{"as_of":"2026-08-21T16:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54c3234e6589a7ff2a72fab3a699a663a28a503ea89c1d545d2bce5ab5ac167f","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":"2026-08-21T06:32:19.484+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":"2026-08-16T10:42:00.752975Z","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":"2026-06-28T17:42:25.771029Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02355","last_updated":"2024-02-07T02:38:52Z","snapshot_observed_at":"2026-08-18T23:51:30.010210Z","submitted_at":"2024-02-04T05:41:27Z","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02355","snapshot_observed_at":"2026-08-16T10:42:00.752975Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.17578","last_updated":"2025-04-24T14:09:22Z","snapshot_observed_at":"2026-08-18T23:47:52.187354Z","submitted_at":"2025-04-24T14:09:22Z","title":"Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T10:42:00.752975Z"},"links":{"cited_paper":"/paper/2402.02355","citing_paper":"/paper/2504.17578"},"observation_digest":"sha256:e2bb8970c6d140bc694a3421c4c3aa48369afa8027f54dcb469a0694495b4fd4","observation_id":"0f7a1e84-ed4c-44a0-b3b2-d18542883cb6","resolution":{"observed_at":"2026-08-16T10:42:00.752975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02355","last_updated":"2024-02-07T02:38:52Z","snapshot_observed_at":"2026-08-18T23:51:30.010210Z","submitted_at":"2024-02-04T05:41:27Z","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02355","snapshot_observed_at":"2026-08-15T22:59:52.776661Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05869","last_updated":"2025-05-09T08:09:21Z","snapshot_observed_at":"2026-08-18T16:09:30.869439Z","submitted_at":"2025-05-09T08:09:21Z","title":"Generative Discovery of Partial Differential Equations by Learning from Math Handbooks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T22:59:52.776661Z"},"links":{"cited_paper":"/paper/2402.02355","citing_paper":"/paper/2505.05869"},"observation_digest":"sha256:6febfacb9a7b2a4f2bca23fefe513a9c149f6e911a5388dffdc024dd95cdc9c8","observation_id":"067d049c-1cc7-4067-80b5-d11f2433426a","resolution":{"observed_at":"2026-08-15T22:59:52.776661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02355","last_updated":"2024-02-07T02:38:52Z","snapshot_observed_at":"2026-08-18T23:51:30.010210Z","submitted_at":"2024-02-04T05:41:27Z","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02355","snapshot_observed_at":"2026-08-04T20:55:41.187090Z","title":"Symbol: Generating flexible black-box optimizers through symbolic equation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08269","last_updated":"2026-08-19T09:09:41Z","snapshot_observed_at":"2026-08-21T16:12:55.556899Z","submitted_at":"2025-09-10T04:05:54Z","title":"A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving","version":6},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T20:55:41.187090Z"},"links":{"cited_paper":"/paper/2402.02355","citing_paper":"/paper/2509.08269"},"observation_digest":"sha256:37b24d730c117505afb6c3fd04d7464c58ad2f83675eebacfdfd51cdd48e9699","observation_id":"78810f1b-a869-4f97-8c6b-db9c12f1f393","resolution":{"observed_at":"2026-08-04T20:55:41.187090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02355","last_updated":"2024-02-07T02:38:52Z","snapshot_observed_at":"2026-08-18T23:51:30.010210Z","submitted_at":"2024-02-04T05:41:27Z","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","version":2},"cited_work":{"arxiv_id":"2402.02355","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02355","snapshot_observed_at":"2026-06-28T17:42:25.771029Z","title":"Symbol: Generating flexible black-box optimizers through symbolic equation learning.arXiv preprint arXiv:2402.02355, 2024","venue":null,"work_id":"fa873266-0fc3-4f0a-9730-4ba743b8bb8b","year":2024},"citing_paper":{"arxiv_id":"2606.00862","last_updated":"2026-05-30T19:26:32Z","snapshot_observed_at":"2026-08-08T15:58:34.048053Z","submitted_at":"2026-05-30T19:26:32Z","title":"Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T17:41:40.567807Z"},"links":{"cited_paper":"/paper/2402.02355","citing_paper":"/paper/2606.00862"},"observation_digest":"sha256:c48353ec997f19cb946aa3df1ffaa494f3d198fcb6a9e5d269ddf784f2f29315","observation_id":"5d487b9a-c9de-48d2-8967-c803b5acc614","resolution":{"observed_at":"2026-06-28T17:42:25.772577Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02355/citation-record","integrity":"/paper/2402.02355/integrity","json":"/paper/2402.02355/citation-record.json","paper":"/paper/2402.02355"},"outbound":[],"paper":{"arxiv_id":"2402.02355","last_updated":"2024-02-07T02:38:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T23:51:30.010210Z","submitted_at":"2024-02-04T05:41:27Z","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation 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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.02355."}