{"as_of":"2026-08-20T18:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5a035d2f465529b4a8cf789de94dd6a4397f761d5083a3bcc3d7788540b760f","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:01:37.329340Z","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":"2026-08-11T19:01:37.506246Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.03409","last_updated":"2024-03-06T02:36:15Z","snapshot_observed_at":"2026-08-16T14:12:27.323223Z","submitted_at":"2024-03-06T02:36:15Z","title":"Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN","version":1},"cited_work":{"arxiv_id":"2403.03409","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.03409","snapshot_observed_at":"2026-08-11T19:01:37.506246Z","title":"Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN","venue":"cs.NE","work_id":"ed13fa00-6b55-41bd-843b-51af62e91dd5","year":2024},"citing_paper":{"arxiv_id":"2412.07243","last_updated":"2024-12-10T07:07:06Z","snapshot_observed_at":"2026-08-19T18:58:15.604329Z","submitted_at":"2024-12-10T07:07:06Z","title":"A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T19:01:37.329340Z"},"links":{"cited_paper":"/paper/2403.03409","citing_paper":"/paper/2412.07243"},"observation_digest":"sha256:6647a759b7e73cf1de243b0e16153bcee5b98d177877f98d80c04e7009325002","observation_id":"3073045d-87e6-4f48-a5b5-9d792a189c86","resolution":{"observed_at":"2026-08-11T19:01:37.509847Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.03409/citation-record","integrity":"/paper/2403.03409/integrity","json":"/paper/2403.03409/citation-record.json","paper":"/paper/2403.03409"},"outbound":[],"paper":{"arxiv_id":"2403.03409","last_updated":"2024-03-06T02:36:15Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-16T14:12:27.323223Z","submitted_at":"2024-03-06T02:36:15Z","title":"Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2403.03409."}