{"as_of":"2026-08-10T13:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ac2e6004b51709a023bbe0014cae6db363f7786a0e06dacd95229d8c5899562","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:57:25.405606Z","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":"2026-07-02T02:56:29.848043Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.04104","last_updated":"2021-04-26T07:18:11Z","snapshot_observed_at":"2026-07-06T10:02:41.979068Z","submitted_at":"2020-10-08T16:39:20Z","title":"Learning the Pareto Front with Hypernetworks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04104","snapshot_observed_at":"2026-08-07T13:57:25.405606Z","title":"Learn- ing the pareto front with hypernetworks","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.20648","last_updated":"2025-05-27T02:53:14Z","snapshot_observed_at":"2026-08-10T08:13:20.467849Z","submitted_at":"2025-05-27T02:53:14Z","title":"Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T13:57:25.405606Z"},"links":{"cited_paper":"/paper/2010.04104","citing_paper":"/paper/2505.20648"},"observation_digest":"sha256:9304eabb19883997cdf466ebbdaf03b134b36033499b254498f063d8f5957c20","observation_id":"e4a81668-fa82-4fed-beae-7edf07694e2f","resolution":{"observed_at":"2026-08-07T13:57:25.405606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04104","last_updated":"2021-04-26T07:18:11Z","snapshot_observed_at":"2026-07-06T10:02:41.979068Z","submitted_at":"2020-10-08T16:39:20Z","title":"Learning the Pareto Front with Hypernetworks","version":2},"cited_work":{"arxiv_id":"2010.04104","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04104","snapshot_observed_at":"2026-07-02T02:56:29.848043Z","title":"Learn- ing the pareto front with hypernetworks.arXiv preprint arXiv:2010.04104","venue":null,"work_id":"8fa28b8e-455a-46ed-aa7c-0108f49b9baf","year":2010},"citing_paper":{"arxiv_id":"2604.11278","last_updated":"2026-04-13T10:38:21Z","snapshot_observed_at":"2026-07-06T22:59:40.900521Z","submitted_at":"2026-04-13T10:38:21Z","title":"Representation-Aligned Multi-Scale Personalization for Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T15:45:17.555896Z"},"links":{"cited_paper":"/paper/2010.04104","citing_paper":"/paper/2604.11278"},"observation_digest":"sha256:0186c00e5254387bffb038f9e1fa6c89bfb64a0645196ff8454a957ed7e2a108","observation_id":"bc8bbdc3-551e-4e59-b36a-babe76dd67d0","resolution":{"observed_at":"2026-05-11T09:56:01.595830Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04104","last_updated":"2021-04-26T07:18:11Z","snapshot_observed_at":"2026-07-06T10:02:41.979068Z","submitted_at":"2020-10-08T16:39:20Z","title":"Learning the Pareto Front with Hypernetworks","version":2},"cited_work":{"arxiv_id":"2010.04104","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04104","snapshot_observed_at":"2026-07-02T02:56:29.848043Z","title":"Learn- ing the pareto front with hypernetworks.arXiv preprint arXiv:2010.04104","venue":null,"work_id":"8fa28b8e-455a-46ed-aa7c-0108f49b9baf","year":2010},"citing_paper":{"arxiv_id":"2606.03102","last_updated":"2026-06-02T03:42:04Z","snapshot_observed_at":"2026-08-03T04:21:57.609394Z","submitted_at":"2026-06-02T03:42:04Z","title":"Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling","version":1},"reference_index":135,"source":"arxiv_source","source_observed_at":"2026-06-28T10:25:10.559953Z"},"links":{"cited_paper":"/paper/2010.04104","citing_paper":"/paper/2606.03102"},"observation_digest":"sha256:e18dd958d052c4776e767cfae6c8304bc42f56290edbe28927529160e5f0769a","observation_id":"317f4aca-3cf3-46f8-b368-d57f96910ec0","resolution":{"observed_at":"2026-07-02T02:56:29.849677Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04104","last_updated":"2021-04-26T07:18:11Z","snapshot_observed_at":"2026-07-06T10:02:41.979068Z","submitted_at":"2020-10-08T16:39:20Z","title":"Learning the Pareto Front with Hypernetworks","version":2},"cited_work":{"arxiv_id":"2010.04104","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04104","snapshot_observed_at":"2026-07-02T02:56:29.848043Z","title":"Learn- ing the pareto front with hypernetworks.arXiv preprint arXiv:2010.04104","venue":null,"work_id":"8fa28b8e-455a-46ed-aa7c-0108f49b9baf","year":2010},"citing_paper":{"arxiv_id":"2606.29521","last_updated":"2026-06-28T17:31:47Z","snapshot_observed_at":"2026-07-07T00:03:27.045821Z","submitted_at":"2026-06-28T17:31:47Z","title":"Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-30T07:30:24.505373Z"},"links":{"cited_paper":"/paper/2010.04104","citing_paper":"/paper/2606.29521"},"observation_digest":"sha256:692e717a682e01e3981c2353e77308bc368d6a943f1af1363f671b50a70a3667","observation_id":"28d6504d-1ed7-4acd-89c9-ede6342e156e","resolution":{"observed_at":"2026-06-30T07:34:21.519258Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2010.04104/citation-record","integrity":"/paper/2010.04104/integrity","json":"/paper/2010.04104/citation-record.json","paper":"/paper/2010.04104"},"outbound":[],"paper":{"arxiv_id":"2010.04104","last_updated":"2021-04-26T07:18:11Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T10:02:41.979068Z","submitted_at":"2020-10-08T16:39:20Z","title":"Learning the Pareto Front with Hypernetworks"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2010.04104."}