{"as_of":"2026-08-17T18:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f87952e1b94d7bf92394ab01b59fc3e56147fa848168eeff394ce28830f5bcc7","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:51:25.769961Z","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":"2026-05-15T15:35:10.782793Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1804.03758","last_updated":"2018-04-11T00:06:36Z","snapshot_observed_at":"2026-08-14T19:27:15.119699Z","submitted_at":"2018-04-11T00:06:36Z","title":"Universal Successor Representations for Transfer Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03758","snapshot_observed_at":"2026-08-14T12:51:25.769961Z","title":"Universal successor features for transfer reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.06376","last_updated":"2019-08-18T04:24:08Z","snapshot_observed_at":"2026-08-16T18:28:50.916767Z","submitted_at":"2019-08-18T04:24:08Z","title":"VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-14T12:51:25.769961Z"},"links":{"cited_paper":"/paper/1804.03758","citing_paper":"/paper/1908.06376"},"observation_digest":"sha256:2f70e540c210f6b06bb424f35368fc7f7d8eee9f35a46874536e0818357d25e1","observation_id":"609da4df-8f92-4ecb-a7a7-742fb1610f90","resolution":{"observed_at":"2026-08-14T12:51:25.769961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03758","last_updated":"2018-04-11T00:06:36Z","snapshot_observed_at":"2026-08-14T19:27:15.119699Z","submitted_at":"2018-04-11T00:06:36Z","title":"Universal Successor Representations for Transfer Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03758","snapshot_observed_at":"2026-08-14T04:59:31.367916Z","title":"Uni- versal successor representations for transfer reinforcement learn- ing","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02291","last_updated":"2021-05-10T12:24:33Z","snapshot_observed_at":"2026-08-14T17:32:21.846938Z","submitted_at":"2019-09-05T10:02:29Z","title":"Learning Action-Transferable Policy with Action Embedding","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T04:59:31.367916Z"},"links":{"cited_paper":"/paper/1804.03758","citing_paper":"/paper/1909.02291"},"observation_digest":"sha256:fb49203224a606a029778f5c3e8dbc61e2616c6b4e7ba0200f401f9eb56cfcf1","observation_id":"62b1e3bf-45fc-442d-a58d-6ef81d335aff","resolution":{"observed_at":"2026-08-14T04:59:31.367916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03758","last_updated":"2018-04-11T00:06:36Z","snapshot_observed_at":"2026-08-14T19:27:15.119699Z","submitted_at":"2018-04-11T00:06:36Z","title":"Universal Successor Representations for Transfer Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"1804.03758","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.03758","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Universal Successor Representations for Transfer Reinforcement Learning","venue":"cs.AI","work_id":"29e09dec-e73c-426f-99ca-3b322f520651","year":2018},"citing_paper":{"arxiv_id":"2211.15657","last_updated":"2023-07-10T07:25:26Z","snapshot_observed_at":"2026-08-14T18:28:45.696263Z","submitted_at":"2022-11-28T18:59:02Z","title":"Is Conditional Generative Modeling all you need for Decision-Making?","version":4},"reference_index":165,"source":"arxiv_source","source_observed_at":"2026-05-15T15:35:10.593969Z"},"links":{"cited_paper":"/paper/1804.03758","citing_paper":"/paper/2211.15657"},"observation_digest":"sha256:9814babdf20e94f696418b8171e021389411dcc923457460ef14e25d9569e2a6","observation_id":"7fb4b8a4-3f02-4d4c-ad20-d37ce9983452","resolution":{"observed_at":"2026-05-15T15:35:10.787253Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1804.03758/citation-record","integrity":"/paper/1804.03758/integrity","json":"/paper/1804.03758/citation-record.json","paper":"/paper/1804.03758"},"outbound":[],"paper":{"arxiv_id":"1804.03758","last_updated":"2018-04-11T00:06:36Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-14T19:27:15.119699Z","submitted_at":"2018-04-11T00:06:36Z","title":"Universal Successor Representations for Transfer 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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1804.03758."}