{"as_of":"2026-08-08T08:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b664991c59446fee05b3fa761f0df6469e5e154a5510c0d7eec0a9515703a441","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T10:36:35.815064Z","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":"2026-07-03T13:48:21.586183Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.06178","last_updated":"2023-03-09T23:18:57Z","snapshot_observed_at":"2026-07-06T13:20:14.010201Z","submitted_at":"2022-06-13T14:07:56Z","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.06178","snapshot_observed_at":"2026-08-07T10:36:35.815064Z","title":"Subramoney, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.04817","last_updated":"2025-06-12T09:39:54Z","snapshot_observed_at":"2026-08-07T10:30:17.621989Z","submitted_at":"2025-06-05T09:38:42Z","title":"Spike-TBR: a Noise Resilient Neuromorphic Event Representation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:36:35.815064Z"},"links":{"cited_paper":"/paper/2206.06178","citing_paper":"/paper/2506.04817"},"observation_digest":"sha256:6a23e74a063ce9a2fa7e0c41c28bb5efe346176fe67449fd35ddd2cde3aa42d2","observation_id":"09530f1b-91b3-4ca0-8c3f-3d97c1d4d5b5","resolution":{"observed_at":"2026-08-07T10:36:35.815064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.06178","last_updated":"2023-03-09T23:18:57Z","snapshot_observed_at":"2026-07-06T13:20:14.010201Z","submitted_at":"2022-06-13T14:07:56Z","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.06178","snapshot_observed_at":"2026-08-06T18:38:18.427735Z","title":"EGRU: Event-based GRU for activity-sparse inference and learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.07734","last_updated":"2025-07-10T13:13:53Z","snapshot_observed_at":"2026-08-06T18:31:09.671750Z","submitted_at":"2025-07-10T13:13:53Z","title":"EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T18:38:18.427735Z"},"links":{"cited_paper":"/paper/2206.06178","citing_paper":"/paper/2507.07734"},"observation_digest":"sha256:1c02f11dbc268ad6413ba337d785c9533f1aaf847702901b5f2f18cf85788b3e","observation_id":"2383e970-80c5-4dde-b56d-b481b5d7b017","resolution":{"observed_at":"2026-08-06T18:38:18.427735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.06178","last_updated":"2023-03-09T23:18:57Z","snapshot_observed_at":"2026-07-06T13:20:14.010201Z","submitted_at":"2022-06-13T14:07:56Z","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time","version":3},"cited_work":{"arxiv_id":"2206.06178","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.06178","snapshot_observed_at":"2026-07-03T13:48:21.586183Z","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time.arXiv preprint arXiv:2206.06178, 2022","venue":null,"work_id":"b5b686f3-071c-4536-9de1-cfb0ee9b2a8e","year":2022},"citing_paper":{"arxiv_id":"2606.12895","last_updated":"2026-06-11T04:54:07Z","snapshot_observed_at":"2026-08-05T13:05:21.202083Z","submitted_at":"2026-06-11T04:54:07Z","title":"LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-06-27T07:26:58.608421Z"},"links":{"cited_paper":"/paper/2206.06178","citing_paper":"/paper/2606.12895"},"observation_digest":"sha256:ce94372ce7c5ab8858dd845ea1493ca7ffd787fa95ddc9dd2b02960675ef9c03","observation_id":"4b6fdb0e-78f1-4cc9-8b68-3f150ec400cf","resolution":{"observed_at":"2026-07-03T13:48:21.587691Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2206.06178/citation-record","integrity":"/paper/2206.06178/integrity","json":"/paper/2206.06178/citation-record.json","paper":"/paper/2206.06178"},"outbound":[],"paper":{"arxiv_id":"2206.06178","last_updated":"2023-03-09T23:18:57Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:20:14.010201Z","submitted_at":"2022-06-13T14:07:56Z","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time"},"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 3 inbound Pith citation observations for arXiv:2206.06178."}