{"as_of":"2026-08-16T12:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:68294aebda80995c601e8d09e0296d7ac89957cecb239fd09326b7119e569cab","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T20:06:26.456549Z","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":"2026-08-05T15:34:46.241921Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1711.06788","last_updated":"2018-06-20T02:19:13Z","snapshot_observed_at":"2026-08-14T20:12:24.331778Z","submitted_at":"2017-11-18T01:42:04Z","title":"MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06788","snapshot_observed_at":"2026-08-15T20:06:26.456549Z","title":"Minimalrnn: Toward more interpretable and trainable recur- rent neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.13440","last_updated":"2025-06-16T12:54:27Z","snapshot_observed_at":"2026-08-16T11:29:32.357017Z","submitted_at":"2025-06-16T12:54:27Z","title":"Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:06:26.456549Z"},"links":{"cited_paper":"/paper/1711.06788","citing_paper":"/paper/2506.13440"},"observation_digest":"sha256:ec6fe76990c56048e83ce8be2de4a35179d8f91190c1ed3aae7404f824dc3873","observation_id":"1b3f639c-d4b3-4008-b382-a7494b24e5ad","resolution":{"observed_at":"2026-08-15T20:06:26.456549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06788","last_updated":"2018-06-20T02:19:13Z","snapshot_observed_at":"2026-08-14T20:12:24.331778Z","submitted_at":"2017-11-18T01:42:04Z","title":"MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06788","snapshot_observed_at":"2026-08-06T20:05:13.293881Z","title":"Minimalrnn: Toward more interpretable and trainable recurrent neural networks, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.03899","last_updated":"2025-07-05T04:35:04Z","snapshot_observed_at":"2026-08-14T10:33:39.403827Z","submitted_at":"2025-07-05T04:35:04Z","title":"Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T20:05:13.293881Z"},"links":{"cited_paper":"/paper/1711.06788","citing_paper":"/paper/2507.03899"},"observation_digest":"sha256:af2730dac5f86b7f8dfd6191d92eb2c8c34f4b01b57404213e39e69338d68ebd","observation_id":"70eedac8-924b-4706-b512-e35dd48f8a12","resolution":{"observed_at":"2026-08-06T20:05:13.293881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06788","last_updated":"2018-06-20T02:19:13Z","snapshot_observed_at":"2026-08-14T20:12:24.331778Z","submitted_at":"2017-11-18T01:42:04Z","title":"MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":"1711.06788","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06788","snapshot_observed_at":"2026-08-05T15:34:46.241921Z","title":"MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks","venue":"stat.ML","work_id":"33b8fd49-786f-4eb7-9a03-068a00f94733","year":2017},"citing_paper":{"arxiv_id":"2508.19806","last_updated":"2025-08-27T11:48:03Z","snapshot_observed_at":"2026-08-12T14:01:49.621166Z","submitted_at":"2025-08-27T11:48:03Z","title":"Context-aware Sparse Spatiotemporal Learning for Event-based Vision","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T15:34:46.126365Z"},"links":{"cited_paper":"/paper/1711.06788","citing_paper":"/paper/2508.19806"},"observation_digest":"sha256:9c146659fe33b9126d34f792f44696b3f94e29c454e09093fb03cafe7d10d993","observation_id":"42642668-1039-4f31-b13c-1aa13f3ba80c","resolution":{"observed_at":"2026-08-05T15:34:46.258126Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1711.06788/citation-record","integrity":"/paper/1711.06788/integrity","json":"/paper/1711.06788/citation-record.json","paper":"/paper/1711.06788"},"outbound":[],"paper":{"arxiv_id":"1711.06788","last_updated":"2018-06-20T02:19:13Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T20:12:24.331778Z","submitted_at":"2017-11-18T01:42:04Z","title":"MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1711.06788."}