{"as_of":"2026-08-09T09:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aba1d71a1a81ecb2c17f0c4142dabaa2485fbb157960f9a3c29258caafe0b51a","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:53:19.979203Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T23:34:18.836211Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-07T14:53:19.979203Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17391","last_updated":"2025-05-23T02:01:15Z","snapshot_observed_at":"2026-08-08T00:22:36.393355Z","submitted_at":"2025-05-23T02:01:15Z","title":"Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:53:19.979203Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2505.17391"},"observation_digest":"sha256:46bae762dd6b38b5846766b53d558e54262c220c86b8163ed40c052fecfea41d","observation_id":"6a95b0a5-e141-4496-a45a-e2b024937985","resolution":{"observed_at":"2026-08-07T14:53:19.979203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-07T13:51:10.237804Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning[J]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23809","last_updated":"2025-06-03T07:39:21Z","snapshot_observed_at":"2026-08-07T13:42:26.146563Z","submitted_at":"2025-05-27T08:40:11Z","title":"LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:51:10.237804Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2505.23809"},"observation_digest":"sha256:4c5bd620cb59a8319af342fbad2f638abd6f77e2126dc930c3579a8f8b0fc855","observation_id":"c01186df-07f0-4efc-929f-4a3c5f375a51","resolution":{"observed_at":"2026-08-07T13:51:10.237804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-07T10:16:53.039714Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05873","last_updated":"2025-06-06T08:41:33Z","snapshot_observed_at":"2026-08-09T08:45:05.065649Z","submitted_at":"2025-06-06T08:41:33Z","title":"Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:16:53.039714Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2506.05873"},"observation_digest":"sha256:2d8328abdda0c839babd464144f8142f3f84df7a040a32aba332b7054c43d566","observation_id":"3c4aa78f-5b96-4774-9d87-1d219bbd07f7","resolution":{"observed_at":"2026-08-07T10:16:53.039714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-07T12:04:53.598481Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning[J]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.06336","last_updated":"2025-05-31T19:17:48Z","snapshot_observed_at":"2026-08-09T08:45:17.774280Z","submitted_at":"2025-05-31T19:17:48Z","title":"Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:53.598481Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2506.06336"},"observation_digest":"sha256:95ec52e19ba03e6b1db25370b2b61c31a200faa7d3286a0f3f72b0410f7637c8","observation_id":"d0af043b-01a9-438d-9f5d-6ef330edf379","resolution":{"observed_at":"2026-08-07T12:04:53.598481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-07T04:13:33.187799Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning[J]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.11421","last_updated":"2025-08-13T15:18:09Z","snapshot_observed_at":"2026-08-09T08:44:53.470070Z","submitted_at":"2025-06-13T02:39:21Z","title":"Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T04:13:33.187799Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2506.11421"},"observation_digest":"sha256:be0f2b925dbc7998cecb93c695ad62e8249b96dbec6d07d30b89e570f0b2034b","observation_id":"3eca97ea-1b97-4605-b251-5e23295e161f","resolution":{"observed_at":"2026-08-07T04:13:33.187799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-06T23:34:57.662314Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning[J]","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.17551","last_updated":"2025-06-24T02:28:50Z","snapshot_observed_at":"2026-08-09T08:44:52.986456Z","submitted_at":"2025-06-21T02:37:25Z","title":"Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:34:57.662314Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2506.17551"},"observation_digest":"sha256:3615bd19e24771d3453e9298028210dde2025d23d7d8a33dc197f0dee3e36557","observation_id":"e39c5510-d8b5-45f9-ae27-90eee3db513a","resolution":{"observed_at":"2026-08-06T23:34:57.662314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2504.21071","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.21071","snapshot_observed_at":"2026-08-06T23:34:18.836211Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning","venue":"cs.RO","work_id":"7e9435f5-565c-4a68-8938-f0b6d15c3e60","year":2025},"citing_paper":{"arxiv_id":"2507.01035","last_updated":"2025-06-21T03:10:50Z","snapshot_observed_at":"2026-08-09T08:44:21.185222Z","submitted_at":"2025-06-21T03:10:50Z","title":"Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:34:18.326716Z"},"links":{"cited_paper":"/paper/2504.21071","citing_paper":"/paper/2507.01035"},"observation_digest":"sha256:4933aca8affbda5c8268b7ea72ba6bc29eb2edc7c59608e427c936581b2e71f9","observation_id":"0b0f6806-5d09-499f-b7ca-1fb281a3cfe3","resolution":{"observed_at":"2026-08-06T23:34:18.891268Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.21071/citation-record","integrity":"/paper/2504.21071/integrity","json":"/paper/2504.21071/citation-record.json","paper":"/paper/2504.21071"},"outbound":[],"paper":{"arxiv_id":"2504.21071","last_updated":"2025-04-29T15:25:34Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T15:58:10.434310Z","submitted_at":"2025-04-29T15:25:34Z","title":"Automated Parking Trajectory Generation Using Deep Reinforcement Learning"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.21071."}