{"as_of":"2026-08-22T08:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d1fc43e77aa729192087d14f85c9543b2ed95a774e5820d51a936922bee8147","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":"2026-08-22T06:32:14.747728+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":"2026-08-05T17:47:16.074134Z","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":"2026-07-04T21:10:09.712524Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.17813","last_updated":"2025-03-07T02:09:32Z","snapshot_observed_at":"2026-08-18T00:49:59.983602Z","submitted_at":"2025-02-25T03:38:52Z","title":"Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17813","snapshot_observed_at":"2026-08-05T17:47:16.074134Z","title":"EEGM2: An efficient mamba-2-based self- supervised framework for long-sequence EEG modeling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.15716","last_updated":"2025-08-22T08:07:44Z","snapshot_observed_at":"2026-08-17T06:08:37.665390Z","submitted_at":"2025-08-21T16:56:28Z","title":"Foundation Models for Cross-Domain EEG Analysis Application: A Survey","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T17:47:16.074134Z"},"links":{"cited_paper":"/paper/2502.17813","citing_paper":"/paper/2508.15716"},"observation_digest":"sha256:1359520cbe7afe97763285ce8fcea1c23579e290453e7d41e66cd0c7101b8b90","observation_id":"b05760bd-e5be-4d49-9de2-784767980867","resolution":{"observed_at":"2026-08-05T17:47:16.074134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17813","last_updated":"2025-03-07T02:09:32Z","snapshot_observed_at":"2026-08-18T00:49:59.983602Z","submitted_at":"2025-02-25T03:38:52Z","title":"Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17813","snapshot_observed_at":"2026-08-05T17:44:23.921963Z","title":"EEGM2: An efficient mamba-2-based self- supervised framework for long-sequence EEG modeling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.15720","last_updated":"2025-08-21T16:57:33Z","snapshot_observed_at":"2026-08-16T06:21:26.242663Z","submitted_at":"2025-08-21T16:57:33Z","title":"WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T17:44:23.921963Z"},"links":{"cited_paper":"/paper/2502.17813","citing_paper":"/paper/2508.15720"},"observation_digest":"sha256:b904d4290e91593877628bc7c4d60c5ac3de4f6b834ca0b7a14cced4b96b7b0a","observation_id":"291830bc-8dc4-4516-bd18-573d455946fe","resolution":{"observed_at":"2026-08-05T17:44:23.921963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17813","last_updated":"2025-03-07T02:09:32Z","snapshot_observed_at":"2026-08-18T00:49:59.983602Z","submitted_at":"2025-02-25T03:38:52Z","title":"Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2502.17813","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.17813","snapshot_observed_at":"2026-07-04T21:10:09.712524Z","title":"2025 , archiveprefix =","venue":null,"work_id":"ac585059-8018-457d-b23d-185f88094922","year":2025},"citing_paper":{"arxiv_id":"2606.25978","last_updated":"2026-06-24T15:50:39Z","snapshot_observed_at":"2026-08-12T13:09:55.410303Z","submitted_at":"2026-06-24T15:50:39Z","title":"Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-06-25T19:00:05.408465Z"},"links":{"cited_paper":"/paper/2502.17813","citing_paper":"/paper/2606.25978"},"observation_digest":"sha256:071643f9a638d81a34f5ee0971878ae6c84c3f482436e85cebe634c030e2a278","observation_id":"dd0f3d30-6035-410f-ae8c-62e4eec91bc1","resolution":{"observed_at":"2026-07-04T21:10:09.714302Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.17813/citation-record","integrity":"/paper/2502.17813/integrity","json":"/paper/2502.17813/citation-record.json","paper":"/paper/2502.17813"},"outbound":[],"paper":{"arxiv_id":"2502.17813","last_updated":"2025-03-07T02:09:32Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-18T00:49:59.983602Z","submitted_at":"2025-02-25T03:38:52Z","title":"Safe Multi-Agent Navigation guided by Goal-Conditioned Safe 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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.17813."}