4913-9547-311699d94bef","resolution":{"observed_at":"2026-08-06T19:51:57.367018Z","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":"2026-08-06T19:51:57.417194Z","title":"Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.417194Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:63e48bb21a5a1356a5f19e4b5d009c5d9aeea8669acd306c62006a60eba39927","observation_id":"d573c10f-0ad4-4a40-9d44-139ed7650fd3","resolution":{"observed_at":"2026-08-06T19:51:57.417194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-06T19:51:57.482390Z","title":"Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.482390Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:2003e08db806071e044cc3cd403554a01c936c583119f3ed0366018e7bcc2619","observation_id":"27eaff30-15a0-4801-8648-eb7ceb2ae5f9","resolution":{"observed_at":"2026-08-06T19:51:57.482390Z","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":"2026-08-06T19:51:57.530435Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.530435Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:48771f6c1846471b8b98eb9cf225cd8bb754e9f78c31497228bbb09b87722635","observation_id":"6ee1b981-c233-42f0-8bfb-a0bab6a015e4","resolution":{"observed_at":"2026-08-06T19:51:57.530435Z","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":"2026-08-06T19:52:00.081352Z","title":"Receding horizon control of nonlinear systems,","venue":null,"work_id":"c65cec84-f699-4fd1-8e03-6c1068b9171f","year":1988},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.590130Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:30fc57aba383b33c00893ed3fc254aadc8b9164a29858c7890c8cc096af2de9c","observation_id":"c48d3016-4c36-454a-b4f8-f0bb7b34801b","resolution":{"observed_at":"2026-08-06T19:52:00.140476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:59.971132Z","title":"An optimization-based receding horizon trajectory planning algorithm,","venue":null,"work_id":"9f903a4e-d74a-4023-9a49-190d3fcd7fa8","year":2020},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.673819Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:573ff42d8bfdb84efd3e67a0bc7dd63ac9f0b8455c66be3640b762acc08af389","observation_id":"1dca1195-bd3b-49b8-ae03-ae9ac5e08f2c","resolution":{"observed_at":"2026-08-06T19:52:00.023505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:59.846191Z","title":"Efficient driving with automated and connected vehicles algorithms, microsimulations, and cyber-physical experiments,","venue":null,"work_id":"575ee92c-cb4b-409a-ab37-c549e488fce6","year":2024},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.734325Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:b684e4224f17a9d5eb5e34873ffc4998892c6a5d8d6a03150535fd7bd02ce2fe","observation_id":"492b77b4-75fc-4bfc-afa5-89a83ed884f0","resolution":{"observed_at":"2026-08-06T19:51:59.915607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:59.692968Z","title":"One-class svms for document clas- sification,","venue":null,"work_id":"afc3355d-57a5-4f7c-8bc9-d7af95e0237c","year":2001},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.815861Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:91cda5618b8ba75c4f66114751b80b0c2eceed2219b26b495f17aa57c2dd20fc","observation_id":"33c45bac-8dbd-4aba-8c49-8a48e7b69936","resolution":{"observed_at":"2026-08-06T19:51:59.756899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:57.897137Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.897137Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:9f4b6098c111de44cef2ed938b47830228c9c52f3f92743f7e94760b53260ee0","observation_id":"f653e06a-cfe7-46de-afe0-d66c3a40b101","resolution":{"observed_at":"2026-08-06T19:51:57.897137Z","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":"2026-08-06T19:51:57.963731Z","title":"Induction of decision trees,","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:57.963731Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:770325d108cb21ac3c21103fc2b30711ae566b6ffc97e780215b90d38d626edb","observation_id":"47abfd69-7430-49c1-8ea7-cdbf3b22e7fc","resolution":{"observed_at":"2026-08-06T19:51:57.963731Z","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":"2026-08-06T19:51:59.512210Z","title":"Supervised autoencoders: Improving generalization performance with unsupervised regularizers,","venue":null,"work_id":"2d34e577-7638-431e-a87e-827137c7b263","year":2018},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.002205Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:5357a590e162e6646e0429894b0bc2cc1f8a52d48f2f713c17f95a0168032687","observation_id":"55880e67-3edc-4eae-8b16-c280a13a5c4b","resolution":{"observed_at":"2026-08-06T19:51:59.581351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:59.315539Z","title":null,"venue":null,"work_id":"bc1830ca-c107-4696-a6d9-8c0d72ec599c","year":null},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.051452Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:7d8c3ebd33de07b854b5a04795d6f860cde7008dc944b85a8d27fbddc0f514d8","observation_id":"0c1fe5d9-fd7f-43ac-bea2-f5de3daccc66","resolution":{"observed_at":"2026-08-06T19:51:59.414129Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:59.146045Z","title":"A methodology to model the rain and fog effect on the performance of automotive lidar sensors,","venue":null,"work_id":"b97b0d14-ba1d-4c26-9d27-cdc6acdad1b7","year":2023},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.108238Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:635d7342f97dc26eda5d630611ccf4d120506d9d0af97c0c064d1e46e48b5790","observation_id":"79c99ae1-043c-4e08-9ecc-b2001156cff4","resolution":{"observed_at":"2026-08-06T19:51:59.238686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:58.990796Z","title":"Predicting the influence of rain on lidar in adas,","venue":null,"work_id":"01922d80-efdd-44d5-8aa9-f039b8bd3262","year":2019},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.176513Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:843253fa99c400f1e91904bbc3ac748001028b129a6c7aecb7556d815a100df0","observation_id":"388b9204-a559-4c67-b700-04e500a2c893","resolution":{"observed_at":"2026-08-06T19:51:59.056460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:58.833105Z","title":"Lidar data noise models and methodology for sim-to-real domain generalization and adaptation in autonomous driving perception,","venue":null,"work_id":"81f72d25-b56a-4086-b3af-27e1ec02267c","year":2021},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.250189Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:95a692ab9a7067628075dbe137838847a33e66cb10440a436411f111372fa18f","observation_id":"5abb92fd-26b7-4399-85e3-fa4dd04ca0c7","resolution":{"observed_at":"2026-08-06T19:51:58.910455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:58.658158Z","title":"Dlp ® dmd technology : Lidar ambient light reduction,","venue":null,"work_id":"848ee58b-e881-4930-a8c9-f20d7ac0cc74","year":2018},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.330672Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:a6b070f75d2ef4c0e63df7648bdb903536f393f68002fac0053e4f6a79c3bb41","observation_id":"6ca3f368-71c5-42fa-bee3-5f940d035afe","resolution":{"observed_at":"2026-08-06T19:51:58.740241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-06T19:51:58.504723Z","title":"Learning to drop points for lidar scan synthesis,","venue":null,"work_id":"46f11cc6-3b92-48ad-bb06-c6bff76748e5","year":2021},"citing_paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:51:58.380456Z"},"links":{"citing_paper":"/paper/2507.04464"},"observation_digest":"sha256:0eeb1fe24c48d26a7284a7b31df68228edcae151ed747e460260bfee95897c07","observation_id":"338f5af8-d6c5-4107-acbc-8e5b81f231bc","resolution":{"observed_at":"2026-08-06T19:51:58.560646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.04464","last_updated":"2025-07-06T17:01:02Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T16:07:23.854763Z","submitted_at":"2025-07-06T17:01:02Z","title":"Anomalous Decision Discovery using Inverse Reinforcement Learning"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":36},"total_outbound_references":49},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2507.04464."}