{"as_of":"2026-08-08T22:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:76ab347d84715089ec012c31156a8c7bfcfb2e0b82903596e955f7c04e16b7d0","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:24:03.035487Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T12:56:56.507096Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-08-07T15:24:03.035487Z","title":"A Com- prehensive Survey of Reinforcement Learning: From Algo- rithms to Practical Challenges,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15863","last_updated":"2025-05-21T07:59:18Z","snapshot_observed_at":"2026-08-07T23:22:27.322342Z","submitted_at":"2025-05-21T07:59:18Z","title":"Generative AI for Autonomous Driving: A Review","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:24:03.035487Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2505.15863"},"observation_digest":"sha256:abce51179b66d057ee749fbf6775211003b3c58ba85f3d18415bfcb21d501187","observation_id":"905e1a6b-bb43-4ddc-b38b-1c52f647d88a","resolution":{"observed_at":"2026-08-07T15:24:03.035487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-08-07T13:13:51.717425Z","title":"A comprehensive survey of reinforcement learning: From algorithms to practical challenges,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.22311","last_updated":"2025-05-28T12:54:07Z","snapshot_observed_at":"2026-08-08T16:17:51.972448Z","submitted_at":"2025-05-28T12:54:07Z","title":"From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T13:13:51.717425Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2505.22311"},"observation_digest":"sha256:694b090b60e071f34a66718ed38db7b55e995527c5656f30a8b68d912726f15c","observation_id":"8649973a-e863-46fd-9b3b-8a86ef7f221c","resolution":{"observed_at":"2026-08-07T13:13:51.717425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-08-07T13:13:05.518697Z","title":"Ghasemi, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22443","last_updated":"2025-05-28T15:07:56Z","snapshot_observed_at":"2026-08-07T20:55:50.404102Z","submitted_at":"2025-05-28T15:07:56Z","title":"Frequency Resource Management in 6G User-Centric CFmMIMO: A Hybrid Reinforcement Learning and Metaheuristic Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:13:05.518697Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2505.22443"},"observation_digest":"sha256:61c1964e94882a7cdfaeedc5c9445a5d985c42bcaed3d0a42811e3cae40a121c","observation_id":"329293e8-818e-4d65-b6a0-c902d1e8f14c","resolution":{"observed_at":"2026-08-07T13:13:05.518697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-08-05T14:42:40.287382Z","title":"Ghasemi, A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.21001","last_updated":"2025-09-05T15:50:08Z","snapshot_observed_at":"2026-08-05T14:42:38.564154Z","submitted_at":"2025-08-28T17:04:00Z","title":"Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T14:42:40.287382Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2508.21001"},"observation_digest":"sha256:a6e7145feb9c56cfa8a4ffcf81eecc578cb00ec98cff14576c7638dbbe888038","observation_id":"160563b1-a527-48d6-a5cd-619c26980934","resolution":{"observed_at":"2026-08-05T14:42:40.287382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":"2411.18892","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-07-02T12:56:56.507096Z","title":"Ghasemi, A","venue":null,"work_id":"cc03c954-d8e2-4d78-a5d5-770da3169b12","year":2025},"citing_paper":{"arxiv_id":"2509.08827","last_updated":"2025-10-09T17:08:52Z","snapshot_observed_at":"2026-08-06T15:38:05.011922Z","submitted_at":"2025-09-10T17:59:43Z","title":"A Survey of Reinforcement Learning for Large Reasoning Models","version":3},"reference_index":161,"source":"arxiv_source","source_observed_at":"2026-05-18T00:02:24.352947Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2509.08827"},"observation_digest":"sha256:b1323d7cc2ba376e6552e8781769efc8bf8ec9addc1dbf0c499fe28f749f689b","observation_id":"a5a6f549-c83d-4625-8d52-b403006dc3fc","resolution":{"observed_at":"2026-05-18T00:02:25.452439Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":"2411.18892","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-07-02T12:56:56.507096Z","title":"Ghasemi, A","venue":null,"work_id":"cc03c954-d8e2-4d78-a5d5-770da3169b12","year":2025},"citing_paper":{"arxiv_id":"2605.18569","last_updated":"2026-05-18T15:47:42Z","snapshot_observed_at":"2026-08-04T08:44:36.675339Z","submitted_at":"2026-05-18T15:47:42Z","title":"Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-20T10:25:40.269360Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2605.18569"},"observation_digest":"sha256:bf49054d4d8700e12145eb9129eef975806d8b8563a19583b21c71e15d1c139c","observation_id":"87812c59-05dd-480d-9180-d7f767a6160c","resolution":{"observed_at":"2026-05-20T10:28:12.125836Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":"2411.18892","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-07-02T12:56:56.507096Z","title":"Ghasemi, A","venue":null,"work_id":"cc03c954-d8e2-4d78-a5d5-770da3169b12","year":2025},"citing_paper":{"arxiv_id":"2606.01249","last_updated":"2026-06-17T04:44:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-31T14:04:51Z","title":"Trust Region On-Policy Distillation","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-28T17:38:50.313305Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2606.01249"},"observation_digest":"sha256:a1dbba181dc57b26b87ada3e3ccbeddda5c25ad7126cfd540dcb091629adcb07","observation_id":"7550a96b-62b7-4431-afc4-48048639118a","resolution":{"observed_at":"2026-07-01T20:56:13.336842Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":"2411.18892","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-07-02T12:56:56.507096Z","title":"Ghasemi, A","venue":null,"work_id":"cc03c954-d8e2-4d78-a5d5-770da3169b12","year":2025},"citing_paper":{"arxiv_id":"2606.27865","last_updated":"2026-06-26T09:06:04Z","snapshot_observed_at":"2026-08-01T15:37:44.932033Z","submitted_at":"2026-06-26T09:06:04Z","title":"From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T02:56:52.303152Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2606.27865"},"observation_digest":"sha256:b5f9795b0dcafc1704ab359bd85bd3a67c838dc6a332c89081957cc6b648948f","observation_id":"190a7ef9-50d6-479d-b85a-6fb7661b0e58","resolution":{"observed_at":"2026-07-01T17:55:51.427942Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges","version":2},"cited_work":{"arxiv_id":"2411.18892","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18892","snapshot_observed_at":"2026-07-02T12:56:56.507096Z","title":"Ghasemi, A","venue":null,"work_id":"cc03c954-d8e2-4d78-a5d5-770da3169b12","year":2025},"citing_paper":{"arxiv_id":"2607.00642","last_updated":"2026-07-01T08:57:25Z","snapshot_observed_at":"2026-08-08T11:37:52.401140Z","submitted_at":"2026-07-01T08:57:25Z","title":"Coachable agents for interactive gameplay","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-02T12:52:05.010028Z"},"links":{"cited_paper":"/paper/2411.18892","citing_paper":"/paper/2607.00642"},"observation_digest":"sha256:9907568e248b7786340dcb570898b799ea6eab69293897480a47b99df4cd7ed9","observation_id":"5fd793d5-d6fc-4f7b-9c5c-c60bfd655e3a","resolution":{"observed_at":"2026-07-02T12:56:56.508979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.18892/citation-record","integrity":"/paper/2411.18892/integrity","json":"/paper/2411.18892/citation-record.json","paper":"/paper/2411.18892"},"outbound":[],"paper":{"arxiv_id":"2411.18892","last_updated":"2025-02-01T23:49:26Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T16:39:56.385826Z","submitted_at":"2024-11-28T03:53:14Z","title":"A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2411.18892."}