{"as_of":"2026-08-08T11:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ec0d0eec74b8d97ca5e5b0b48e36e2c2deca3dd46c5d8d0277fd56d448a68b15","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T17:53:37.012368Z","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":"2026-05-22T08:01:15.974036Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.10846","last_updated":"2023-07-20T13:08:14Z","snapshot_observed_at":"2026-07-06T15:56:25.975005Z","submitted_at":"2023-07-20T13:08:14Z","title":"Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning","version":1},"cited_work":{"arxiv_id":"2307.10846","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.10846","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Goal-conditioned reinforcement learning with disentanglement-based reachability planning","venue":null,"work_id":"86d6a98f-e024-4bec-ba28-2e92f6f2991d","year":2023},"citing_paper":{"arxiv_id":"2605.22164","last_updated":"2026-05-21T08:34:57Z","snapshot_observed_at":"2026-08-07T04:10:09.031043Z","submitted_at":"2026-05-21T08:34:57Z","title":"Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T07:56:49.042239Z"},"links":{"cited_paper":"/paper/2307.10846","citing_paper":"/paper/2605.22164"},"observation_digest":"sha256:a4fda6fb49b2a4666067562458a7c8423f999b572a044e18b97fb5f1a0c9516e","observation_id":"271399cd-0bae-4f54-9c9b-432f1d35b48d","resolution":{"observed_at":"2026-05-22T08:01:15.976144Z","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":"2307.10846","last_updated":"2023-07-20T13:08:14Z","snapshot_observed_at":"2026-07-06T15:56:25.975005Z","submitted_at":"2023-07-20T13:08:14Z","title":"Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10846","snapshot_observed_at":"2026-07-30T17:53:37.012368Z","title":"Reuven Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23602","last_updated":"2026-07-26T11:11:23Z","snapshot_observed_at":"2026-08-05T10:42:24.747043Z","submitted_at":"2026-07-26T11:11:23Z","title":"Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-07-30T17:53:37.012368Z"},"links":{"cited_paper":"/paper/2307.10846","citing_paper":"/paper/2607.23602"},"observation_digest":"sha256:12c47f6c47b99a70c05fec898238f397bb479fc18289538b475f9c1327a64eda","observation_id":"d2c29f64-e791-4a0a-a998-6939adb1345b","resolution":{"observed_at":"2026-07-30T17:53:37.012368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2307.10846/citation-record","integrity":"/paper/2307.10846/integrity","json":"/paper/2307.10846/citation-record.json","paper":"/paper/2307.10846"},"outbound":[],"paper":{"arxiv_id":"2307.10846","last_updated":"2023-07-20T13:08:14Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-07-06T15:56:25.975005Z","submitted_at":"2023-07-20T13:08:14Z","title":"Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning"},"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 2 inbound Pith citation observations for arXiv:2307.10846."}